Real-time Simulation for Sustainable Production: Enhancing User Experience and Creating Business Value 2020054552, 2020054553, 9780367515164, 9780367515188, 9781003054214

Table of contents :
Cover
Half Title
Series Information
Title Page
Copyright Page
Table of Contents
Illustrations
Contributors
1 Creating Value with Sustainable Production Based on Real-Time Simulation
1.1 Introduction
1.2 Reflections on Sustainable Production Based on real-Time Simulation
1.3 Origins of the Book
1.4 Future Research Directions
1.5 Conclusion
References
Part I Industrial Needs of Sustainable Production
2 Identifying Industrial Needs for Real-Time Simulation and Digital Twins
2.1 Introduction
2.2 Real-Time Simulation and Digital Twins Over the Product Lifecycle
2.2.1 Real-Time Simulation and Digital Twins
2.2.2 Product-Service System and Product Lifecycle Management
2.3 Methodology
2.4 Results – Identified Industrial Needs For Real-Time Simulation and Digital Twins
2.5 Conclusions
Acknowledgments
References
3 Company Capabilities and Implementing Real-Time Activities
3.1 Introduction
3.2 Theoretical Framework and Research Model
3.2.1 Theoretical Framework
3.2.1.1 Digital Business Strategy
3.2.1.2 Digital Capabilities
3.2.1.3 Digital Business Strategy and Real-Time Activities
3.2.1.4 Digital Capabilities and Real-Time Activities
3.2.2 Research Model
3.3 Empirical Examination of Real-Time Simulation
3.3.1 Data Collection and Sample
3.3.2 Descriptive Results
3.3.3 Statistical Analysis Results
3.4 Conclusions
3.4.1 Theoretical Implications
3.4.2 Managerial Implications
3.4.3 Limitations and Further Research
References
4 Real-Time Simulation Strategies: Implications For Operational Excellence and Sustainability Performance
4.1 Introduction
4.2 Real-Time Simulation For Sustainability
4.2.1 Characteristics of Real-Time Simulation Models
4.2.2 The Concept of Sustainability Performance
4.3 Empirical Examination of Real-Time Simulation Strategies
4.3.1 Data Collection
4.3.2 Cluster Analysis Results
4.3.3 Characteristics of the Real-Time Simulation Strategies
4.3.3.1 The “Data Wisdom” Strategy
4.3.3.2 The “in the Game” Strategy
4.3.3.3 The “Bystander” Strategy
4.4 Conclusions: Sustainable Strategies For Real-Time Simulation
4.4.1 Theoretical Implications
4.4.2 Managerial Implications
4.4.3 Limitations and Further Research Directions
References
5 Selling Digital Twins in Business-To-Business Markets
5.1 Introduction
5.2 Theoretical Background
5.2.1 Digital Twins in Manufacturing Industry
5.2.2 Selling Data-Based Solutions in Data-Based-Business-To-Business Markets
5.3 Research Methodology
5.4 Results
5.4.1 Model Illustrating the Level of Customer and Digital-Twin understanding Needed From the Sales Perspective
5.4.2 Challenges in Selling Digital Twins
5.5 Conclusions
Acknowledgments
References
Part II Game-Like Virtual Environments
6 Accelerating Design Processes Using Data-Driven Models
6.1 Introduction
6.2 Concept of a Data-Driven Model
6.2.1 System Modeling
6.2.2 Neural-Networks-Based Data Models and Other Methods
6.2.3 Data-Driven Models in the Multibody Framework
6.3 Applications of Data-Driven Models
6.3.1 Research and Product Development
6.3.2 Enhanced Operation
6.3.3 Maintenance and Service
6.3.4 New Business Opportunities
6.3.5 Supporting Sales and Purchase
6.4 Conclusions
Notes
References
7 Gamification and the Marketing of Agricultural Machinery
7.1 Introduction
7.2 Modeling An Agricultural Machine
7.2.1 Design Process For Parameterization
7.2.2 Environment Modeling
7.3 Gamification
7.3.1 Elements of a Game
7.3.2 Methods of Data Extraction
7.4 Case Example of a Farm Tractor
7.4.1 Gamification of the Farm Tractor Model
7.4.2 Product Development Opportunity
7.4.3 Marketing Opportunity
7.5 Conclusion
Acknowledgment(s)
References
8 Added Value From Virtual Sensors
8.1 Introduction
8.2 Virtual Sensors: Context and Background
8.3 Virtual Sensors As a Part of the Product Offering
8.3.1 Technical Methods Enabling Virtual Sensing
8.3.1.1 Analytical Methods
8.3.1.2 Numerical Methods
8.3.2 Opportunities/benefits and Challenges of Virtual Measurements
8.3.2.1 Offline Virtual Measurement – Slower Than Real Time
8.3.2.2 Online Virtual Measurement – Faster Than Real Time
8.3.3 Business Opportunities Introduced By Virtual Sensors
8.4 Conclusions
References
9 The Technical-Business Aspects of Two Technical-Business-Mid-Sized Manufacturing Companies Implementing a Joint Simulation Model
9.1 Introduction
9.2 Related Research
9.3 Methodology: Assembling the Joint Simulation Models
9.3.1 Developing Customer-Oriented B2B Products
9.3.2 Preparation of B2B Parameterized Real-Time Joint Simulation Model
9.3.3 Combining Parameterized Models and the Optimized Model
9.4 Joint Simulation of Industrial Mobile Machines
9.4.1 Technical-Business Challenges of Joint Simulation/Joint simulation Challenges
9.4.2 Real-Time Joint Simulation Solutions
9.4.2.1 Selection of Optimal Parameters
9.4.2.2 Optimal Range of Parameters
9.4.2.3 Joint-Model Stability
9.4.2.4 Feasibilities of Joint Simulation Combinations
9.4.2.5 User Selection of Parameterized Model/User Designing and Testing of Simulation Model
9.4.3 Collaboration Benefits and Issues On the Alliance
9.5 Conclusions
References
Part III Capturing Customer Value and User Experience
10 Implementing Digital Twins to Enhance Digitally Extended Product-Service Systems
10.1 Introduction
10.2 Related Research
10.3 Research Methodology
10.4 Results
10.5 Discussion and Conclusions
Acknowledgments
References
11 The Expected Benefits of Utilizing Simulation in Manufacturing Companies: Insights From a Delphi Study
11.1 Introduction
11.2 Simulation Modeling Motives in Manufacturing
11.3 Research Method and Data Gathering Process
11.4 Simulation Modeling in Manufacturing Companies: Insights From the Expert Panel
11.4.1 Benefits of Using Simulation in the Long Term For the Customer and Other Stakeholders
11.4.2 The Business-Activity Related Effects of Simulation
11.5 Conclusions
References
12 Integrating the User Experience Throughout the Product Lifecycle With Real-Time Real-Time-Simulation-Based Digital Twins
12.1 Introduction
12.2 Related Research
12.2.1 Multibody Definition of a Digital Twin
12.2.2 Product Lifecycle
12.2.3 The User Experience
12.2.4 Co-Creating Product Value With the UX and Co-Creating-Multibody-Based digital Twins
12.3 Enabling User Experiences in the Product Lifecycle With An Immersive Multibody-Based Multibody-Based-Digital-Twin Approach
12.3.1 Developing a User-Centered Virtual Space of a Physical Model
12.3.2 User Selection of Component Design Data
12.3.3 Immersive Methods For Generating User Input
12.3.4 Simulator Or Motion Feedback Platform
12.3.5 VR, AR, Mixed Reality Glasses, Leap Controllers and Haptics
12.3.6 Manufacturing of the Physical Product
12.3.7 Real-Time Communication Between the Physical and Virtual Spaces of the Digital Twin
12.3.8 Product Life Management Data
12.3.9 Enhancement of Measured Data
12.4 Industrial Case Study: the UX in Different Phases of the Product Lifecycle With a Multibody Digital Twin
12.4.1 New Product Development Approach: User Co-Creation of a New Forklift Mast System in the Virtual Space
12.4.2 Commercialization: User Testing of the Parameterized Model in Different Environments
12.4.3 Manufacturing: Utilizing the User-Based Multibody Model in Production
12.4.4 User-Related Product Services in the Operation Phase: Updating the Virtual Space of the Digital Twin With User-Related-Real-World Information
12.4.5 End of Product Life: Retiring the Product Based On User Data generated in the Digital Twin
12.5 Conclusion
Acknowledgments
References
Part IV Value for Business
13 The Digital Twin Combined With Real-Time Performance Measurement in Lean Manufacturing
13.1 Introduction
13.2 Context and Background
13.2.1 Lean Approach and Performance Measurement
13.2.2 Digital Twins and Performance Measurement
13.3 Methodology
13.4 Advantages of the Digital Twin and Real-Time Performance Measurement
13.4.1 Advantages to Stakeholders
13.4.1.1 Management
13.4.1.2 Worker
13.4.1.3 Customer
13.4.2 Advantages By Process Perspective
13.5 Conclusion
References
14 Using Real-Time Simulation in Company Value Chains and Business Models For Value Creation
14.1 Introduction
14.2 The Effect of Digitalization On the Market
14.3 Real-Time Simulation Models and How They Create Value For Customers
14.4 Business Model Canvas As a Tool to Analyze the Value Chain
14.5 Applying Real-Time Simulation to Different Alue Chain Activities
14.5.1 Applying Real-Time Simulators in R&d and Product Development
14.5.2 Applying Real-Time Simulators in Training
14.5.3 Applying Real-Time Simulators to Predict Faults
14.5.4 Applying Real-Time Simulators in Services
14.5.5 Applying Real-Time Simulators in Sales and Marketing
14.5.6 The Effect of Real-Time Simulation On Business Models
14.6 Discussion and Conclusions
Notes
References
15 Sustainable Competitive Advantage Through the Implementation of a Digital Twin
15.1 Introduction
15.2 A Multidimensional Model For the Implementation of a Digital Twin
15.2.1 Theoretical Underpinnings
15.2.2 Defining a Digital Twin
15.2.3 Description of the Model
15.2.3.1 Firm Competencies
15.2.3.2 Strategic Direction
15.2.3.3 Sustainability Performance
15.2.3.4 External Environment
15.3 Requisite Competencies and Research Propositions
15.3.1 Linking Competencies to the Strategic Direction of a Digital Twin
15.3.2 Linking Strategic Direction of a Digital Twin to Sustainability performance
15.3.3 External Environment As Moderator
15.4 Conclusions
15.4.1 Theoretical Implications
15.4.2 Managerial Implications
15.4.3 Limitations and Further Research Directions
References
16 Managing Digital-Twin lifecycle – Recognition and handling of Business Risks
16.1 Introduction
16.2 Theoretical Background
16.2.1 Digital Twins
16.2.2 Digital Twin Lifecycle
16.2.3 Lifecycle Risks of Digital Twins
16.3 Research Methodology
16.4 Results
16.5 Concluding Remarks
Acknowledgments
References
Index

Citation preview

Real-​time Simulation for Sustainable Production

This book provides a comprehensive overview of potential opportunities and the business value position related to implementing physics-​based real-​time simulation to production. The objective of real-​time simulation is to provide value for all three dimensions of sustainability: economic, social, and environmental. By reviewing actual industrial cases and presenting relevant academic research, the book examines the topic from four interrelated viewpoints: the industrial need for sustainable production, the development of game-​like virtual environments, capturing customer value and enhancing the user experience, and finally, establishing business value. It offers a framework that will enable a rethink and shift in mindset to appreciate how real-​time simulation can change the way products are manufactured and services are produced. This book will appeal to researchers and scholars in areas as diverse as strategic management, manufacturing and operations management, marketing, industrial economics, and product lifecycle management. Juhani Ukko is a senior researcher at LUT University, School of Engineering Science, Department of Industrial Engineering and Management, Finland. His current research focuses on performance measurement, operations management, digital transformation, digital services, and corporate sustainability performance. Minna Saunila is a senior researcher at LUT University, School of Engineering Science, Department of Industrial Engineering and Management, Finland. Her research covers topics related to performance management, innovation, service operations, and sustainable value creation. Janne Heikkinen works as a post-​ doctoral researcher in Laboratory of Machine Dynamics at LUT. His research interests are in the fields of rotordynamics, structural vibrations, multibody simulation, and vibration measurements. R. Scott Semken is a senior researcher at the LUT University School of Energy Systems, Department of Mechanical Engineering, Finland, working in the Laboratory of Computational Mechanics. Aki Mikkola is working as Professor in the Department of Mechanical Engineering at LUT University, Finland. His major research activities are related to flexible multibody dynamics, real-​time simulation, rotating structures, and biomechanics.

Routledge Advances in Production and Operations Management

This series sets out to present a rich and varied collection of cutting-​edge research on production and operations management (POM), addressing key topics and new areas of interest in order to define and enhance research in this important field. Bringing together academic study on all aspects of planning, organizing and supervising production, manufacturing or the provision of services, subject areas will include, but are not limited to: operations research, product and process design, manufacturing strategy, scheduling, quality management, logistics and supply chain management. Highly specialised and industry-​ specific studies are actively encouraged. Real-​time Simulation for Sustainable Production Enhancing User Experience and Creating Business Value Edited by Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken and Aki Mikkola For more information about this series, please visit: www.routledge.com/​ The-​Routledge-​Philosophers/​book-​series/​RAPOM

Real-​time Simulation for Sustainable Production Enhancing User Experience and Creating Business Value Edited by Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken and Aki Mikkola

First published 2021 by Routledge 2 Park Square, Milton Park, Abingdon, Oxon OX14 4RN and by Routledge 605 Third Avenue, New York, NY 10158 Routledge is an imprint of the Taylor & Francis Group, an informa business © 2021 selection and editorial matter, Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken and Aki Mikkola; individual chapters, the contributors The right of Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken and Aki Mikkola to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs and Patents Act 1988. All rights reserved. No part of this book may be reprinted or reproduced or utilised in any form or by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying and recording, or in any information storage or retrieval system, without permission in writing from the publishers. Trademark notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. British Library Cataloguing-​in-​Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-​in-​Publication Data Names: Ukko, Juhani, editor. Title: Real-time simulation for sustainable production : enhancing user experience and creating business value / edited by Juhani Ukko, Minna Saunila, Janne Heikkinen, R. Scott Semken and Aki Mikkola,. Description: Abingdon, Oxon ; New York, NY : Routledge, 2021. | Series: Routledge advances in production and operations management | Includes bibliographical references and index. Identifiers: LCCN 2020054552 (print) | LCCN 2020054553 (ebook) | ISBN 9780367515164 (hbk) | ISBN 9780367515188 (pbk) | ISBN 9781003054214 (ebk) Subjects: LCSH: Sustainable development–Simulation methods. | Sustainability–Simulation methods. Classification: LCC HC79.E5 R4154 2021 (print) | LCC HC79.E5 (ebook) | DDC 658.5/038–dc23 LC record available at https://lccn.loc.gov/2020054552 LC ebook record available at https://lccn.loc.gov/2020054553 ISBN: 978-0-367-51516-4 (hbk) ISBN: 978-0-367-51518-8 (pbk) ISBN: 978-1-00-305421-4 (ebk) Typeset in Bembo by Newgen Publishing UK

Contents

List of illustrations  List of contributors  1 Creating value with sustainable production based on real-​time simulation 

viii xi

1

M I N N A S AU N I LA, JUHANI UK KO, JANNE HE I K K IN EN, R . S C OT T S E M K E N AND AK I MI K KO LA

PART I

Industrial needs of sustainable production 

11

2 Identifying industrial needs for real-​time simulation and digital twins 

13

L E A H A N N O L A, I LK K A D O NO GHUE , K I RSI KO KKON EN, K ALLE E L F V E N GRE N AND JO RMA PAPI NNI E MI

3 Company capabilities and implementing real-​time activities 

28

M I N A N ASI RI , JUHANI UK KO, MI NNA SAUNI LA A N D T E RO R ANTALA

4 Real-​time simulation strategies: implications for operational excellence and sustainability performance 

42

M I N N A S AU N I LA, MI NA NASI RI AND JUHANI UKKO

5 Selling digital twins in business-​to-​business markets  T U I JA R AN TALA, K I RSI KO K KO NE N AND LE A HA N N OL A

51

vi Contents PART II

Game-​like virtual environments 

63

6 Accelerating design processes using data-​driven models 

65

E M I L K U RV I NE N, I I NE S SUNI NE N, GRZE GO R Z OR Z ECH OWSKI, JI N H . C H O I , JI N- ​G Y UN K I M AND AK I MI K KOL A

7 Gamification and the marketing of agricultural machinery  77 SU RAJ JA I SWAL, ANSSI TARK I AI NE N, TUHI N CH OU DH U RY, JU SSI S O PA NE N AND AK I MI K KO LA

8 Added value from virtual sensors 

90

JA N N E H E I KK I NE N, E MI L K URVI NE N AND JU SSI SOPA N EN

9 The technical-​business aspects of two mid-​sized manufacturing companies implementing a joint simulation model 

102

M AN O U C H E HR MO HAMMAD I , K ALLE E LFVEN GR EN, Q A SI M K H AD I M AND AK I MI K KO LA

PART III

Capturing customer value and user experience 

119

10 Implementing digital twins to enhance digitally extended product-​service systems 

121

I LK K A D O N O GHUE , LE A HANNO LA AND ANTT I SÄ Ä KSV U OR I

11 The expected benefits of utilizing simulation in manufacturing companies: insights from a Delphi study 

132

K A LLE E L F VE NGRE N, MANO UCHE HR MO HAM M A DI, V I L LE K ALLI O LA AND LE A HANNO LA

12 Integrating the user experience throughout the product lifecycle with real-​time simulation-​based digital twins 

147

Q A SI M K H AD I M, LE A HANNO LA, I LK K A D O NOGHU E, AK I M I K KO L A, E SA- ​P E K K A K AI K KO AND TE RO H U KKATA IVA L

PART IV

Value for business 

163

13 The digital twin combined with real-​time performance measurement in lean manufacturing 

165

M I RA H O LO PAI NE N, JUHANI UK KO, MI NNA SAUN IL A , TE RO R AN TALA AND HANNU RANTANE N

Contents  vii

14 Using real-​time simulation in company value chains and business models for value creation 

177

M AYA K RI STI NA CHE I K H- ​E L- ​C HABAB, O LLI K UIVA L A IN EN, U L F R . A N D E RSSO N, RO O PE E SKO LA AND AK I MIKKOL A

15 Sustainable competitive advantage through the implementation of a digital twin 

196

J U H AN I U K KO, TE RO RANTALA, MI NA NASI RI AN D M IN N A SAUN IL A

16 Managing digital-​twin lifecycle –​recognition and handling of business risks 

213

T E RO R AN TALA, MI NNA SAUNI LA, JUHANI UK KO, A KI M IKKOL A , J U H A KO RT E LAI NE N AND AK HTAR ZE B

Index 

224

Illustrations

Tables 1.1 2.1 3.1 3.2 4 .1 4.2 4.3 5.1 9.1 9 .2 10.1 1 0.2 11.1 11.2 11.3 11.4 11.5 11.6 1 6.1 16.2

Suggestions for future research  Information of the data collection process  Survey items  The role capabilities in considering digital as real-​time activities among manufacturing companies (N = 116)  Cluster analysis results  The means of the sustainable value dimensions in each cluster  Descriptions of the clusters based on size, maturity, and type of business  Interviewed companies, their main products and services, number of interviewees, and interview dates  Major parameterized components/​specifications for the two case models –​each has a certain number of values that users can select  The challenges found and recommended solution procedures  Digitalization framework for digitally extended PSS and SCPS  Case company averages based on the adapted framework  The Delphi panelists  The developed Delphi process  The typical goals the companies are hoping to gain from simulation modeling  Benefits emerged or expected because of the simulation models  Resisting forces against the simulation functions/​efforts in the companies  Suggestions for business-​related achievements of using simulation in the long term  Process of empirical data gathering  Business risks related to digital twin lifecycle 

8 18 34 37 46 47 48 55 110 116 126 128 135 136 137 138 139 141 219 221

List of Illustrations  ix

Figures 1 .1 Sustainable value creation with real-​time simulation and digital twins  6 2.1 Product lifecycle management framework (modified from Donoghue et al. 2018b)  16 2.2 Industrial needs for product lifecycle 1 –​Product in Portfolio  20 2.3 Industrial needs for product lifecycle 2 –​Delivered Instance  21 2.4 Industrial needs for product lifecycle 3 –​Instances in Operation  22 3.1 Research model: capabilities affecting the contribution of manufacturing companies considering digital as real-​time activities  33 3.2 Manufacturing emphasis on real-​time activities  36 3.3 Level of capabilities in manufacturing companies  36 5.1 Selling digital twins in B2B markets showing the levels of customer and digital-​twin understanding required as well as ecosystem complexity  56 5.2 Selling digital twins in B2B markets showing the levels of customer and digital-​twin understanding required as well as ecosystem complexity from the perspectives of the case companies both now and in the future  57 6.1 Data-​driven models and their potential uses and benefits and accessibility  72 7.1 A design algorithm to build a parameterized multibody model  80 7.2 Gamification algorithm employed in this study  83 7.3 Real-​time tractor simulation model in a deformable sand field environment  84 7.4 The gamified graphical user interface of the tractor model showing the restricted field of view for the driver  84 9.1 The traditional and proposed product development approaches in a B2B venture: (a) traditional and (b) proposed joint real-​time-​simulation-​based product development  105 9.2 Assembling two models, each with various component/​ specification options, to construct a joint simulation model –​ the check marks in the figure show which types were selected for this particular simulation  106 9.3 Parameterization procedure  106 9.4 A user interface constructed in Excel –​users can easily select their intended parameters/​specifications  107 9.5 (a) The harvester crane system showing the crane mounted on the truck trailer, (b) the tractor with its trailer  107 9.6 The location of the connection spot (Point A) between two models  111

x  List of Illustrations 9.7 Frog-​leg stabilizers to prevent the main-​booms, the cabin, and the trailer from falling down to the right and to the left  9.8 Schematic of the procedure on how two models can be assembled and joined together  9.9 The process of constructing the joint simulation model comprising several sub-​systems –​sub-​systems are specified using the parameterization technique  10.1 Intelligent PSS  12.1 Methodology to enable UX integration into the product life cycle using multibody virtual and physical spaces of a digital twin  12.2 UX-​driven product development of a 3W, 2.0-​ton, EVOLT 48 counterbalance forklift using multibody real-​time simulation  12.3 Accelerating the marketing process of the 3W, 2.0-​ton, EVOLT 48 forklift with the introduction of a multibody simulation model  13.1 The advantages of the digital twin and real-​time performance measurement to company’s stakeholder  13.2 The advantages of the digital twin and real-​time performance measurement to the performance of the process and the company  14.1 Virtual environment (Mevea, 2018a, 2018b, 2018c)  15.1 Model of a digital-​twin implementation 

111 113 114 129 152 156 158 170 173 183 200

Contributors

Ulf R. Andersson is a professor at Mälardalen University, Sweden, and Adjunct Professor at BI Norwegian Business School. His research focuses on subsidiary evolution, knowledge governance and transfer, and network theory. Maya Kristina Cheikh-​el-​Chabab is the marketing manager at Caddyboo Ltd, Finland. Maya participated in assisting some research projects related to Real-​time simulation and project management at LUT by collecting data from multinational enterprises and SMEs. Jin H. Choi is a professor of Kyung Hee University, Seoul, and Chair of FunctionBay Inc. and his research interests are Rigid & Flexible Multibody Dynamics, and multi-​ Physics Interactions, CAE S/​ W development, and Data Driven CAE using DNN. Tuhin Choudhury is a junior researcher at LUT University, Lappeenranta, Finland. Currently, he is pursuing doctoral studies in the department of Mechanical Engineering in LUT University. His research interests include designing, modeling and simulation of rotating machines and analysis of rotor behavior to understand the root cause of unwanted vibrations, specifically, due to unbalance. Ilkka Donoghue is a junior researcher and Ph.D. Candidate in the School of Engineering Science at LUT University, Finland. His research is related to intelligent product-​service systems and the role they play in developing sustainable business ecosystems. Kalle Elfvengren is an associate professor in the School of Engineering Science at LUT University, Finland. His research interests include process development in the health-​care sector, management of technology, and risk management. Roope Eskola is a research and development manager at Raute Corporation, Finland. He received the Lic.Tech in paper technology in 2015 from LUT University, Lappeenranta, Finland.

xii Contributors Lea Hannola is an associate professor in the School of Engineering Science at LUT University, Finland. Currently, she is doing research on innovation and technology management, especially on user and customer needs assessment, digital tools in industrial settings, and business model research in the context of product-​service systems. Janne Heikkinen works as a post-​doctoral researcher in Laboratory of Machine Dynamics at LUT. His research interests are in the fields of rotordynamics, structural vibrations, multibody simulation, and vibration measurements. Mira Holopainen is a project researcher in the School of engineering Science at LUT University, Finland. Her research is related to performance measurement and management as well as digital transformation of industrial companies. Tero Hukkataival is currently working as a manager of Testing and Prototypes in Rocla Oy –​a group company of Mitsubishi Logisnext Co. Ltd. Suraj Jaiswal is a junior researcher at LUT University of Technology, Lappeenranta, Finland, where he is currently pursuing the Ph.D. degree in mechanical engineering. His research interests include multibody system dynamics, real-​time simulation, state observers, extended Kalman filters, and vehicle dynamics. Esa-​Pekka Kaikko is currently working as CAE simulation Engineer in Rocla Oy –​a group company of Mitsubishi Logisnext Co. Ltd. Ville Kalliola works as a project planner at Neste Engineering Solutions, Finland, focusing on constructing and maintaining schedules for the upcoming major turnaround. Qasim Khadim is a junior researcher and doctoral candidate in the field of machine design. His current research interests include real-​time simulation of multibody systems, user experiences, and digital product processes. Jin-​Gyun Kim is an assistant professor at Kyung Hee University, Seoul. His research interests are computational dynamics and vibration, especially linear/​nonlinear model reduction, data-​driven approach, and virtual sensing. Kirsi Kokkonen is a post-​doctoral researcher in the School of Engineering Science at LUT University, Finland. Her research interests include, e.g., industrial service development and service business models, value networks and business ecosystems, and entrepreneurship. Juha Kortelainen is a principal scientist and principal investigator at VTT Technical Research Centre of Finland Ltd., Finland, on the AI-​aided systems engineering research team. His recent research work has focused on data, information and knowledge management, and knowledge engineering in engineering design processes.

Contributors  xiii Olli Kuivalainen is a professor of International Marketing and Entrepreneurship at LUT University School of Business and Management, Finland. His expertise covers broad areas of international business, marketing, and entrepreneurship and their interplay, also with advancement of technologies. Emil Kurvinen works as a post-​doctoral researcher at LUT University. His research interests are rotating machines, especially high-​ speed machines, digital twins, and freeze crystallization. Aki Mikkola is working as a professor in the Department of Mechanical Engineering at LUT University, Finland. His major research activities are related to flexible multibody dynamics, real-​time simulation, rotating structures, and biomechanics. Manouchehr Mohammadi is a junior researcher at LUT University, Finland where he is currently a junior researcher. His research interests include the design of real-​time simulation models and multibody system dynamics. Mina Nasiri is a post-​ doctoral researcher at LUT University, School of Engineering Science, Department of Industrial Engineering and Management, Finland. Her research interests lie in the area of digitalization, digital transformation, performance measurement and management, operations management, and sustainable strategies. Grzegorz Orzechowski is a post-​doctoral researcher at LUT University and Senior Developer at Mevea Ltd. His research interests are Flexible Multibody Systems, Artificial Intelligence applications in Mechanical Engineering, and Real-​time Simulations. Jorma Papinniemi is a senior lecturer and project manager in the School of Engineering Science at LUT University, Finland. Currently, Mr. Papinniemi is participating in research and development projects related to development of a digital product-​service platform for manufacturing SMEs. Tero Rantala is a post-​doctoral researcher at LUT University, School of Engineering Science, Finland. His current research focuses on performance management and measurement of university–​industry collaborations. Tuija Rantala works as a senior scientist at VTT. Her main research interests are related to new business creation, innovation development, and business-​ to-​business (B2B) sales. Hannu Rantanen is a professor of Industrial Management at the LUT University. His research concentrates on performance measurement and management in the private and public sector, and cost management in industrial enterprises. Minna Saunila is a senior researcher at LUT University, School of Engineering Science, Department of Industrial Engineering and Management, Finland.

xiv

Contributors

Her research covers topics related to performance management, innovation, service operations, and sustainable value creation. R. Scott Semken is a senior researcher at the LUT University School of Energy Systems Department of Mechanical Engineering, Finland, working in the Laboratory of Computational Mechanics. Jussi Sopanen is currently serving as a professor in Machine Dynamics at LUT University, Lappeenranta, Finland. His research interests are in rotor dynamics, multibody dynamics, and the mechanical design of electrical machines. Iines Suninen works as a project manager at LUT-University. Background is B.Sc. in Business Law. Antti Sääksvuori is a management consultant at Krios Business Consulting Oy, Finland, focusing on product lifecycle management, product management, product and portfolio strategies, product innovation, and service development and productization. Anssi Tarkiainen is an associate professor (tenure track) in Digital Marketing and Director of the Viipuri Lab in LUT School of Business and Management, Finland. Overarching theme in his research is human behavior and decisionmaking of individuals and groups, which he has studied in the contexts of consumer behavior sales management, and strategy. Juhani Ukko is a senior researcher at LUT University, School of Engineering Science, Department of Industrial Engineering and Management, Finland. His current research focuses on performance measurement, operations management, digital transformation, digital services, and corporate sustainability performance. Akhtar Zeb is a research scientist at VTT Technical Research Centre of Finland Ltd., Finland. His current research work relates to engineering knowledge management, semantic data modeling, data harmonization, data management of digital twins, and applications of AI and machine learning in engineering design.

1  Creating value with sustainable production based on real-​time simulation Minna Saunila, Juhani Ukko, Janne Heikkinen, R. Scott Semken and Aki Mikkola

1.1  Introduction There has been shift away from technology-​driven design and production toward sustainable value creation (Kaewunruen & Lian, 2019; Ukko et al., 2019). Traditionally, product and service development decisions have been made by experts tasked with directly addressing development issues.Their decisions have been based, for the most part, on relevant technical aspects. The focus, however, is moving away from purely technical aspects and moving toward enhancing the user experience and customer value. To address this trend, many manufacturing companies are reconsidering and re-​engineering their existing product processes so they can become more competitive. Advanced and persistent usage of key enabling technologies will be fundamental to success, and as the new enabling technologies are implemented to provide future products and services, user and customer needs must be better addressed and increasingly fulfilled (cf., Jones et al., 2020; Ukko et al., 2020). Accordingly, product processes must be reconsidered so the broader spectrum of stakeholders –​beginning with supply, encompassing all internal organizational functions, the user and ending with aspects of recycling or disposal –​are closely engaged (Kokkonen et al., 2020; Nasiri et al., 2020). The critical task is to develop the necessary techniques and toolsets needed in design and manufacturing to enable new sustainable business opportunities. Often, new products embedded with innovative digital solution functionalities such as Internet of Things (IoT) are verified and validated by building and testing prototypes. In general, the prototyping process includes detailed design, parts procurement, prototype assembly, verification and validation testing, results assessment, and redesign. Each step in this process consumes time, which delays the commercialization of new innovations, and money, which results in more expensive product development in which customer needs may not be completely fulfilled. On top of wasting time and money, in many cases the manufactured prototypes cannot be used, as such, in follow-​on prototyping, which contradicts the idea of sustainable production. New digital approaches for the design, prototyping, and testing of new machines make it possible to

2  Minna Saunila et al. account for human-​interaction and customer needs early on in new product development. Getting the earliest possible feedback from the end user or any other stakeholder promises to significantly reduce costs while increasing sustainability in product and production processes and providing more configurable product families for multiple market niches (cf., Tao et al., 2019; Zhong et al., 2015 Zhou et al., 2020). Digital design methods have been available and in use for decades. The computer-​aided design tools have been available since the 1960s. The development of digital design tools has progressed from clumsy 2D blueprints to realistic 3D models that can enhance, for example, studies of manufacturability and assembly in engineering development and not on the factory floor. Another level of advancement was achieved when static objectives were combined to form systems in which virtual parts could move relative to each other and accurately simulate the real machine behaviors of actual machine parts. It sounds simple, but on the theoretical level, this breakthrough required a comprehensive mathematical interpretation of the complete system. Armed with these models capable of accurately simulating real-​ life mechanisms, researchers have been able to come up with more advanced and sophisticated methods to describe the motions of the mechanisms. Technical solutions driven by real-​time simulation and digital twins based on real-​time simulation are rapidly developing, and their application, from a technical point of view, is constantly becoming easier and more cost effective. However, meaningfully applying digital twins requires business understanding (Kokkonen et al., 2020; Qi et al., 2018). Digital twins based on real-time simulation need to enable new business, or at least enhance existing business processes, and therefore deliver value that is greater than the costs of their implementation. Consideration should be given to how the real-time simulation model can deliver significant business benefits over the traditional digital product development process. In the traditional digital product development process, there is no real-​time utilization of the digital product model and product or system and the continuous linking of the collected information. Despite growing interest, there is lack of research on which characteristics of digital twins based on real-time simulation contribute to the different uses. In terms of new business opportunities, the whole concept needs further development. This chapter describes research into physics-​based real-​time simulation from a business perspective and provides guidance for further studies. It addresses the following two questions: • What is the meaning of real-​time simulation in contemporary business practice? • And, how does real-​time simulation contribute to sustainable production? The immediately following paragraphs present the potential of real-​ time simulation in sustainable production. Then, suggestions for future research are summarized.

Creating value with sustainable production  3

1.2  Reflections on sustainable production based on real-​time simulation As already mentioned, digital tools enable more sustainable business operations (Saunila et al., 2019; Ukko et al., 2019). However, to dive deeper into value creation from digital methods, stakeholders of a company including managers, business owners, personnel, investors, and others must understand the state-​ of-​art of available digital methods as well as the current company status with respect to the use of digital methods. Prerequisites for taking full advantage of digital tools include knowing and understanding existing capabilities and having the required human resources, calculation power, and software licenses. Digital models of the equipment that will be used to build value are also needed. In modern business operations, the digital models are already a part of the product management system. However, the digital model itself is only the first step toward creating tangible value out of the virtual counterpart of the physical systems. As part of the research being reported in this book, representatives of several Finnish manufacturing companies from different industrial sectors were interviewed. These companies are in the frontline of taking the advantage of simulation tools for research and development, and simulation is a big part of their product development footprint.The company representatives interviewed included technical experts, already well aware of the advantages of simulation, as well as other non-​technical stakeholders.These comprised business managers and decision-​makers. The diversity in the group of company experts made it possible to explore the bigger business perspective instead of just focusing on technical issues. The results of the interviews are reported in the chapters of this book. The main takeaways from the interviews were that the technical people do not have a clear idea of how simulation can be best applied to increase customer and company value. Moreover, the business managers and decision-​makers do not understand how much potential for value creation the digital tools really have. It is an interesting situation that suggests there is an enormous business opportunity looming in the background. The traditional way simulation is used as a digital counterpart of the physical system, especially in the earliest stages of product development. The benefits of exercising digital instead of real prototypes are easy to justify economically. However, from the technical perspective, they offer an even more significant advantage. A digital model can be adjusted, tested, and redesigned in a matter of hours, whereas the manufacture, testing, and redesign of a prototype take weeks and months. The ease with which a virtual prototype can be manipulated encourages engineers to be more creative and promotes the development of new innovations and methods. Innovations are naturally a key to success in the global marketplace, therefore modern simulation tools accelerate, both directly and indirectly, the overall sustainable growth of the companies that use them.

4  Minna Saunila et al. Simulation tools are also being used in sales and marketing, where they are also bringing a clear competitive advantage. The most advanced simulators feature a complete operator cockpit sitting on a motion platform, a realistic 3D simulation model of the machine (already customized in customer colors), a realistic model of the customer-​specific environment, and a high-​end graphical user interface. The simulator package allows the customer or end user to immersively and realistically operate the simulated machine and experience its performance. Even though it seems like game play, the results of scientific experiments and industrial feedback demonstrate that having such a simulator builds trust and enhances the branding of the equipment manufacturer. Customers can configure the simulation model enabling them to try out different equipment options, such as motor and gearbox or other functional accessories, before making their purchase decision. The abovementioned use-​cases are the most obvious, and simulators have been used for such purpose in recent years. However, new business initiatives are planning for broader use of digital tools to create even more value that coming from product sales. The additional value can be built, for example, by exercising simulation tools to make operations more efficient (cf., Jaiswal et al., 2019; Khadim et al., 2020; Pan et al., 2020). Different drive cycles or maneuvers can be simulated to define the execution of the operation for the fastest working cycle, safest driving path, lowest fuel consumption, etc. The difficulty of such an approach is to justify the money savings for the end user and to innovate suitable performance-​oriented business models. Another example of added value is the usage of digital counterparts of the physical systems for virtual measurements and condition monitoring. Virtual measurements are software algorithms that use measured data from the real machinery to complete its virtual model with real-​life input data.This approach enables the virtual model to imitate the maneuvers of the actual system, and instead of installing additional measuring sensors in the real physical machine, the virtual model can be exploited to estimate the performance parameters of the machinery. This is particularly interesting for measuring parameters or details that are hard to reach due to machine geometry, are located in a harsh environment, or are difficult to measure by other means. Further, this approach makes it possible to perform more comprehensive condition monitoring and fault diagnostics during the life cycle of the machine improving its overall performance. The additional value for the end user is the amount of received data from the system using a limited number of sometimes very costly sensors. From the value creation aspect, the more accurate analysis of the machine conditions and better predictability of typical failures enables performing predictive maintenance to extend service intervals and cut direct service costs. If the digital models are sufficiently accurate and the virtual model functions in real time in parallel with the actual system, the model can give the driver or operator additional information about the machine as it operates. The simulation model can estimate different physical performance indicators such as load

Creating value with sustainable production  5 conditions, forces, velocities, accelerations, and mechanical durability. The additional virtual data can be presented to the operator in the form of numerical values, alarms, or warning lights via a graphical interface. Again, the additional information is clearly valuable for the end user, yet it remains the responsibility of the original equipment manufacturer to design a business model that results in more customer value and subsequent added revenue.

1.3  Origins of the book The need to take simulator-​driven design and production to the next level by developing and evaluating a number of community-​based real-​time simulator-​ driven processes led to this writing and publication of this book. The intent is to narrow the research gap between the technical and business aspects of implementing the new digital toolset and clarify how this type of digital transformation of industry will improve company effectiveness, increase customer value, and maximize business potential resulting in sustainable value creation. In addition to scholarly interest, this type of approach is relevant for policy makers and company managers in the areas of research and development, the service businesses, commercialization, and customer service for all stakeholders. Therefore, despite the increased amount of research on simulator-​driven design and production, there seems to be little progress in understanding the complexities of the subject from a business perspective. The literature strongly suggests that this type of digital transformation of industry is beneficial, but exactly how value will be created seems to be the black box. Accordingly, this book tackles the questions: • How can real-​time simulation improve the effectiveness, customer value, and business potential of production processes? • And, how will real-​time simulation help industry to be more sustainable, more cost effective, more energy efficient, and more capable of responding to local and global societal challenges? These questions can be answered by encouraging a new way of looking at real-​ time simulation studies.The complementary and multi-​disciplinary perspectives of the book ensure both the technical and economic viability of the solutions provided to answer the questions. Each chapter provides its unique view of sustainable production based on real-​time simulation, but there are common themes. By reviewing actual industrial cases and presenting relevant academic research, the big picture is framed from four interrelated viewpoints: the industrial need for sustainable production, the development of game-​ like virtual environments, capturing customer value and enhancing the user experience, and establishing business value. Academic research into the interface of physics-​based real-​time simulation and business management is multidisciplinary. Therefore, this book provides implications to management research in areas as diverse as strategic

6  Minna Saunila et al. management, manufacturing and operations management, marketing, industrial economics, and product lifecycle management. Further research will be required to clarify the mechanisms through which sustainable value creation can be achieved via real-​time simulation and its related technologies.

1.4  Future research directions There are clear signs that the benefit of incorporating the business perspective into real-​time simulation studies is acknowledged in practice and theory. The chapters of this book show that real-​time simulation and its related applications, such as digital twinning, can be utilized in a variety of company operations that not only affect its business, but also impact society at large. This book argues that real-​time simulation contributes to sustainable value creation, for example, in terms of product development, marketing, and service businesses (see Figure 1.1). For example, from a macro perspective, real-​time simulation and its extensions can greatly impact sustainability performance. The research concerning real-​ time simulation has mainly focused on the physical modeling of products and production systems, where modeling refers to the process of representing a physical entity in digital forms that can be processed, analyzed, and managed by computers (cf., Qi et al., 2020). In addition to physical modeling, the results of the chapters together with some prior studies show that, for example, digital twins can be used for simulation, monitoring, diagnostics, prognostics, optimization, and for the training of users, operators, maintainers, and service providers.

Value creation via…

New business opportunities

Marketing Service business

Business model

Supply chain optimization

Education Product development

Sustainable production based on…

IoT and simulation technologies

Process improvement

Real-time simulation

Digital/virtual environments

Figure 1.1 Sustainable value creation with real-​time simulation and digital twins.

Creating value with sustainable production  7 This book indicates that in addition to using real-​time simulation for physical assets, digital-​twin technology can be applied to non-​physical modeling that in all forms can provide comprehensive support for decision-​making covering a wide range of company operations (cf.,Vijayakumar, 2020). The chapters of this book indicate that potential benefits of digitization are manifold and include increases in sales or productivity, innovations in value creation, and novel forms of interaction with customers. From a theoretical point of view, the book significantly supports and contributes to the expertise related to digital twins and especially real-​time simulation. The book promotes the application of real-​time simulation in industry, the exploitation of business value, and the implementation of new science-​intensive commercial innovations. However, in the future, the most significant innovations should be combinations of physical and non-​physical solutions. Consequently, future research must understand and utilize digital twins and real-​time simulation as a complementary product or complement to physical products that can be licensed and scaled. A novel aspect for future research is to examine different types of solutions through a variety of perspectives. Table 1.1 offers several topics and research questions for future research.

1.5  Conclusion This book provides a comprehensive overview of potential opportunities and the business value proposition related to implementing sustainable production using physics-​based real-​time simulation. It offers a framework for a rethinking and shift in mindset of how real-​time simulation changes the way products are manufactured and services are produced. Already established as an important part of product development, the application of physics-​based real-​time simulation to improve marketing and business practices is new. This book describes the environment and offers the reader insight, accounting for all the relevant engineering areas, into a number of business opportunities associated with the new approach.The foundation of the book is to establish that physics-​based real-​time simulation will make possible the development of new products and services that will increase customer value and open up new business opportunities. The book includes views from academia and industry to offer a truly holistic approach. The scientific novelty of the book is built upon the following two premises. Entire community-​ based, real-​ time, simulator-​ driven environments are developed and evaluated. And, rather than examining purely technical aspects, the discussion focuses on enhancing user experience and business value.

8  Minna Saunila et al. Table 1.1 Suggestions for future research Perspective

Suggestions

Possible research questions

Strategic management

Strategy-​driven approaches to technology implementation Ecosystem formation

Performance management

Measuring and managing value of novel technologies

Operations management

Tactical approaches of managing digital transformation

Production planning

Digital transformation of service production

Innovation and technology management

Technology-​driven transformation and renewal

Supply chain management

Managing digital transformation beyond organizational boundaries

Industrial marketing

New product design with the assistance of novel technologies The role of different types of capabilities

How could real-​time simulation and digital twins change strategic management practices? Which information will be necessary for decision-​making? How to manage business ecosystems around real-​time simulation and digital twins? How to measure and manage performance with the assistance of real-​time simulation and digital twins? How could real-​time simulation support the development of performance measurement? How to facilitate operations with real-​time simulation and digital twins? What kind of operational implications do the adoption of real-​time simulation and digital twins can have? How could real-​time simulation and digital twins impact on the redesign of production planning? Which are the main barriers that companies will have to face? Are innovations in simulation created for identified business need, or are new innovations triggering new business needs? How to facilitate continuous business renewal with real-​time simulation and digital twins? How does the adoption of novel technologies increase transparency and visibility in the supply chain? How could real-​time simulation and digital twins be used for the governance of the supply chain? How different solutions suit for different industries? How to leverage new service business as well as generating new business models around real-​time simulation and digital twins? What type of functionalities should be connected to digital twins to use them to generate new business?

Creating value with sustainable production  9

References Jaiswal, S., Korkealaakso, P., Åman, R., Sopanen, J., & Mikkola, A. (2019). Deformable terrain model for the real-​time multibody simulation of a tractor with a hydraulically driven front-​loader. IEEE Access, 7, 172694–​172708. Jones, D., Snider, C., Nassehi, A., Yon, J., & Hicks, B. (2020). Characterising the digital twin: A systematic literature review. CIRP Journal of Manufacturing Science and Technology, 29, 36–​52. Kaewunruen, S., & Lian, Q. (2019). Digital twin aided sustainability-​based lifecycle management for railway turnout systems. Journal of Cleaner Production, 228, 1537–​1551. Khadim, Q., Kaikko, E. P., Puolatie, E., & Mikkola, A. (2020). Targeting the user experience in the development of mobile machinery using real-​time multibody simulation. Advances in Mechanical Engineering, 12(6), 1687814020923176. Kokkonen, K., Hannola, L., Rantala, T., Ukko, J., Saunila, M., & Rantala, T. A. K. (2020). Digital twin business ecosystems –​Preconditions and benefits for service business, Proceedings of the 21st International CINet Conference, 1–​11. Nasiri, M., Ukko, J., Saunila, M., & Rantala, T. (2020). Managing the digital supply chain: The role of smart technologies. Technovation, 96–​97, 102121. Pan, Y., Dai, W., Xiong, Y., Xiang, S., & Mikkola, A. (2020). Tree-​topology-​oriented modeling for the real-​time simulation of sedan vehicle dynamics using independent coordinates and the rod-​removal technique. Mechanism and Machine Theory, 143, 103626. Qi, Q., Tao, F., Zuo,Y., & Zhao, D. (2018). Digital twin service towards smart manufacturing. Procedia CIRP, 72, 237–​242. Qi, Q.,Tao, F., Hu,T., Anwer, N., Liu, A.,Wei,Y., … & Nee, A.Y. C. (2020). Enabling technologies and tools for digital twin. Journal of Manufacturing Systems, 72, 237–​242. Saunila, M., Nasiri, M., Ukko, J., & Rantala,T. (2019). Smart technologies and corporate sustainability:The mediation effect of corporate sustainability strategy. Computers in Industry, 108, 178–​185. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S.C.Y., & Nee, A.Y.C. (2019). Digital twin-​ driven product design framework. International Journal of Production Research, 57(12), 3935–​3953. Ukko, J., Nasiri, M., Saunila, M., & Rantala,T. (2019). Sustainability strategy as a moderator in the relationship between digital business strategy and financial performance. Journal of Cleaner Production, 236(1), 117626. Ukko, J., Saunila, M., & Rantala, T. (2020). Connecting relational mechanisms to performance measurement in a digital service supply chain. Production Planning & Control, 31(2–​3), 233–​244. Vijayakumar, D. S. (2020). Digital twin in consumer choice modeling. Advances in Computers, 117(1), 265–​284. Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X., & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-​enabled production data. International Journal of Production Economics, 165, 260–​272. Zhou, G., Zhang, C., Li, Z., Ding, K., & Wang, C. (2020). Knowledge-​driven digital twin manufacturing cell towards intelligent manufacturing. International Journal of Production Research, 58(4), 1–​18.

Part I

Industrial needs of sustainable production

2  Identifying industrial needs for real-​time simulation and digital twins Lea Hannola, Ilkka Donoghue, Kirsi Kokkonen, Kalle Elfvengren and Jorma Papinniemi

2.1  Introduction The introduction of digital tools and technologies (e.g., real-​time simulation) or virtual models (e.g., digital twins) for enhancing business success is not enough. Matching these enabling technologies to the actual needs of users and customers is equally important. This chapter identifies and analyzes current industrial needs to develop various digital approaches, e.g., simulation models and digital twinning into the different phases of the product lifecycle in manufacturing companies. The digital approaches and tools are facilitators to respond to lifecycle-​related requirements, e.g., sustainability of a product, traceability, and the reusability of data and information in manufacturing environments. The effective use of product and lifecycle information via real-​time simulation also enables faster responses to changes in customer needs and product-​ service-​related requirements. Simulation tools and digital twins can function as information carriers throughout a product lifecycle. These tools can also generate information to predict how the product will behave in its lifecycle state. Their ability to provide information based on the physics they can digitalize is their strength. Product lifecycle requirements also include service-​based requirements leading to the concept of a product-​service system (PSS). Another objective of introducing new digital approaches, such as IoT technologies, is to support customer processes to increase customer value. Extending traditional physical products to include digital PSSs, co-​creation, and the deeper involvement of multiple stakeholders in business processes all point to the need to reform customer and innovation processes and build relevant corporate capabilities. For instance, real-​time simulation technologies and tools affect new product development processes by replacing prototype testing with virtual prototype testing, which shortens new product development processes and enhances accuracy. Recent research has strongly focused on how real-​time simulation or digital twinning affect product development (e.g., Alaei et al., 2018; Donoghue et al.,

14  Lea Hannola et al. 2018a, 2019). Jones et al. (2020) have classified research papers from the last ten years related to digital twins with respect to the product lifecycle model developed by Stark (2015). Their results reveal that research focuses mainly on the realization and support/​use phases of the lifecycle. There are relatively few papers that consider digital twinning across all phases. The study by Jones et al. (2020) encourages further research to understand the requirements of digital twins across the entire lifecycle, and to find out whether the existing approaches from other lifecycle phases are applicable. Further, Jones et al. argue that performing this research could lead to benefits, e.g. reducing costs, risks, and design time; fostering innovation; and improving general reliability and decision-​making, especially in the imagine, define, and retire/​dispose phases of the product lifecycle. Therefore, the objective of this chapter is to collect and analyze the current needs of several industrial companies related to potential opportunities enabled by real-​time simulation and digital twinning across the entire lifecycle for their PSSs. The data for the analysis were collected by carrying out semi-​structured interviews with representatives from nine manufacturing companies. The results of the study are key to enabling the digital transformation of business processes and the successful implementation of real-​time simulation and digital twin approaches across the entire lifecycle of PSSs. The rest of this chapter is structured as follows. The next section reviews related research on real-​time simulation, digital twins, and product lifecycle management (PLM). It also presents the research approach and data collection methods followed by a discussion of the interview results. The final section offers conclusions and suggests further research directions.

2.2  Real-​time simulation and digital twins over the product lifecycle 2.2.1  Real-​time simulation and digital twins In use for the past three decades, real-​time simulation is not new. The challenge has been developing simulation models that produce sufficiently accurate results. Real-​time simulation is a technology that enables the development and use of simulation environments to model the real-​world behaviors of a product solution within its operating environment in real-​time. This can be, for example, an aircraft operating in its simulated mission environment beginning with pre-​ flight checks and ending with the aircraft parking in its hangar upon mission completion. For this model to have value, it must be capable of simulating real-​world physics as accurately as possible. The objective of real-​time simulation is to produce a precise model of a complex system and its operating environment that represents the system’s physical behavior defined by physics equations that describe the system and the operating environment (de Jalon & Bayo, 1994).

Identifying industrial needs  15 Simulation tools have been used widely for decades in the planning and design phases of electrical system development, and they have played a critical role in the successful development of a huge number of applications, e.g., from the layout of transmission lines in large-​scale power systems to the optimization of motor drives in transportation (Bélanger et al., 2010). According to Guillaud et al. (2015), digital real-​time simulations can be used to develop models and design new concepts or devices for various applications, prototyping and its implementations, and teaching and training. The concept of Digital Twins has been in the scope of PLM for about two decades, but the language used to describe the digital twin has evolved over the years (Grieves, 2006) as technology has advanced. The digital twin originated in 2002 when the digital twin concept was presented at the first time by Dr. Michael Grieves from the University of Michigan. Grieves (2019) sees the digital twin as a model connected to its real-​world counterpart so that the digital twin and its real-​world counterpart form a connected dual system that are copies of one another. The connection between system elements can be either one way or two way, where data or information flows back and forth. The flow of information can come from a real-​time simulation model and its simulation environment where the operational parameters can be compared to the real-​world counterpart. 2.2.2  Product-​service system and product lifecycle management Extending traditional physical products into services has led to the concept of the PSS. Baines et al. (2007) define a PSS as an integrated combination of products and services where products are tangible and services intangible. A PSS is a special case of servitization where a manufacturing company offers value to the market with the inclusion of services defined around the core products. This can offer a unique solution for the customer that builds value that is difficult to copy. Typically, manufacturing companies are product based, and they use servitization to offer complimentary services over the product lifecycle. PLM is a systematic approach to managing and developing products and product-​related information (Sääksvuori & Immonen, 2013). Grieves (2006) defines PLM as an information-​driven methodology that integrates people, processes/​ practices, and technologies throughout a product’s entire life including its development, manufacture, deployment, maintenance, removal, and final disposal. Different authors describe the product lifecycle phases using various terms and categories. Kiritsis (2011) categorizes the product lifecycle process into the following three main phases. • Beginning of life (BOL) includes conceptualization, definition, and realization processes. • Middle of life (MOL) includes usage, service, and maintenance processes.

16  Lea Hannola et al. • End of life (EOL) includes reuse of the product with refurbishing, reuse of components with disassembly and refurbishing, material reclamation with and without disassembly, material reclamation with disassembly, and disposal with or without incineration. According to Terzi et al. (2010), BOL includes design and manufacturing. Design comprises product, process, and plant design and includes several sub-​actions, such as analyzing requirements, defining concepts, doing more detailed design, developing prototypes, and performing tests. Further, MOL includes distribution (external logistic), use, and support (in terms of repair and maintenance). Finally, EOL is where products are retired, i.e., reverse logistics targeting recycling (disassembly, remanufacture, reuse, etc.) or disposal. Stark (2006) has introduced two viewpoints for the product lifecycle: the manufacturer’s view and the user’s view. From the manufacturer’s viewpoint, a product’s lifecycle lasts from the idea of the product via its production, realization, support, and services to its retirement. For users, a product has a “life” from the moment they acquire it and start using it to the moment they stop using it or dispose of it. These two viewpoints have congruent steps, but especially the last two steps are different and not chronologically related; a user may stop using the product, but the manufacturer still produces the product and related services. On the other hand, the manufacturer may retire the product well before the user disposes of it. Donoghue et al. (2018b) have identified in their study that companies can also have a PLM framework that includes three lifecycle phases and the interaction between the different product layers, which need to be managed with the different core business processes. This PLM framework is illustrated in Figure 2.1. These three lifecycle phases are (1) product lifecycle 1, which focuses on the existing product portfolio and developing new PSSs based on the markets requirements (outside-​in), (2) product lifecycle 2, which focuses on sell

Product Prod Pro duct Lifecycle Liffecycle 2 Li (Delivered Instance) (Del De iver ivered ed In Ins staance)

Product Prod Pro duct Lifecycle Liffecycle 1 Li (Product (P Produ d ct in in Portfolio) Porttfoli Por lio o) Market Process

R&D Process

Marketing Process

Sell Process

Product Lifecycle 3 (Instances in Operation) Deliver Process

Maintain Installed Base (Operations)

Product Lifecycle Management (PLM) to enable Product & Service Digitalization

Outside-In Market Requirements

Inside-Out Product & Service Marketing

Figure 2.1 Product lifecycle management framework (modified from Donoghue et al. 2018b).

Identifying industrial needs  17 and deliver processes, and (3) product lifecycle 3, which focuses on maintaining the installed base and operations.There is a marketing process between product lifecycles 1 and 2, which focuses mainly on product and services marketing (inside-​out) to increase market awareness of the existing products and services to the existing and new customers. The development of shared virtual space has radically changed the way we use information to manage products and their lifecycles (Grieves, 2006; Tao et al., 2019). The premise that each product consists of two systems was introduced first time in the beginning of the twenty-​first century (Grieves & Vickers, 2017). The two systems include the physical system, which has always existed, and a new virtual system (the digital twin), which contains all product information. In 2006, Grieves presented an Information Mirroring Model (IM Model) that comprises a physical space, a virtual space, the linkages between these two spaces, and a virtual simulation space.The IM Model not only enabled the capture of information during the product lifecycle, but it also made it possible to simulate various product actions that would be costly or even destructive to carry out in real life. Grieves later expanded the digital twin concept via the introduction of the Digital Twin Prototype, Digital Twin Instance, Digital Twin Aggregate, and Digital Twin Environment. Together, these enable the application of a data-​driven approach throughout the entire lifecycle of a product (Grieves, 2019; Grieves & Vickers, 2017). Even though digital twinning has recently attracted much attention in both academia and companies, the concept and its industrial applications need consolidation (Jones et al., 2020). For example, Tao et al. (2019) argue that the convergence of a physical product and the virtual space is still usually absent in many companies. Jones et al. (2020) state that establishing the requirements and realizing the benefits of digital twin solutions across the product lifecycle is still quite challenging and should be researched in different industrial contexts. Further research to understand the needs of digital twins throughout the entire lifecycle should be encouraged.

2.3  Methodology In this chapter, the research methodology is qualitative.The examination focuses on activities occurring in the work environment and attempts to make sense of or interpret phenomena in terms of the understanding of the workers (Denzin & Lincoln, 2005). Theoretical and empirical knowledge is combined via case studies (Yin, 2013).Yin (2013) defines a case study as an empirical inquiry that investigates a contemporary phenomenon within its real-​life contexts, especially when the boundaries between phenomenon and context are not clear. The data collection method comprises semi-​structured interviews with nine manufacturing companies and the data were collected between October 2017 and March 2018. The number of interviewees from the companies varied from two to seven. These were group interviews, and there were two interviewers

18  Lea Hannola et al. present at each session.The interviews were documented in written format, and the key results of the interviews were summarized into separate documents and sent to each company for review and further comment. Detailed information about the interview participants is summarized in Table 2.1. The objective was to identify and analyze the current needs of industrial companies related to the potential opportunities of real-​time simulation across the entire lifecycle of their PSSs. In addition, viewpoints were collected related to digital twinning. The interview questions were divided into four different subject areas as follows: • • • •

Digitalization in business and product processes The role and possibilities of simulation and digital twins The key benefits and functionalities of simulation Company architecture and simulation; processes, data, systems, and tools

The focus of the study was to analyze the interview results related to the digitalization of business and product processes and the role and possibilities of real-​ time simulation and digital twins. First, the starting point for each company was discussed, especially addressing the implementation status for real-​time simulation and digital twinning in their product processes, i.e., in different phases of the product lifecycle. Second, interviewee expectations and their ideas about the possibilities offered by the simulation tools or digital twins were collected

Table 2.1 Information of the data collection process Company

Main products and services

Alfa

Forestry and material handling technology and solutions Elevators, escalators, automatic building doors, monitoring, access, and destination control systems Material handling solutions, attachments, and expert services Tractor manufacturer Hydraulic cylinders and solutions, motion control, and related services Machinery, systems, and technology for the production of plywood and veneer Drive technologies and solutions Trucks, automatic truck systems, and related services Tools and tooling systems for industrial metal cutting, stainless steels

Beeta Gamma Delta Epsilon Zeeta Eeta Theeta Ioota

Interviewees

Interview date

2

10 October 2017

4

23 October 2017

2

12 October 2017

4 1

22 September 2017 23 October 2017

2

27 October 2017

11 3 7

8 January 2018 20 March 2018 21 December 2017

Identifying industrial needs  19 and discussed focusing on the needs of the company and the nature of the benefits these technologies could bring to the product lifecycle. The collected data were analyzed by reviewing the interview summaries to collect all industrial needs related to real-​time simulation and digital twins. These needs were grouped together based on the lifecycle phase they represent.

2.4  Results –​identified industrial needs for real-​time simulation and digital twins The results of the study revealed several industrial needs and suggested possibilities related to the digitalization of product processes and the utilization of real-​ time simulation tools or virtual tools and technologies such as digital twinning or virtual-​and augmented-​reality technologies. The following paragraphs present the main results based on the PLM framework presented earlier in the related research section (see Figure 2.1). The interview results about identified industrial needs are divided into three phases following the structure of the PLM framework: • Needs related to product lifecycle phase one –​Product in Portfolio (Figure 2.2) • Needs related to product lifecycle phase two –​Delivered Instance (Figure 2.3) • Needs related to product lifecycle phase three –​Instances in Operation (Figure 2.4). In product lifecycle phase one (Figure 2.2), companies had mainly focused on the R&D process phase for industrial needs. Interviewees indicated they had already utilized real-​time simulations or other digital tools to support and accelerate their R&D processes.Virtual testing was highlighted as a key requirement to receive user and customer feedback as early as possible and to test virtual prototypes, machine implements, automatic functions, and software. Further, the need to virtually test several machines and virtually simulate its environment was also mentioned. However, it was emphasized that real-​time simulation models or digital twins should be developed as accurately as possible so the most relevant data could be obtained from customers and end users. The results also indicated that these companies have realized the value that simulation brings to their marketing processes. Interestingly, the interviewees did not separate marketing activities into the two different processes the PLM framework suggests, i.e., outside-​in and inside-​out marketing. The collected needs were divided into outside-​in market requirements and inside-​out product and service requirements. Utilizing simulation tools was seen as important to marketing to determine customer needs before kicking off a new product development project and to collect feedback from the developed PSSs after completion of the R&D process. Further, enhancing the user experience with augmented reality (AR) and virtual reality (VR) technologies was mentioned

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Product Lifecycle 1 (Product in Portfolio)

Market Process

 Lifecycle marketing  Collecting user and customer needs through simulation models before product development  Testing different products, parts or constructions with customers  User experience in different levels in virtual environment, e.g., user interface for driving experience  New possibilities and restrictions of AR/VR technologies in testing user experience  Need to measure user experience

R&D Process

 Customer feedback in the early phases of R&D  Acceleration of the R&D process, e.g., a real-time simulator to get feedback saves time and cost - no need to travel to arrange test times  Fact-based R&D process  Development of virtual user interface – simulation as real as possible for users  Virtual testing of new prototypes, implements in machines, automatic functions and software  Virtual testing cooperation of several machines  Detailed real-time simulation model of a vehicle, a part of vehicle or an environment  Detailed real-time model enabling software testing from the point of vehicle efficiency

Outside-In Market Requirements

Figure 2.2  Industrial needs for product lifecycle 1 –​Product in Portfolio.

Marketing Process

 Marketing new product-service systems through simulation  Lifecycle marketing  Indicating e.g., energy savings for customers  Testing modular parts with AR/VR  New IoT data-based business and lifecycle services

Inside-Out Product & Service Marketing

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Product Lifecycle 2 (Delivered Instance)

       

Sell Process

Deliver Process

 Education and training through simulation  Detailed real-time simulation model of a vehicle, a part of vehicle or an environment  Data gathering by IoT can open opportunities to consult a customer, e.g., related to productivity => Overall efficiency  Simulation of project management

Figure 2.3  Industrial needs for product lifecycle 2 –​Delivered Instance.

Identifying industrial needs  21

Simulation as selling and marketing tool Product showcasing Supporting buy decisions of customers New opportunities of VR/AR in sell process Tools to increase service business New service business models New IoT data-based business and lifecycle services Need for modularity in sales, possibility to select between different customer solutions  Pre-order services – information and feedback about customers as early as possible before a buy decision

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Maintain Installed Base (Operations)  Embedded real-time models in service business (e.g., Digital Twin – models in maintenance, predictive maintenance and diagnostics)  Use of real-time simulation with IoT-systems (e.g. utilizing unit or product specific data, production test results, control parameters in Digital Twin model and further refining of design and maintenance parameters)  On-time preventive service calls  Visualization and digital improvements in documentation of maintenance services  Modernization of machines at customers sites; increasing new functionalities  Modular subassembly/asset models for quick testing of differently configured machines

 Tools to increase service business  New service business models  Advanced sensoring based on virtual modelling for new service models and system cost optimization  Increasing service business supporting customer´s production processes by optimizing them and assisting maintenance  Better service availability – improved customer satisfaction  Effective usage of resources in service business  New IoT data-based business and lifecycle services  User experience in different levels in virtual environment

Figure 2.4 Industrial needs for product lifecycle 3 –​Instances in Operation.

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Product Lifecycle 3 (Instances in operation)

Identifying industrial needs  23 as a way to further improve marketing. Interviewees suggested that a tool to measure user experience was also necessary. In addition, the possibilities of AR/​ VR technologies in enabling customers to test modular parts of a product were highlighted. In the product lifecycle phase two (Figure 2.3), industrial needs were divided into sell and deliver processes. However, the interviewees brought up that marketing and sales processes are usually seen as one entity, and the needs are therefore difficult to distinguish from each other. Simulation tools and virtual models were considered important additions to the sales processes, where they could be used to further increase sales by promoting more service business. Simulation tools were also seen as beneficial for supporting the customer buy decisions during product showcasing, because they make it possible for the customer to “see, test, and feel” the final product. Moreover, virtual modeling was seen to support pre-​order services, where information and feedback about customers can be collected as early as possible before their final buy decision. The usage of simulation tools and digital twins in education and training was mentioned as one of the main needs in the deliver process. Simulation tools could enhance the training process of new PSSs at customer sites. Especially, there was a growing interest in testing AR or VR technologies for training. In addition, the interviewees mentioned that these virtual models could also be utilized for the digitalization and visualization of project management practices. Interview results, however, revealed there were relatively few ideas on how to apply simulation tools to improve the delivery process. For example, the interviewees did not mention any industrial needs related to manufacturing processes or production. In product lifecycle phase three (Figure 2.4), the industrial needs focused on operation instances, i.e., maintenance and service businesses. The use of real-​time simulation with IoT-​systems adds new possibilities for utilizing unit or product specific data, production test results, or control parameters in the digital twin model and further refines design and maintenance parameters. Interviewees also suggested that real-​time models such as the digital twin are needed in predictive maintenance and diagnostics, e.g., on-​time preventive service calls. Further, the use of simulation tools to enhance the visualization of maintenance services was mentioned. Developing an online shop for maintenance services to ensure better service availability is an example. Also, by optimizing customer production processes and assisting in maintenance, the virtual tools were seen as important to building the service business. The interviewees brought up several times the need to explore the potential of digital tools and technologies (e.g., real-​time simulation), or virtual models (e.g., digital twins) to increase their service businesses and to develop both suitable and sustainable business models to achieve a competitive market advantage. The additional services for customers could be provided in each phase of a product’s lifecycle, but the need for more holistic services was also highlighted. The willingness to provide entire lifecycle services to support customer business processes is an example.

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2.5  Conclusions Based on the empirical results, product lifecycle 1 is the phase where real-​ time simulation solutions have already been utilized widely by industrial companies, especially in R&D processes, but also to support marketing activities before and after product development. With the fast development of digital twin solutions, companies are beginning to accelerate existing R&D activities, gather increasing amounts of customer data before and after the product development, and improve marketing processes and tools. In the second product lifecycle phase, product lifecycle 2, identified needs were scarcer and mainly related to the sales of a product/​solution rather than to its installation. Company needs were identified, e.g., in promoting further sales, supporting buying decisions of the customers, and training processes at customer sites. In the product lifecycle 3, the identified needs related mainly to improving maintenance and monitoring services. A need to explore new service business opportunities made available by the increasing amount of IoT data, for example, was also expressed. The empirical results support the previous findings from the recent literature (e.g., Jones et al., 2020) that digital twins that cover the entire lifecycle are still rare. One remarkable reason for this may be that digital twin solutions are still under development in many companies; their utilization has become general in R&D processes, and increasingly, these solutions are integrated into instances that are in the early phases of operation. It will take time until these products and processes are at the end of their relatively long lifecycles. Therefore, the necessity of real-​time simulation or digital twinning at product lifecycle end may not yet be clear. Moreover, many manufacturing companies are still taking early steps in their servitization, and PSSs and related service business capabilities are still developing. In manufacturing companies, product development is still often product-​ centric, and the role of services is complementary. Congruently, PLM and real-​ time simulation solutions have traditionally concentrated on physical products. As companies are transitioning toward co-​ development of product-​ service offerings across the entire product lifecycle, the need for digital twins to support lifecycle services is expected to grow, as also revealed by the empirical research. However, digitalization may still take time in many manufacturing companies. One of the main concerns is associated with data ownership, i.e., who owns the data and how and by who the data can be used. Often the up-​to-​date information about the installed base is with the customer, and the information that the manufacturer needs is spread over multiple back-​end systems (e.g., PLM, enterprise resource planning, and manufacturing execution systems).This makes it challenging to maintain a relevant digital twin and carry out meaningful real-​time simulation later in the product lifecycle. Certain companies in the study have emphasized collecting good install base information for services, but the information is not connected to real-​time simulation models.

Identifying industrial needs  25 One of the challenges faced by all the companies surveyed is data security and how to comply with the different data security requirements that customers and authorities are imposing, which complicates the application of digital twinning and real-​time simulation models that represent customer instances. As the amount of data increases in companies and in the business ecosystems, the pressure or opportunity to utilize digital twins will increase also. This scenario also opens even more opportunities to take advantage of real-​time simulation in business operations. Customer needs are not divided or spread over the PLM lifecycle phases presented in this chapter. The needs determined here are typically internal to the companies surveyed, and they do not include the customer view directly. An example of this is the marketing activities done before and after delivery that could result in a continuous marketing lifecycle that supports customer engagement throughout the product lifecycle. Customer needs are still collected in a way that reflects the manufacturers’ view of the product lifecycle, which represents an inside-​out view of the market. Interestingly, based on the survey results, there is a gap between real-​time simulation and delivery in PLM lifecycle phase 2. Although this gap was not examined thoroughly, some initial conclusions can be offered. Real-​time simulation has only seen limited implementation directed at research and development functions. There have been no production implementations. However, opportunities available to the manufacturing engineering of new products and service are obvious. The current lack of production implementations might indicate that manufacturing engineering has been outsourced to partners and suppliers, and these partners and suppliers did not participate in the survey. This is an area where real-​time simulation could offer benefits and should be investigated in more detail in the future research. The concepts of digital twins and real-​time simulation are not established in the companies surveyed. Many of the companies involved have invested in real-​time simulation, but the business benefits that it brings were difficult for decision-​makers to understand. The most common statement was “why should the company invest in real-​time simulation”. In general, demonstrating the benefits seems to present a challenge. This is probably because in most cases the focus of real-​time simulation has been on product development alone. This study encourages decision-​makers and business managers to consider the potential of digital tools and virtual models throughout the entire lifecycle of PSSs to build relevant corporate capabilities for achieving competitive advantage in the marketplace.

Acknowledgments This study was a part of the DigiPro project and has received funding from Business Finland and the SIM research platform (sustainable product processes through simulation) at LUT University, Finland.The authors would also like to thank all the involved companies for the collaboration.

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References Alaei, N., Rouvinen, A., Mikkola, A., & Nikkilä., R. (2018). Product processes based on digital twin. In Berns et al. (Eds.), Commercial vehicle technology (pp. 187–​194). Wiesbaden: Springer Vieweg. Baines T. S., Lightfoot H., Steve E., Neely A., Greenough R., Peppard J., … Wilson H. (2007). State-​of-​the-​art in product service-​systems, the Proceeding of IMechE, Part B: Journal of Engineering Manufacture, 221, 1543–​1552. Bélanger, J., Venne, P., & Paquin, J. -​N. (2010). The what, where and why of real-​time simulation, the Proceeding of PES General Meeting, Oct. 2010, 37–​49. Denzin, N., & Lincoln, Y. (2005). The Sage handbook of qualitative research (3rd ed.). Thousand Oaks, CA: SAGE Publications. Donoghue, I., Hannola, L., & Mikkola, A. (2019). The value of digital twins and IoT-​ based services in creating lifecycle value in B2B manufacturing companies, Portland International Conference on Management of Engineering and Technology, 25–​29 August 2019, Portland, OR. Donoghue, I., Hannola, L., & Mikkola, A. (2018a). The benefits and impact of digital twins in product development phase of PLM. In Chibert, P. et al. (Eds), Product lifecycle management to support industry 4.0 (pp. 432–​441). Cham, Springer. Donoghue I. D. M., Hannola L.T., & Papinniemi J. J. (2018b). Product lifecycle management framework for business transformation. LogForum, 14(3), 293–​303. Grieves, M. (2006). Product lifecycle management. Driving the next generation of lean thinking. New York: McGraw-​Hill. Grieves, M. (2019). Virtually intelligent product systems: Digital and physical twins, in complex systems engineering: Theory and practice. In Flumerfelt, S. et al. (Eds), American Institute of Aeronautics and Astronautics (pp. 175–​200). American Institute of Aeronautics and Astronautics: Reston,VA. Grieves M., & Vickers J. (2017). Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Kahlen F. J., Flumerfelt S., Alves A. (Eds.), Transdisciplinary perspectives on complex systems (pp. 85–​113). Springer, Cham. Guillaud, X., Faruque, O., Teninge, A., Hariri, A.H., Vanfretti, L., Paolone, M., … Davoudi, A. (2015). Applications of real-​time simulation technologies in power and energy systems. IEEE Power and Energy Technology Systems Journal, 2(3), 103–​115. de Jalon G. J., & Bayo, E. (1994). Kinematic and dynamic simulation of multibody systems:The real time challenge. New York: Springer-​Verlag. Jones, D., Snider, C., Nassehi, A., Yon, J., & Hichs B. (2020). Characterising the digital twin: A systematic literature review. CIPR Journal of Manufacturing Science and Technology, NULL (2020), DOI: https://​doi.org/​10.1016/​j.cirpj.2020.02.002. Kiritsis, D. (2011). Closed-​loop PLM for intelligent products in the era of the Internet of things. Computer-​Aided Design, 43, 479–​501. Sääksvuori, A., & Immonen, A. (2013). Product lifecycle management. Berlin Heidelberg: Springer Science & Business Media. Stark, J. (2006). Product lifecycle management –​21st century paradigm for product realization. London: Springer-​Verlag London Limited. Stark, J. (2015). Product lifecycle management –​21st century paradigm for product realisation (3rd ed.,Vol. 1). London: Springer-​Verlag London Limited. Tao, F, Sui, F, Liu, A., Qi, Q., Zhang, M., Song, B., … Nee, A.Y. C. (2019). Digital twin-​ driven product design framework. International Journal of Production Research, 57(12), 3935–​3953.

Identifying industrial needs  27 Terzi, S., Bouras, A., Dutta, D., Garetti, M., & Kiritsis, D. (2010). Product lifecycle management –​from its history to its new role. International Journal of Product Lifecycle Management, 4(4), 360–​389. Yin, R. K. (2013). Case study research: Design and methods. Thousand Oaks, CA: SAGE Publications, 312 p.

3  Company capabilities and implementing real-​time activities Mina Nasiri, Juhani Ukko, Minna Saunila and Tero Rantala

3.1  Introduction With a rapid expansion in the number of businesses, real-​time activities are becoming crucial to address the developing demands of remotely located business units such as manufacturing sites, retailers, and service centers (Oyekan et al., 2017). Here, real-​time activities are defined as different tasks that can be handled via the collaboration of geographically separated individuals in actual time. Real-​time inventory is one example of real-​time activity in manufacturing companies that ensures the sufficiency of stock on hand by continually monitoring stock levels (Büyüközkan & Göçer, 2018). Monitoring production processes remotely, providing maintenance and repair with remote access, as well as offering online products and services are all examples of real-​time company activities (Lenka et al., 2017; Lerch & Gotsch, 2015; Parida et al., 2015). However, there is a lack of information about what capabilities are needed for companies to successfully operate in real time. This study contributes to filling that research gap by examining the capabilities needed to implement real-​time activities within a manufacturing company. To make the right decisions, the right information must be available at the right time for the right person in the right format (Zhang et al., 2012). This is made possible by determining appropriate resources, strategies, and capabilities (Büyüközkan & Göçer, 2018; El Sawy et al. 2016; Matt et al. 2015; Wu et al., 2010). In this study, therefore, both digital business strategies and digital capabilities have been presented as influential factors to achieving real-​time operation. A digital business strategy encompasses managerial capabilities and operational capabilities (Liu et al., 2013, 2018; Ukko et al., 2019). Digital capabilities comprise human capabilities (Khin & Ho, 2019; Lenka et al., 2017), collaboration capabilities (De Oliveira et al., 2019; Lenka et al., 2017), technical capabilities (De Oliveira et al., 2019; Khin & Ho, 2019; Lenka et al., 2017; Parida et al., 2015), and innovation capabilities (Khin & Ho, 2019; Parida et al., 2015). The results show a statistically significant difference in the mean of digital business strategy (managerial and operational capabilities) and two of the digital capabilities (human and collaboration capabilities) for the manufacturing companies that consider their level of real-​time activity as high.

Implementing real-time activities  29 The reminder of this chapter is structured as follows. This introduction is followed by the theoretical framework and research model, which investigates the understanding of digital business strategy and digital capabilities, the importance of each of those to real-​time activities, and the research model.The next section describes the experimental examination of real-​time simulation including data collection, descriptive results, and statistical analysis results. The last section presents conclusions, which summarize the research findings and the theoretical and managerial implications, as well as limitations and possible further research.

3.2 Theoretical framework and research model 3.2.1 Theoretical framework 3.2.1.1  Digital business strategy The study defines managerial capabilities and operational capabilities (Li et al., 2018; Ukko et al., 2019) as the components of a digital business strategy. Managerial capabilities refer to the competencies with which companies and entrepreneurs in contemporary operation and business environments develop, extend, and modify the way they operate their businesses (Gauthier et al., 2018). In other words, managerial capabilities refer to the characteristics of a manager’s behavioral abilities to be able to organize and manage resources and people (Welter et al., 2013). Generally, managerial capabilities describe different types of behaviors that differentiate effective from ineffective performance. They can include motives, beliefs, and values (Anzengruber et al., 2017; McLagan, 1996). Gauthier et al. (2018) presented that entrepreneurs’ managerial capabilities can be classified in three categories: managerial human capital, managerial social capital, and managerial cognition. Srećković (2018) argued that managerial capabilities comprise a company’s skills, knowledge, and expertise to operate with complex and challenging production-​and management-​related tasks (Choi & Shepherd, 2004) and its capability to efficiently identify operations for the production and distribution of products and services (Collis, 1994). For example, managerial capabilities of IT executives can be defined as capabilities arising from a deep understanding of company operations and its business environment and from excellent general management skills (Bassellier & Benbasat, 2004; Heart et al., 2010). At the general level, and based on previous literature, the operational capabilities of a company are its competencies that help the operations management system address the challenges of greatest interest to the company and to its critical stakeholders (Dosi et al., 2003; Flynn & Flynn, 2004; Wu et al., 2012). According to Wu et al. (2012), in the field of operations strategy, managers introduce operational change initiatives and allocate resources to develop new practices and capabilities to build and support competitive advantage. In other words, operational capabilities are considered a “secret

30  Mina Nasiri et al. ingredient” needed to develop and maintain a company’s competitive advantage (Wu et al., 2010). Operational capabilities make it possible to integrate and to direct both resources and operational practices. According to Wu et al. (2010), operational capabilities encapsulate both explicit elements, such as resources and practices, and tacit elements, such as expertise and leadership, that companies must apply to find solutions for different challenges. As such, the operational capabilities of the organizations draw on the operational practices and resources to produce outputs and value to stakeholders in a designed manner. They can also be considered a company-​specific set of individual routines, processes, and skills that are implemented and operationalized in everyday workflow to promote business and build value through operational resources and practices. 3.2.1.2  Digital capabilities Digital capabilities can be defined as the set of company capabilities needed to support digital world activities (De Oliveira et al., 2019). A variety of competencies are needed to successfully develop the requisite digital capabilities including human capabilities (Khin & Ho, 2019; Lenka et al., 2017), collaboration capabilities (De Oliveira et al., 2019; Lenka et al., 2017), technical capabilities (De Oliveira et al., 2019; Khin & Ho, 2019; Lenka et al., 2017; Parida et al., 2015), and innovation capabilities (Khin & Ho, 2019; Parida et al., 2015) in the companies. Human capabilities, specifically the digital skills of a company’s personnel, are essential to the integration of digital technologies (Khin & Ho, 2019). Substantial investment in developing the digital skills and readiness of the workforce are required to reach a digital-​competency maturity level (El Sawy et al., 2016; Lerch & Gotsch, 2015; Parida et al., 2015). Collaboration should also be a primary goal for any organization in the current connected world where the competition for the acquisition of opportunities, resources, and various capabilities is fierce. Fruitful collaboration can occur by sharing knowledge, resources, and work practices to facilitate coping with the challenges of building a digital presence to support organizational capabilities (El Sawy et al., 2016; Lenka et al., 2017; Lerch and Gotsch, 2015). Technical capabilities are the backbone of a company’s digitality (De Oliveira et al., 2019; Khin & Ho, 2019; Lenka et al., 2017), which enables the integration of product and services and borderless activity (El Sawy et al., 2016). Without the necessary technical capabilities, a company will find it challenging to operate in real-​time with up-​to-​date services (Parida et al., 2015). And finally, innovation capabilities are key to transforming the traditional way of doing business and modernizing it with new business solutions, processes, and infrastructures (Sia et al., 2016; Xue, 2014). Digitality offers opportunities to develop new services, which foster innovation capabilities and result in a better ability to meet market needs (Parida et al., 2015).

Implementing real-time activities  31 3.2.1.3  Digital business strategy and real-​time activities Several researchers define managerial capabilities and operational capabilities as the key dimensions to a digital business strategy (Liu et al., 2013; Li et al., 2018; Ukko et al., 2019). Dynamic and informed managers may recognize the potential of novel technologies and encourage their introduction, a prerequisite for a successful digital business strategy (Chatterjee et al., 2002; Li et al., 2018). Ukko et al. (2019) claimed that company managers must be (1) familiar with existing digital tools, applications, and solutions, (2) must have a clear vision of how to utilize the digital technologies now and in the future, and (3) must build a management culture that supports the utilization of digital technologies. This can be accomplished, for example, by introducing real-​time/​online sales channels, where the only diversification into the digital world is to make products available via digital channels (Hess et al., 2016). According to Hess et al. (2016), real-​time digital activities can be fully integrated into the firm’s core business, and they mostly affect production processes and to some extent product and service offerings. Operational capabilities in digital business strategies reflect a company’s proficiency in adopting and implementing digital tools and solutions and using them as a natural part of business processes (Benitez et al., 2018; Peng et al., 2008; Ukko et al., 2019). In other words, in digitalized business environments, operational capabilities reflect the planned ability to effectively execute substantive daily operations such as manufacturing, logistics, and sales (El Sawy & Pavlou, 2008; Zawislak et al., 2018), and to efficiently monitor/​develop these operations online. Matt et al. (2015) argued that the scope of digital transformation strategies is more broadly designed and explicitly includes digital activities, such as digital technologies as part of the end-​user product, at the interface with or fully on the side of customers. They considered this meaningful in comparison to process automation and optimization, since digital activities as strategic steps go beyond the process paradigm and include changes to and implications for products, services, and business models. In summary, the managerial and operational capabilities of a digital business strategy seem to be important to the successful implementation and utilization of real-​time activities. Therefore, the evidence from prior studies supports the formation of the following proposition: P1. The extent of a company’s real-​time activities is a function of the strength of its digital business strategy. 3.2.1.4  Digital capabilities and real-​time activities Real-​time data tracking is crucial for contemporary companies. It makes it possible to optimally manage production and related operations by ensuring that the correct information is provided to the correct person at the correct

32  Mina Nasiri et al. time, and in the correct form (Zhang et al., 2012).Therefore, as a crucial operative level competency for any company operating with that type of digitalized business model, digital capabilities may play a significant role in the number of real-​time operations that a company can develop and sustain. Human processes are considered one of the important capabilities for a company’s digitalized business model (Arendt, 2008; Bouncken et al., 2019). The rise of digitality calls for a workforce with greater complexity, abstraction, and problem-​solving skills (Lerch & Gotsch, 2015). One example is the autonomously operating production system, which demands high-​level human capabilities to control those systems and/​or be directed by them (Fischer & Pöhler, 2018). Furthermore, Ramaswamy and Ozcan (2018) reveal that different types of digital interfaces enable personnel to engage remotely in real-​time system environments thereby eliminating issues related to a geographically dispersed workforce. These digital interfaces are also changing human work experiences (Ramaswamy & Ozcan, 2018). Compared to traditional work, real-​time operation requires different capabilities. In addition to in-​house expertise, companies must be able to acquire knowledge via external collaboration to effectively digitalize operations (Bouncken et al., 2019). In the context of digitalized product service systems, there must be tight collaboration among manufacturing firms and their equipment providers and customers (Lerch & Gotsch, 2015). Similarly, Mahesh et al. (2007) propose a model for distributed collaborative manufacturing, where all interacting companies benefit from resource sharing and task redistribution. Technical readiness is also a requirement for operating in a digitalized real-​ time environment. To adopt a digitalized business model, a company must be able to utilize digital technologies comprehensively in all its operations –​value creation, value capture, and value proposition –​not just for certain company activities (Bouncken et al., 2019). One example of technical readiness offered by Lerch and Gotsch (2015, p. 45) is “equipping products with intelligent digital systems that allow the products to operate independently of human intervention and communicate with other machines”. Accomplishing this type of real-​time, remotely controlled activity requires a sound and reliable technical infrastructure (Lerch & Gotsch, 2015). Finally, innovation is at the center of the digital business model, because its successful implementation demands novel technological and organizational innovation (Bouncken et al., 2019). To summarize, a company’s real-​time activities may require the utilization of digital capabilities in terms of its in-​house expertise, its level of collaboration, its technical readiness, and its ability to innovate.Therefore, the following proposition is suggested: P2. The extent of a company’s real-​time activities is a function of the strength of its digital capabilities.

Implementing real-time activities  33

Digital capabilities

Digital business strategy Managerial capability

Operational capability

Human capability Real-time activities

Collaboration capability Technical capability Innovation capability

Figure 3.1 Research model: capabilities affecting the contribution of manufacturing companies considering digital as real-​time activities.

3.2.2  Research model The research model was developed based on the reviewed studies in the context of company real-​time activities. Operating in real time is considered one of the important digitally enabled activities in manufacturing companies, which makes it crucial to study the capabilities required to implement real-​time activities. As demonstrated in Figure 3.1, survey results revealed that a manufacturing company must have a digital business strategy and the necessary digital capabilities to successfully carry out real-​time activities. The digital business strategy must include two determinant capabilities: managerial and operational. The digital capabilities must include human expertise, collaboration, technical ability, and innovation.

3.3  Empirical examination of real-​time simulation 3.3.1  Data collection and sample The data were gathered using a survey questionnaire of small-​and medium-​ sized enterprises (SMEs) that operate in the manufacturing sector in Finland. The questions were addressed to managers. As shown in Table 3.1, real-​time activities were scored based on a 7-​point Likert scale, in which 1 corresponds to “strongly disagree” and 7 corresponds to “strongly agree”. The respondents were also asked if “In our company, digitality refers to operation in real-​time”. An effective digital business strategy must include managerial capabilities and operational capabilities. Each of these were measured based on the three items listed in Table 3.1. Four digital capabilities are also essential. As shown in Table 3.1, these include human capabilities (3 listed items), collaboration capabilities (3 items), technical capabilities (4 items), and innovation capabilities (3 items). Respondents were asked to assess the degree to which they would

34  Mina Nasiri et al. Table 3.1 Survey items Themes

Items

Digital business strategy Managerial Our company’s management is capabilities familiar with digital tools. Our company’s management has a clear vision of utilizing digitality in the future. Our company’s management supports the utilization of digitality in our company. Operational Utilizing digitality in internal capabilities processes has become an important part of our business. Digitality is a natural part of our business. Digitality enhances our business. Digital capabilities Human Digital skills development is capabilities supported and promoted in our company. Our employees are well trained in digital tools usage. Digitalization of the operating environment is easily accepted by our employees. Collaboration Digital cooperation is made capabilities with other companies. Digital channels are used to share information with other companies. Digitality transforms the shape of social relationships in our business. Technical Digitality increases the value of capabilities our products or services. Digitality enables the integration of products and services into our company. Digitality enables up-​to-​ date, location-​independent services for our customer. Digitality allows us to work across boundaries of time, place, or activities.

Scale

References

From 1 to 7 (strongly disagree to strongly agree)

Liu et al. (2013); Li et al. (2018); Ukko et al. (2019)

From 1 to 7 (strongly disagree to strongly agree)

Benitez et al. (2018); Hess et al. (2016); Peng et al. (2008); Ukko et al. (2019)

From 1 to 7 (strongly disagree to strongly agree)

El Sawy et al. (2016); Khin and Ho (2019); Lerch and Gotsch (2015); Parida et al. (2015)

From 1 to 7 (strongly disagree to strongly agree)

El Sawy et al. (2016): Lenka et al. (2017); Lerch and Gotsch (2015)

From 1 to 7 (strongly disagree to strongly agree)

De Oliveira et al. (2019); El Sawy et al. (2016); Lenka et al. (2017); Parida et al. (2015)

Implementing real-time activities  35 Table 3.1 Cont. Themes

Items

Scale

References

Innovation capabilities

Digitality enables innovations and new ideas in our company. Digitality forces us to develop new solutions. Digitality helps to produce new products and services. In our company, digitality refers to operation in real time.

From 1 to 7 (strongly disagree to strongly agree)

Khin and Ho (2019); Parida et al. (2015); Sia et al. (2016); Xue (2014)

From 1 to 7 (strongly disagree to strongly agree)

Lerch and Gotsch (2015); Parida et al. (2015)

Real-​time activities

agree or disagree with the each of the statements listed in the table by selecting a number from 1 to 7 (1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5 = slightly agree, 6 = agree, or 7 = strongly agree). 3.3.2  Descriptive results Responses were received from 116 companies. Of these, 64 percent were small (less than 49 employees) and the rest (36 percent) were medium-​sized (between 50 and 249 employees). Two-​thirds of the respondents had less than 50 years of experience in their business. One-​fourth had over 50 years of experience. The majority of respondents (93 percent) were business-​to-​business firms. The remaining 7 percent were business-​to-​consumer. Figure 3.2 illustrates the degree to which the surveyed manufacturing companies emphasize real-​time activities. A high percentage (66 percent) were companies that believe digitality means operating in real time (mean of 6–​7 on the 1–​7 scale). Only 34 percent believe that digitality has not resulted in real-​ time activities within the company (mean of 1–​5.99 on the 1–​7 scale). Figure 3.3 illustrates the level of capabilities established by the results for the surveyed Finnish manufacturing companies. Three levels have been defined including low (mean of 1–​3), medium (mean of 3.01–​5.99), and strong (mean of 6–​7). For managerial capabilities, 9 percent of companies came in at the low level, 68 percent were at the medium level, and 23 percent were strong. For operational capabilities, 8 percent came in at the low level, 57 percent were at the medium level, and 35 percent were strong. In terms of human capabilities, 8 percent of the surveyed Finnish manufacturers came in at the low level, 80 percent at the medium level, and 12 percent at the strong level. A similar 9 percent of companies scored low level for collaboration. However, 66 percent came in at the medium level, and

36  Mina Nasiri et al.

Emphasis on real-time activities

0

0.1

0.2

0.3

0.4

0.5

0.6

Strongly disagree

Disagree

Slightly disagree

Slightly agree

Agree

Strongly agree

0.7

0.8

0.9

1

Neither agree nor disagree

Figure 3.2 Manufacturing emphasis on real-​time activities.

Innovation capability Technical capability Collaboration capability Human capability Operational capability Managerial capability 0

0.1

0.2 Low

0.3

0.4

Medium

0.5

0.6

0.7

0.8

0.9

1

Strong

Figure 3.3 Level of capabilities in manufacturing companies.

25 percent were at the strong level. For technical capabilities, 10 percent of the surveyed manufacturing companies fell into the low level tier, 55 percent were at the medium level, and 35 percent claimed a strong-​level capability. Finally, a small 3 percent came in at the low lever for innovation. Over half, 51 percent were at the medium level, and 46 percent were at the strong level. Based on the survey results, the capabilities essential to real-​time operations seem to be available at a good level in Finnish manufacturing companies. Innovation, technical, and operational capabilities received the highest marks. 3.3.3  Statistical analysis results A variance analysis was used to discover if the level of real-​time activity at the surveyed manufacturing companies is a function of their capabilities. Both their digital business strategies and digital capabilities were examined. For digital business strategies, statistically significant differences were found in both managerial and operational capabilities, which means that the makeup of each company’s digital business strategy affects how well they can operate

Implementing real-time activities  37 Table 3.2 The role capabilities in considering digital as real-​time activities among manufacturing companies (N = 116)

Digital business strategy Digital-​related capabilities

Managerial Capabilities Operational Capabilities Human Capabilities Collaboration Capabilities Technical Capabilities Innovation Capabilities

Non-​real time Real time Non-​real time Real time Non-​real time Real time Non-​real time Real time Non-​real time Real time Non-​real time Real time

Mean

F

4.5083 5.2814 4.6417 5.3860 4.2583 4.8831 4.5250 5.1272 4.6688 5.2110 5.2667 5.7127

13.798* 11.311* 9.647* 6.476* 4.564 4.424

*p ≤ 0.01

in real time. In other words, manufacturing companies with strong managerial and operational capabilities also enjoy a high level of real-​time activity. In contrast, companies with relatively weak managerial and operational capabilities struggle to support real-​time operations. These results are summarized in Table 3.2. In terms of their digital capabilities, also as summarized in Table 3.2, human and collaboration capabilities (two of the four digital capabilities surveyed) demonstrate a statistically significant effect on the level of real-​time activities in Finnish manufacturing.The manufacturing companies that have a high-​level of human expertise and collaboration capabilities are able to operate effectively in real time. Technical and innovation capabilities do not have a substantial effect on the level of real-​time activities in manufacturing companies.

3.4  Conclusions The aim of this chapter is to contribute empirically to the implementation of real-​time activities in manufacturing companies. In this regard, the chapter examined the capabilities needed to implement real-​time activities within a manufacturing company. Therefore, this chapter contributes to previous literature by revealing the roles both a strong digital business strategy and digital capabilities play in supporting a company’s real-​time activities. Furthermore, this study reveals which capabilities are most important. 3.4.1 Theoretical implications First, this chapter reflects on prior research by introducing managerial and operational capabilities as the required capabilities to realize a digital business

38  Mina Nasiri et al. strategy for real-​time operations.The results confirmed prior research (Liu et al., 2013; Li et al., 2018; Ukko et al., 2019) that concluded that managerial and operational capabilities are key to realizing a digital business strategy. Moreover, the results also revealed the statistically significant effect of a sound digital business strategy on real-​time activities at manufacturing companies. These real-​time activities are mainly related to conducting every task (from designing to delivering products or services) remotely in actual time (Büyüközkan & Göçer, 2018; Lenka et al., 2017; Lerch & Gotsch, 2015; Parida et al., 2015). Successful real-​time operation depends on having access to the right information at a right time in the right format (Zhang et al., 2012). An effective digital business strategy that includes the necessary managerial and operational capabilities enables this required access to the right information for manufacturing companies. Second, the chapter also supports previous research by reaffirming that human expertise, collaboration, technical ability, and innovation are crucial digital capabilities for real-​time activity. Previous studies confirmed the important role played by human capabilities (Khin & Ho, 2019; Lenka et al., 2017), collaboration capabilities (De Oliveira et al., 2019; Lenka et al., 2017), technical capabilities (De Oliveira et al., 2019; Khin & Ho, 2019; Lenka et al., 2017; Parida et al., 2015), and innovation capabilities (Khin & Ho, 2019; Parida et al., 2015) in forming digital capabilities. Furthermore, the results showed the statistically significant effect of human and collaboration capabilities, two of the four digital capabilities, on a company’s real-​time activities. Real-​time activities transform the working environment (Ramaswamy & Ozcan, 2018). In this regard, employees, as an important element in manufacturing companies (Arendt, 2008; Bouncken et al., 2019), should develop their capabilities, including digital skills and mindsets, for a real-​ time system environment. Additionally, collaboration plays an important role in successful real-​time operations such as offering digitalized product service systems (Lerch & Gotsch, 2015). In this regard, manufacturing companies can benefit and learn from each other by sharing their experiences. 3.4.2  Managerial implications This chapter gives evidence for managers of SMEs in the manufacturing sector to help them understand how important a sound digital business strategy and a strong digital capabilities are in establishing and supporting real-​time activities. Because of its statistically significant level of importance to real-​time activity, managers of manufacturing companies should work to make their digital business strategy more compatible with achieving real-​time operation. Furthermore, this study verified how important human and collaboration capabilities are to successfully implementing and sustaining real-​time activities. Therefore, SME managers should ensure that these digital capabilities are present as well.

Implementing real-time activities  39 3.4.3  Limitations and further research This research was conducted in single country and analyzed based on a survey of 116 manufacturing companies, so the results come with some limitations. The results also present research opportunities. The limitations include a possible lack of reliability and validity. This limitation has been addressed by carrying out a different statistical test at every step, from data collection to data interpretation. The main opportunity is the possibility for other researchers to further develop this research in multiple countries with multiple respondents. Also, because the cross-​sectional nature of the data might limit visibility to issues that develop gradually over time, future studies could further develop this research by collecting longitudinal data and conducting in-​depth research on other capabilities influential to real-​time activity.

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4  Real-​time simulation strategies Implications for operational excellence and sustainability performance Minna Saunila, Mina Nasiri and Juhani Ukko

4.1  Introduction A real-​time simulation model is defined as “a computer model of a physical system that can execute at the same rate as actual time” (Lee et al., 2004, p. 1441). Therefore, real-​time simulation models imitate a primary system (machinery, etc.) by emulating its behaviors (Lee et al., 2004). To realize this similarity, it is necessary for the physical system to have some characteristics such as programmability, addressability, sensibility, communicability, associability, and the ability to memorize (cf., Yoo, 2010; Yoo et al., 2010). These characteristics make it possible to build a higher level of sustainability value compared to traditional production, because they demonstrate high levels of data integration. It is necessary, therefore, to study the application of real-​time simulation in industrial production. In response, Chen et al. (2012, 2015) have presented several important sustainability subthemes and indicators for industrial production that may be tackled by utilizing real-​time simulation.They report that the economic aspects of sustainability performance include energy use, material consumption, waste management, profitability, and manufacturing costs. The social aspects include working conditions, the work’s impact on long-​term worker health, employee turnover, the proportion of permanent employees, and employee empowerment. Finally, the environmental aspects include its positive impacts on climate change, sources of energy, water quality through radiation heat transfer, water quality through solid waste, and water and soil through acidification. (Chen et al., 2012, 2015). On the other hand, many researchers argue that to get the best out of digital technology, digitalization must be a strategic issue (Hess et al., 2016; Matt et al., 2015; Ukko et al., 2019). For example, Hess et al. (2016) present that the alignment and coordination of companies’ many strategies in the light of digital transformation have led some researchers to argue for a digital business strategy that combines IT with business strategy. However, this kind of strategy usually consists of a company’s vision for future digital business models while ignoring guidelines on the actual transformational steps (Hess et al., 2016). Another stream of researchers highlight a stand-​alone digital transformation strategy that signposts the way toward digital transformation and guides managers through

Real-time simulation strategies  43 the transformation process resulting from the integration and use of digital technologies (Hess et al., 2016; Matt et al., 2015). These implications may also be relevant in the context of real-​time simulation. Consequently, it is essential to determine if real-​time simulation can be considered a digital transformation strategy or whether its elements (programmability, addressability, sensibility, communicability, associability, and the ability to memorize) can be considered actual transformational steps. In other words, does the explicit presence and integration of these elements in the company’s real-​time simulation strategy positively affect operational excellence and sustainability performance? Therefore, this chapter empirically analyzes the real-​time simulation strategies of companies. It makes use of empirical data from small and medium-​ sized enterprises (SMEs) in the manufacturing sector to test the relationship between the strategic-​level integration of real-​time simulation models and sustainability performance. In so doing, the chapter develops a new way of characterizing real-​time simulation based on the different tiers of data integration, and of defining different real-​time simulation strategies. Each of the identified strategies is characterized by a different tier of data integration, representing the degree of integration of the simulation model into company production operations. The evidence showed that performance enhancement was strongly connected to the degree of data integration within production. The chapter proceeds as follows. First, it presents prior literature on the contribution of real-​time simulation to sustainable development. Next, the chapter reveals the research methodologies and results from 116 manufacturing companies. Finally, some implications of these findings for manufacturing and operations management research and practice are presented.

4.2  Real-​time simulation for sustainability 4.2.1  Characteristics of real-​time simulation models A real-​time simulation model is “a computer model of a physical system that can execute at the same rate as actual time” (Lee et al., 2004, p. 1441). In other words, real-​time simulation models simulate, in real time, the behaviors of the systems being modeled (Lee et al., 2004). To make this similarity, it is necessary for the physical system to have some characteristics such as programmability, addressability, sensibility, communicability, associability, and the ability to memorize (cf., Kallinikos et al., 2013; Yoo, 2010). All these characteristics require a high level of data integration. Embedded software in a physical system enables it with programmable characteristics so it can accept new sets of logic to adapt functions and behavior. Standardized protocols such as IP addresses enable addressability of physical systems, so they can individually reply to messages sent to a group of similar devices. In combination with embedded software, sensors give sensibility to the physical system enabling it to monitor and react to different situations.

44  Minna Saunila et al. In transportation, for example, Google Maps can be considered a simulation model that, with the support of embedded GPS chips in mobile phones, can recognize a person’s location and propose the shortest, safest, and least congested routes. Combining a communication network with physical-​system addressability makes the physical system communicable, enabling it to send and receive messages. Including internal and external memory devices gives a physical system the ability to memorize, so the simulated system can record and store generated, sensed, and communicated information.Tags, keywords, or affiliation patterns can be set up as enablers to give the physical system associability so it can be connected and recognized by other devices, places, and persons based on specific shared features (Kallinikos et al., 2013;Yoo, 2010). 4.2.2 The concept of sustainability performance Brundtland et al. (1987) have defined sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. From a company perspective, this means considering the level of substitutability of different forms of capital such as manufactured, natural, human, and social (Figge & Hahn, 2004). Managing sustainability performance necessitates linking a company’s management of environmental and social issues with its business strategy and processes (Schaltegger & Wagner, 2006). Therefore, sustainability performance for companies will be determined by how well the company can carry out its daily activities while securing its economic, environmental, and social performance (Figge & Hahn, 2004; Hahn et al., 2007). To gain sustainability performance, companies need to pay attention to cost awareness, quality, and flexibility. According to De Ron (1998), this requires continuous improvement. Continuous improvement in efficiency, quality, and flexibility makes it possible for companies to attain sustainable operations and production (De Ron, 1998). Also, Adams et al. (2016) conclude that both operational optimization and organizational transformation must be taken into account to develop sustainability-​oriented practices and processes. Operational optimization is doing the same things better in terms of compliance and efficiency. Organizational transformation involves doing new things in terms of novel products, services, or business models. Therefore, the current study applies the concept of operational excellence to describe a company’s operational performance and renewal capability that contributes to its sustainable value creation.

4.3  Empirical examination of real-​time simulation strategies 4.3.1  Data collection Data were collected from a survey of top SME managers in the manufacturing sector. To assess the six elements of real-​time simulation models, survey respondents were requested to express the degree to which they agreed with

Real-time simulation strategies  45 the consequent items using a 7-​point Likert scale (1 = strongly disagree, 2 = disagree, 3 = slightly disagree, 4 = neither agree nor disagree, 5 = slightly agree, 6 = agree, 7 = strongly agree). The items were as follows: • • • • • •

In our company, all the devices are programmable. In our company, all the devices can be uniquely identified. In our company, all the devices are aware of the respond to changes in their environment. In our company, all the devices can send and receive messages. In our company, all the devices can record and store all information. In our company, all the devices can be identified with other devices, places, or people.

Items included in the survey were identified from prior literature and adapted as required by the researchers. Sustainability performance was assessed by asking respondents to evaluate the success of their company over the last three years with respect to three dimensions of sustainability: financial, social, and environmental. In addition, operational excellence was assessed by asking respondents to indicate their company’s operational performance and renewal capability over the last three years. The response scale for the items of sustainability performance and operational excellence ranged from 1 to 4 (1 = weak, 2 = satisfactory, 3 = good, 4 = excellent). For classification purposes, the survey included items related to company size (number of employees and revenue), maturity (years established), and type of business (business-​to-​business or business-​to-​consumer). The survey received 116 responses. To evaluate non-​response bias, responses to all study items were compared for early-​wave versus late-​wave respondents. According to Armstrong and Overton (1977), late-​wave respondents can be used as representatives for non-​respondents. There were no significant differences found between the early-​wave and late-​wave respondents, which suggest that non-​response bias are not likely to exist in the data. Responding companies included raw material, component, and final product manufacturers in different industries such as forest, metal, food, plastic, and machinery. Responding companies ranged in size from 49 or fewer employees (64% of sample) to over 50 (36% of sample). Median company size was 31 employees. Only 9% of the responding companies had been in operation less than ten years. The majority of the companies (65%) had operated over 10 but less than 50 years, whereas the remaining 27% of the companies had operated more than 50 years. Median company age was 34 years. The majority of the surveyed companies operate in business-​to-​business markets (93% of sample), and only 7% operate in business-​to-​consumer markets. 4.3.2  Cluster analysis results To identify possible strategies for utilizing real-​time simulation in the context of industrial production, cluster analysis was employed to group the companies

46  Minna Saunila et al. Table 4.1 Cluster analysis results Elements of real-​time Simulation

Mean Overall (n = 116)

Cluster 3 (n = 32)

Cluster 1 (n = 33)

Cluster 2 (n = 51)

Programmability Addressability Sensibility Communicability Associability The ability to memorize

3.39 4.15 3.05 3.03 3.08 3.15

5.66 5.53 4.94 5.50 5.34 5.41

3.82 5.15 3.00 2.79 3.06 3.03

1.75 2.65 1.94 1.67 1.69 1.78

into homogenous categories. The clustering was performed based on the tiers of data integration, that is, specifically how programmable, addressable, sensible, communicable, associable, and memorizable the companies’ real-​time simulation models are. Cluster analysis refers to a method of classifying objects with similar attributes into groups (Hair et al., 1998). In this study, the K-​means clustering method was used, because it is suitable for grouping based on conceptual issues. K-​means cluster analysis was used to divide companies into groups based on the tier of data integration within real-​time simulation models. Responses to the items on the six elements of real-​time simulation were used to perform the clustering, i.e., to sort the 116 cases into the three different real-​time simulation strategies (Table 4.1). For example, responding companies that were in the top level of data integration within the real-​time simulation model were classified as companies that follow a “data wisdom” strategy. Their real-​time simulation models were highly programmable, addressable, sensible, communicable, associable, and memorizable. Companies who reported high integration levels in programmability and addressability were categorized as following an “in the game” strategy. However, the level of data integration was below average in terms of sensibility, communicability, associability, and the ability to memorize. Finally, companies that reported low levels of integration in all the elements of real-​ time simulation models (programmability, addressability, sensibility, communicability, associability, and the ability to memorize) were labeled as companies that follow a “bystander” strategy. 4.3.3  Characteristics of the real-​time simulation strategies The three tiers of data integration representing “data wisdom”, “in the game”, and “bystander” strategies were tested using analysis of variance to identify significant differences between their levels of improvement for sustainability performance and operational excellence.Table 4.2 shows there were several significant differences (p < 0.1) between the three different real-​time simulation strategies. Further, Table 4.3 shows the descriptions of the companies following each strategy. The information provided in these two tables is used to describe the characters of the companies following each strategy.

Real-time simulation strategies  47 Table 4.2 The means of the sustainable value dimensions in each cluster Performance dimensions

Operational excellence Renewal Operational performance Sustainability performance Financial sustainability Social sustainability Environmental sustainability

Mean Overall (n = 116)

Cluster 3 (n = 32)

Cluster 1 (n = 33)

Cluster 2 (n = 51)

Sig.

2.67 2.79

2.94 2.97

2.78 2.84

2.43 2.67

0.005* 0.089*

2.71 2.93 2.84

3.03 3.00 2.84

2.63 3.03 2.88

2.59 2.84 2.84

0.080* 0.352 0.967

*p ≤ 0.1

4.3.3.1 The “data wisdom” strategy Of the companies surveyed, 27.6% carry out the “data wisdom” strategy. The companies following this strategy with the highest levels of data integration outperform other companies in terms of operational excellence (both renewal capability and operational performance) and financials, one of the three dimensions of sustainability. A similar statistically significant difference in comparison to other strategies was not found with respect to the social and environmental dimensions. This cluster comprises mostly small companies and young companies that have been in operation for less than 30 years. 4.3.3.2 The “in the game” strategy The “in the game” strategy is followed by 28.4% of the companies surveyed. Companies following this strategy also performed above average in terms of operational excellence (both renewal capability and operational performance). However, companies following this strategy did not outperform in terms of the financial, social, and environmental dimensions of sustainability performance. Medium-​sized companies are more represented in this group. A large portion of them have been in operation more than 30 years, and the companies are more mature than the average surveyed. 4.3.3.3  The “bystander” strategy The remaining 44% of the surveyed companies embrace the “bystander” strategy. These represent the lowest level of data integration, and they perform worse than average considering all the studied performance dimensions. Medium-​sized companies are also more represented in this group. Having been in operation more than 30 years, the “bystander” companies are more mature than the average surveyed company.

48  Minna Saunila et al. Table 4.3 Descriptions of the clusters based on size, maturity, and type of business No of employees

All No

%

Small Medium Missing

62 54 0 116

53.45 46.55 0.00 100.00

Revenue

No

%

Small Medium Missing

67 49 0 116

57.76 42.24 0.00 100.00

Maturity

No

%

below 30 years above 30 years Missing

42 74 0 116

36.21 63.79 0.00 100.00

Type of business

No

%

B2B B2C Missing

108 8 0 116

93.10 6.90 0.00 100.00

Cluster 3 (n = 32)

Cluster 1 (n = 33)

Cluster 2 (n = 51)

No

No

No

19 12 1 32 No 18 14 0 32 No 16 16 0 32 No 27 4 1 32

% 59.37 37.50 3.13 100.00 % 56.25 43.75 0.00 100.00 % 50.00 50.00 0.00 100.00 % 84.37 12.50 3.13 100.00

17 16 0 33 No 19 14 0 33 No 10 23 0 33 No 30 3 0 33

% 51.52 48.48 0.00 100.00 % 57.58 42.42 0.00 100.00 % 30.30 69.70 0.00 100.00 % 90.91 9.09 0.00 100.00

26 24 1 51 No 30 21 0 51 No 16 35 0 51 No 50 1 0 51

% 50.98 47.06 1.96 100.00 % 58.82 41.18 0.00 100.00 % 31.37 68.63 0.00 100.00 % 98.04 1.96 0.00 100.00

4.4  Conclusions: sustainable strategies for real-​time simulation This chapter empirically analyzes the strategies used by 116 manufacturing companies to employ real-​time simulation. It takes empirical data from SMEs in the manufacturing sector and tests the relationship between strategic-​level integration of real-​time simulation models and sustainability performance. In doing so, a new way of characterizing real-​time simulation based on the different tiers of data integration is developed, thereby defining different real-​time simulation strategies.The main contributions of the chapter can be summarized as follows. 4.4.1 Theoretical implications First, the analysis shows that it is possible to define real-​time simulation strategies that are characterized by different tiers of data integration. The most advanced strategies include the adoption of real-​time simulation models that

Real-time simulation strategies  49 are highly programmable, addressable, sensible, communicable, associable, and memorizable. Second, the comparison reveals that in terms of renewal capability, operational performance, and financial sustainability, real-​time simulation strategies with the lowest levels of data integration also demonstrate the lowest levels of operational excellence. From this, it can be concluded that an advanced real-​time simulation strategy with a high level of data integration improves the performance of a company. Third, small and young companies seem to be more advanced than larger and more mature companies in their approach to adopting advanced real-​time simulation strategies. 4.4.2  Managerial implications The following suggestions are offered for managers implementing real-​time simulation strategies. In terms of their attitudinal and demographic properties, SMEs fall into three categories. Implementing a comprehensive real-​time simulation strategy such as “data wisdom” with the highest tier of data integration is not essential for all companies. However, it serves as an example for companies willing to increase the level of data integration in their production operations. Furthermore, managers can benefit from an increased understanding of the benefits of real-​time simulation when considered as a strategic issue. Adopting real-​time simulation to achieve operational excellence is justified, but it can also more broadly provide sustainability value. 4.4.3  Limitations and further research directions First, the survey and the data used in this analysis cover only one country. More research will be needed to validate the results for other regions. Second, self-​ reported measures were used to establish the characteristics of real-​time simulation models and company performance. This is a practical approach that is widely used in collecting data, but it may come with some problems in terms of validity and reliability. In research terms, the validity and reliability were achieved by following an exact procedure to carry out the statistical analysis, from data collection to interpretation. Therefore, in terms of the research, the results can be considered valid and reliable. This study provides some avenues for future research. First, as this study has used quantitative data, it needs to be accompanied by qualitative and longitudinal research providing insights into SME real-​time simulation strategies. In-​depth research is required to study the additional characteristics of real-​time simulation to assist in the realization of sustainable strategies. Second, it is not clear whether or not or to what extent the characteristics of real-​time simulation models correlate with different usages (learning, control, etc.).This could be a fruitful avenue for future study. This research suggests a positive relationship between real-​time simulation and sustainability performance.Therefore, more in-​depth research will be needed on the real-​time simulation-​performance relationship accounting for the mediating effect of company-​level features and processes.

50  Minna Saunila et al.

References Adams, R., Jeanrenaud, S., Bessant, J., Denyer, D., & Overy, P. (2016). Sustainability-​ oriented innovation: A systematic review. International Journal of Management Reviews, 18(2), 180–​205. Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–​402. Brundtland, G. H., Khalid, M., Agnelli, S., Al-​Athel, S., & Chidzero, B. (1987). Our common future. New York: United Nations, 8. Chen, D., Heyer, S., Ibbotson, S., Salonitis, K., Steingrímsson, J. G., & Thiede, S. (2015). Direct digital manufacturing: Definition, evolution, and sustainability implications. Journal of Cleaner Production, 107, 615–​625. Chen, D., Heyer, S., Seliger, G., & Kjellberg, T. (2012). Integrating sustainability within the factory planning process. CIRP Annals, 61(1), 463–​466. De Ron, A. J. (1998). Sustainable production: the ultimate result of a continuous improvement. International Journal of Production Economics, 56, 99–​110. Figge, F., & Hahn,T. (2004). Sustainable value added—​measuring corporate contributions to sustainability beyond eco-​efficiency. Ecological Economics, 48(2), 173–​187. Hahn,T., Figge, F., & Barkemeyer, R. (2007). Sustainable value creation among companies in the manufacturing sector. International Journal of Environmental Technology and Management, 7(5–​6), 496–​512. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (1998). Multivariate data analysis. Upper Saddle River, NJ: Prentice Hall. Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2), 123–​139. Kallinikos, J., Aaltonen, A., & Marton, A. (2013). The ambivalent ontology of digital artifacts. MIS Quarterly, 37(2), 357–​370. Lee, M. G., Lee, S., & Kim, K. H. (2004). Implementation of a TMO-​ structured real-​ time airplane-​ landing simulator on a distributed computing environment. Software: Practice and Experience, 34(15), 1441–​1462. Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & Information Systems Engineering, 57(5), 339–​343. Schaltegger, S., & Wagner, M. (2006). Integrative management of sustainability performance, measurement and reporting. International Journal of Accounting, Auditing and Performance Evaluation, 3(1), 1–​19. Ukko, J., Nasiri, M., Saunila, M., & Rantala,T. (2019). Sustainability strategy as a moderator in the relationship between digital business strategy and financial performance. Journal of Cleaner Production, 236, 117626. Yoo, Y. (2010). Computing in everyday life: A call for research on experiential computing. MIS Quarterly, 34(2), 213–​231. Yoo, Y., Henfridsson, O., & Lyytinen, K. (2010). Research commentary—​the new organizing logic of digital innovation: An agenda for information systems research. Information Systems Research, 21(4), 724–​735.

5  Selling digital twins in business-​to-​business markets Tuija Rantala, Kirsi Kokkonen and Lea Hannola

5.1  Introduction Recently, simulation-​ based product development has received significant attention in scientific publications (Alaei et al., 2018). That same trend is recognizable with the deployment and popularity of the digital twin, which is designed, e.g., to accommodate the need for cheaper and repeatable product development (Alaei et al., 2018).The levels of maturity in digital-​twin use, the solutions used, and the definition what is understood to belong to the digital twin concept still vary a lot across industries and among different companies (Lee et al., 2013; Tao et al., 2019). In addition to rapid development of technological solutions enabled by digital transformation, the growing emphasis in many industrial fields on the service-​based business (e.g., Kohtamäki et al., 2018) demands that companies develop an ability to work with their customers to co-​create value (Marcos-​Cuevas et al., 2016). Therefore, to develop new data-​based innovations such as digital-​twin services, companies must acquire a new understanding of their customers and their customers’ businesses. The digital twin, based on digital technologies, is at the core of digitalization. The “digital twin”, “data utilization”, and “value-​based sales” are currently the subject of much discussion among academics and practitioners (e.g., Donoghue et al., 2019; Gandomi and Heider, 2015; Manyika et al., 2011; Vargo & Lusch, 2016). However, thus far, there is little formal research into these important topics. Previous studies do not emphasize selling digital twins in business-​ to-​ business (B2B) markets, where it is crucial to precisely address customer need. This chapter aims to fill the gap by combining the literature of B2B sales with new information coming from a study on digital twinning in a manufacturing context. In addition, this chapter of the book highlights the importance of research related to sales and the establishment of business value through better customer understanding. The value that customers gain from digital solutions is different than the value gained from their purchase of physical products. This difference affects sales processes. Moreover, value creation for customer business is a key focus of

52  Tuija Rantala et al. this book. By applying a sales viewpoint to digital twinning, new insights can be provided into the value elements of the differing levels of digital-​twin solution implementation and about how best to promote the sale of these digital-​twin solutions. In fact, how to promote the sale of digital twins is the primary focus of this chapter. In addition, the chapter reviews the level of understanding needed to successfully implement digital-​twin solutions, how much must be known about the customers and their businesses to sell digital twins, and what kind of challenges can emerge when selling digital twins today and what challenges will emerge as digital twins advance to satisfy the future wants and needs of the case companies surveyed in this study. This chapter presents a model that illustrates the level of customer understanding and the level of understanding of data-​based solutions required from the viewpoint of selling digital twins in B2B markets. In the empirical study, the model was applied to six participating case companies, each working with a different digital-​twin solution. Practical examples that were revealed during the qualitative interviews are presented. The remainder of the chapter is structured as follows. The next section reviews related research on digital twins, especially in the manufacturing context, and digital twin sales in B2B markets. Further, the research methodology and the results of the empirical study are presented. In the final section, research findings are discussed and recommendations and conclusions are offered.

5.2 Theoretical background 5.2.1  Digital twins in manufacturing industry The digital twin terminology was first introduced by Grieves in 2003. Since then, there have been several digital-​ twin definitions. For example, Chen (2017) defines the digital twin as a computerized model of a physical device or system representing all functional features and links with the working elements. According to Madni et al. (2019), a digital twin is a virtual illustration of a physical system that is constantly updated with performance, maintenance, and health status data throughout the lifecycle of a physical system. The digital twin provides several opportunities for new product and service innovations (Tao et al., 2019; Zheng et al., 2018). In manufacturing industries, several digital-​twin-​enabled services have been developed related, e.g., to fault diagnosis, real-​time state monitoring, predictive maintenance, performance analysis, user management, user behavior analysis, and product-​or plant-​ level virtual maintenance and operation (Kritzinger et al., 2018;Tao et al., 2019; Zheng et al., 2018). The use of the digital twin enables virtual product and process planning, and companies are increasingly turning their attention on how and in what ways the communication, synergy, and coevolution between a physical product and its digital twin can lead to more innovative product design processes (Kritzinger et al., 2018; Tao et al., 2019). Based on recent literature, digital twins are most commonly used in production planning and

Selling digital twins  53 control and condition-​based maintenance. Digital-​twin integration throughout the entire product lifecycle or production system is still rare (Kritzinger et al., 2018; Zheng et al., 2018). The opportunities for the collection and utilization of IoT data have remarkably changed manufacturing company product-​service systems and boosted their transformation toward becoming service-​based businesses (Rymaszewska et al., 2017). The increasing amount of available data, gathered from machines and manufacturing processes, offers new service innovations and business opportunities for several companies within the entire manufacturing industry (Olaf & Hanser, 2019), e.g., in planning, project management, simulation modeling, visualization, control systems, and automation services. However, these service business opportunities enabled by digital twins at the ecosystem level still seem to be scarcely researched (e.g., Zheng et al., 2018). An even less researched topic is the selling of digital twins, the focus of this chapter. 5.2.2  Selling data-​based solutions in business-​to-​business markets Utilizing data in business is still relatively new for many companies. It is generally not the result of strategic planning or management. In addition, digital business models in several companies are unstructured (Ulander et al., 2019). Therefore, sales forces are challenged by data-​based innovations, which can be described as opportunities that arise from business model reinvention (new data-​based services, for example). Furthermore, there may be a radical shift in selling practices, the object for sales, and earning logics. Sales forces may face several challenges when moving from products to value-​added services, rethinking value propositions, reordering value chains, reconfiguring value delivery models, or even when moving to different markets (Westerman et al., 2014). Unlike traditional transactions, selling data-​based solutions is in many cases equivalent to selling value.Value-​based sales is more about the offering’s potential implications for the customer’s business than about the customers’ actual expressed needs (Terho et al., 2012). Meanwhile, the developments of emerging technologies may force an increasing demand for even more effective salespeople to carry out the value-​based selling process (Marshall et al., 2012). Furthermore, a face-​to-​face meeting, for example, may be difficult to arrange if the salesperson’s capability to influence the customer’s buying process is challenged (Adamson et al., 2012). When selling complex data-​based solutions, such as digital twins, it is important to deeply understand the customer’s business, as well as the solution being sold. With respect to digital twins, however, the B2B salesperson’s perspective has not been properly considered in previous published research. Rantala et al. (2020) presented a model for understanding data-​based value sales and the knowledge needed in relation to the customer, data-​based solutions, and technologies. To be effective, the salesperson must better understand customer processes, the customer’s business, and what customer value a data-​based solution can offer. The salesperson must understand the data-​based solution

54  Tuija Rantala et al. on three levels: light, moderate, and deep (Rantala et al., 2020). The model introduced by Rantala et al. (2020) was selected as a basis for this work, because it brings new ideas on how to sell data-​based solutions. The model brings together in a fresh way two important aspects: (1) the level of understanding needed about the customer’s business and (2) the level of understanding needed regarding the data-​based solution.

5.3  Research methodology In this chapter, the focus is on selling data-​based solutions and innovations based on digital twins with the main research question of the study: How can the sales of digital twin be promoted? Two sub-​questions of the study are as follows: • What kind of customer and digital twin understanding is needed when selling digital twins in B2B markets? • What kind of challenges emerge when selling digital twins currently or in the next level of transformation? The research methodology employed in this chapter is a qualitative case study. The case study was chosen as a method because of its suitability for situations that include complex and multiple variables and processes (Yin, 2014). According to Yin, case studies can be used as empirical investigations, which examine a present phenomenon, e.g., the concept of digital twin, within its real-​life contexts (in companies), especially when the boundaries between phenomenon and real-​life contexts are not clearly apparent. In this study, six cases were selected where the concept of digital twins from the sales point of view was examined. The qualitative data were collected in January and February of 2020 from 16 semi-​ structured theme interviews held with representatives from 6 different companies from the areas of intelligent machine control systems, software solutions, real-​time simulation, and machine engineering (Table 5.1). The case companies all operate in B2B markets.They were selected, because they are actively developing solutions related to digital twins. The interviews were recorded and transcribed. The duration of a typical interview was 1–​ 1.5 hours, and each involved 1–​4 interviewers. The semi-​structured theme interviews were chosen as the main source of empirical material, because the study is partly explorative in nature, and the meanings of concepts needed to be negotiated with the interviewees. The interviews went beyond customer aspects of the digital twins to cover a broad range of themes, such as current utilization, advantages, and challenges of the digital twins. Content analysis was the approach to analyzing the data. Emerging main themes were grouped and analyzed over several researcher meetings. Then, interview data were studied from the perspectives of the model of Rantala et al. (2020).

Selling digital twins  55 Table 5.1 Interviewed companies, their main products and services, number of interviewees, and interview dates Company

Main products and services

Number of interviewees

Interview date

A

Product data sharing software and solutions for product lifecycle management collaboration Solutions for digitalizing earthmoving jobsites and robotizing earthmoving machinery Data visualization and rationalizing the operative hands-​on work with different kinds of digital-​twin solutions Tools and tooling systems for industrial metal cutting, stainless steels Technologies and lifecycle solutions for the marine and energy markets Software for digital twins and real-​time simulations

1

February 2020

4

January 2020

4

January 2020

4

January 2020

1

February 2020

2

February 2020

B C D E F

5.4  Results 5.4.1  Model illustrating the level of customer and digital-​twin understanding needed from the sales perspective The paper presents a model (Figure 5.1) that illustrates, from the viewpoint of digital twinning, the level of understanding needed of the customer and the data-​based solution. From the salesperson viewpoint, it means the need to increase simultaneously one’s own understanding of a customer’s sub-​ processes, processes, and business as well as understanding which value-​adding data-​based solution will provide the best result. The level of needed data-​ based-​solution understanding can be light, moderate, or deep (Rantala et al., 2020). The model of Rantala et al. (2020) is related to data-​based solutions in general. It is not specific to digital twinning. Rather, it is an example of data-​based services for optimizing an industrial company’s operations. In this chapter, practical examples are presented to illustrate what understanding of the digital twin and the customer business is required to effectively sell data-​ based solutions. Selling “light” digital-​twin solutions to customers is the first level in the model. At this level, the digital-​twin solutions are characterized as “light”. They are usually light simulations of the product that are used as supporting tools for the sales process and are not delivered to the customer or updated afterwards. At this level, the salesperson’s existing understanding about the

Level of needed customer understanding

•Real-time data with machine learning, etc. and recommendations for developing customer’s (and other actors’ businesses) in the future •Full digital version of real, e.g., factory area or construction site •Data are provider/utilized/integrated in ecosystem value chain level .

Customer’s business

Customer’s process

Customer’s sub process

•Light mock-up, e.g., powerpoint, which do not have link to reality •Tool that is used by the seller in sales phase, e.g., light simulation of equipment •Digital twin is not sold/delivered to the customer

Light

•Digital twin that is utilized in certain part of the process equipment •Data are utilized by the provider company or user company – no life time data collection •Improving the customer’s process productivity

•Real-time data and implications for, e.g. , predictive condition-based maintenance planning of production line •E.g. Remote monitoring of system, remote operating services •Data are utilized by several actors .

•Monitoring certain part of the process or part of the equipment in closed environment •Data are used by the provider company in their R&D

•Remote monitoring failures •Real-time data for predictive condition-based maintenance planning •Equipment real-time monitoring for preventing unplanned shutdown •Data are utilized by certain persons (do not include precise implications from data)

Moderate

Deep

Level of digital twin complexity (diversity, real-me, life cycle, etc.) increases

Level of ecosystem complexity increases

56  Tuija Rantala et al.

Level of needed digital-twin understanding

Figure 5.1 Selling digital twins in B2B markets showing the levels of customer and digital-​twin understanding required as well as ecosystem complexity

customer equipment and the development of the product with digital features is usually enough. When moving to the next level, selling “moderate” digital-​twin solutions to customers (middle column of the model), the complexity of the digital-​twin solution increases. Digital twins at this level can be used in the customer’s process or sub-​process, and they are usually utilized for providing monitoring data on the process or equipment, e.g., for improving efficiency and predicting maintenance needs. At this level, salespersons need a deeper understanding of the data utilization and working principles of the digital-​twin solution as well as a better understanding of customer systems. At the customer sub-​process level, the sales team’s existing knowledge of equipment and process is usually enough. However, when moving up to the customer process level, salespeople need a broader knowledge of customer production lines and processes. Selling “deep” digital-​twin solutions to customers (right column of the model) requires real-​time data from multiple sources for more extensive use in customer production lines, factory areas, or construction sites. The wider the utilization area of the digital twin, the more actors are involved. At the highest level (top right in the figure), data are utilized in value chains or ecosystems, and the customer business can be supported by advanced and integrated digital-​ twin solutions that probably involve machine learning. At this level, salespersons need knowledge of real-​time data management and advanced technological tools, such as artificial intelligence. Moreover, they must understand the value of data to the customers’ business, the complexity of the related ecosystem, and the role of data. This jump in complexity may demand new value propositions,

Selling digital twins  57

Customer’s business

Company C

Customer’s process

Company Compan F

Company B

Company D

Customer’s sub process

Company E

Level of ecosystem complexity increases

Level of needed customer understanding

Company A Light

Moderate

Deep

Level of digital twin complexity (diversity, real-me, life cycle etc.) increases

Level of needed digital twin understanding

Figure 5.2 Selling digital twins in B2B markets showing the levels of customer and digital-​twin understanding required as well as ecosystem complexity from the perspectives of the case companies both now and in the future

value co-​creation with the customers, and even new business models and earning logics. Selling digital-​twin innovations at this level demands a deep understanding of the customer, other ecosystem actors, and their businesses. The interviewed companies are in different phases of the model presented in Figure 5.1. The interviewed companies have been positioned according to their current digital-​twin solution, as well as future development directions (Figure 5.2). The companies may have several digital twins, but in this study, the focus was on a particular digital-​twin solution envisioned for the future. In Figure 5.2, the interviewed companies (A through F) are positioned according to their current level of digital-​twin-​solution integration. Targeted development directions are marked with arrows. One company (C) has two development direction arrows. The dashed-​line arrow represents a more ambitious future scenario. 5.4.2  Challenges in selling digital twins According to the interview results, the major challenge is to identify customer value, i.e., what is real value for the customer, and then transform the innovations related to digital twins to provide that value from the customer perspective. For example, when selling data-​based solutions, the sales team must listen carefully to the customer and respond to the precise need. In general, selling services to a traditional product company may be a challenge. Selling services involving digital twinning may be even more difficult. To respond precisely to the customer need, sales must understand both the digital-​twin solution and the need,

58  Tuija Rantala et al. in detail, to transform the voice of the customer into the outputs of the digital twin. The sales team is facing more knowledgeable and demanding customers than ever before, and responding by offering the value the customer is seeking can be challenging. With respect to selling “light” digital-​twin solutions to customers, the interviewees identified several challenges that salespeople may face. At this stage, the challenges are associated with understanding digitalization, how it applies to customer need, and the benefits of digital twinning. For instance, traditional industry players may simply be resistant to change or an enterprise may have silos that inhibit progressive transformation. Or, customers may have doubts about the reliability of the proposed configuration of the digital-​twin solution. Moreover, there may be confusion within the sales team about what customers really need. Finally, challenges related to achieving and sustaining information may emerge. One interviewee (the CEO of company A) clarified the importance of having constant reliable information flow as follows: If the digital twin is disintegrated, it’s worth is zero. There might be challenges regarding responsibility ownership. For example, who is responsible for updating the digital twin configuration and the digital-​ twin information, etc. In addition, smaller companies may find it difficult to compete with larger companies such as Siemens. Companies with more advanced deep implementations of digital twinning may also make use of light versions. Light versions can be used in training or in sales to present the digital-​ twin concept before it is further developed with the customer. With respect to selling “moderate” digital-​ twin solutions to customers, interviewees identified several challenges related to the compatibility of platform, systems, and data. The customer platform may be old, compatibility may become a big issue, or the company infrastructure may struggle to keep up with technological changes.There might be significant sales challenges from customers working in traditional industries that are resistant to change. “No digi needed” or “big brother is controlling us” are examples of comments received from interviewees (companies B and C). Another challenge may be that a business may be strictly standardized, which could restrict utilization opportunities for digital-​twin solutions (company B). Lack of openness in sharing data, the incompatibility of systems, and data integration challenges from different sources are recognized as major challenges (companies B and C) when contemplating the implementation of the next level of digital twinning. Digital twins for a certain part of both equipment and process are less complex with respect to the level of diversity, real-​time capability, and lifecycle data. As one interviewee (Research Scientist in company B) stated: Open data transformation formats and common rules are needed.

Selling digital twins  59 Another challenge might be that the process lines or equipment offered may be so customer-​specific that the digital twin must be modified for each sales case (company D). With increases in digital-​twin complexity, sales personnel must learn new products and product features (company C). In addition, there may be lack of knowledge about technological aspects or customer needs (company D). For the next level of digital twins, for example, the sales team might present an agile factory of the future (company D). With respect to selling “deep” digital-​twin solutions to customers, interviewees recognized challenges related to complexity.When reaching out to the right top corner (Figure 5.2), the digital-​twin provider may face challenges related to understanding the meaning of real-​time data and find it difficult to make the meaning clear to the customers.The availability of real-​time data may be beneficial to the provider, but not yet of perceived benefit to the customer (company E). Moreover, the sales team may think that access by the customer to too much detailed information about its processes may be disruptive and result in an overly concerned customer (company E). Technical development is continuous, and customers are making more intelligent and more complex products and machines. The challenge is to stay on the crest of the wave and be able to provide customers the components they need and that they are able to model to their complex machinery (company F). As one interviewee (Technology Director in company F) stated: One of the challenges is that when customer models become more complex and heavier, how can they be solved in real-​time. At the next level, the provider may need to develop new business models, such as licensing models (monthly or project-​based usage of DT software), around the digital twin to make it easier to implement in a complex ecosystem (company F). Another challenge may be model complexity. As the digital-​twin model becomes more complex, achieving real-​time solutions will become more difficult (company F). The development of virtual models takes a lot of time and resources. The seller may use different versions of the models for different phases. For example, a simpler version may be sufficient for a concept phase (concept testing), but for testing final control systems a more advanced model may be needed to get relevant information from the model (company F).

5.5  Conclusions Based on the empirical results, the main challenges in selling “light” digital-​ twin solutions are often related to the adoption of digital technological solutions in general. Many manufacturing companies are still conservative, and the advantages of digital twins or how to assign responsibilities for their operation may be unclear. At this level, sales team efforts should promote the added value offered by digital twins. Using a digital twin, the physical product can be

60  Tuija Rantala et al. visualized in advance, which helps in understanding how it might be received and how it will operate in practice. At the “moderate” level, challenges are often related to the technological fit of the systems and interfaces or the customer’s outdated machine infrastructure that doesn’t necessarily support all the digital twin integrations. Existing digital-​twin solutions are often related to separate installations, but the target of companies at this level is often to extend the digital-​twin solutions to an increasing number of systems and users. For the sales force, it is essential to know the customer systems and understand the realistic possibilities for integration. For example, what installations and parts of the processes will benefit from digital twinning? Or, what is the effectiveness or cost savings in relation to existing solutions? There is no point in selling “too heavy” digital-​twin solutions that are not technologically or economically feasible. The value for customer comes mainly from the incremental improvement of its processes. Moreover, the value and business opportunities arising from the increasing amount of data may arouse interest in companies that are transitioning toward service-​based business. At the “deep” level, the challenges relate to the management of complex solutions and especially to managing the increasing amount of data. The practicalities and rules for data ownership and sharing are still underdeveloped, and companies may be cautious about open collaboration. At this level, customer companies are increasingly adopting service-​based business logics, and this development in product-​service offerings can cause changes to their value elements and, as a result, changes to their business models. Customer companies are increasingly interested in providing data-​based lifecycle services. This, of course, provides opportunities for digital twinning and raises question about how real-​time simulation and digital twins could be utilized across product/​ process/​factory lifecycles and who could utilize the data. The empirical results of this study support views from the existing literature (e.g., Kirtzinger et al., 2018; Zheng et al., 2018) and also from Chapter 2 of this book (“Identifying industrial needs for real-​time simulation and digital twins”) that digital-​twin integrations across the entire product lifecycle are still scarce. One reason for this may be that the digital twinning is a relatively new concept for many companies, and its integration has thus far concentrated mainly on the first steps of product lifecycles. From a technological perspective, incompatibility of systems and insufficient capacities are still slowing down development in many companies. As the models become more complex, they become heavy and more difficult to solve in real time. However, the expectation is that more complex digital twins that also provide real-​time data across the entire lifecycle will be coming in the near future. Moreover, data are supposed to be utilized by an increasing number of different actors in manufacturing ecosystems. This development is not easy to navigate, as it demands that companies adopt a new kind of openness about sharing their data and their expertise in analyzing and using this data. From

Selling digital twins  61 the sales perspective, being able to provide sustainable value for customers and selling complex digital-​twin solutions not only demand knowledge of the customer’s business, but also an understanding of their partners, ecosystems, and future business development targets. Empirical findings found in the literature regarding digital twins focus mainly on technical rather than business aspects.There are relatively few studies covering sales, B2B companies, and digital twins. This chapter gives practical viewpoints for selling data-​based solutions related to digital twins in B2B markets. The chapter also tests and broadens the model presented by Rantala et al. (2020) related to the level of needed customer understanding and data-​ based-​solution understanding associated with the sale of digital-​twin solutions. Based on these research results, the model of Rantala et al. (2020) conforms to the general theme of selling digital twins. However, the suitability of the model requires more testing with a larger set of case companies and industries to generalize the suitability. This chapter is intended to help practitioners to benchmark practices in other companies and to give feedback to managers for developing their B2B sales function successfully in practice. The study also helps researchers visualize how data can be applied for new innovations in a broader context including the digital twin, B2B sales, and management. In addition, this study increases customer understanding related to digital twins.

Acknowledgments The authors would like to thank all the parties behind the DIGIBUZZ (Toward Commercial Exploitation of the digital twins) project as well as Business Finland –​the Finnish innovation funding, trade, investment, and travel promotion organization –​for its support of this study.

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62  Tuija Rantala et al. Kritzinger,W., Karner, M.,Traar, G., Henjes, J., & Sihn,W. (2018). Digital twin in manufacturing: A categorical literature review and classification. IFAC PapersOnLine, 51(11), 1016–​1022. Lee, J., Lapira, E., Bagheri, B., & Kao, H. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38–​41. Madni, A., Madni, C., & Lucero, S. (2019). Leveraging digital twin technology in model-​ based systems engineering. Systems, 7(1), 7. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A.H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey & Company, McKinsey Global Institute, June 2011. Marcos-​Cuevas, J., Nätti, S., Palo, T., & Baumann, J. (2016). Value co-​creation practices and capabilities: Sustained purposeful engagement across B2B systems. Industrial Marketing Management. 56, 97–​107. Marshall, G.W., Moncrief,W.C., Rudd, J.M., & Lee, N.J. (2012). Revolution in Sales:The impact of social media and related technology on the selling environment. Journal of Personal Selling and Sales Management, 32(3), 349–​363. https://​doi.org/​10.2753/​ PSS0885-​3134320305. Olaf J.M., & Hanser E. (2019). Manufacturing in times of digital business and Industry 4.0 –​The industrial internet of things not only changes the world of manufacturing. In: Hloch, S., Klichová, D., Krolczyk, G., Chattopadhyaya, S., Ruppenthalová, L. (Eds.) Advances in manufacturing engineering and materials. Lecture Notes in Mechanical Engineering. Springer, Cham, 11–​17. Rantala, T., Apilo, T., Palomäki, K., & Valkokari, K. (2020). Selling data-​based value in business-​ to-​ business markets. Technology Innovation Management Review, 10(1), 45–​53. Rymaszewska, A., Helo, P., & Gunasekaran, A. (2017). IoT powered servitization of manufacturing –​an exploratory case study. International Journal of Production Economics, 192, 92–​105. Tao, F, Sui, F, Liu, A., Qi, Q., Zhang, M., Song, B. … Nee, A.Y.C. (2019). Digital twin-​ driven product design framework. International Journal of Production Research, 57(12), 3935–​3953. Terho, H., Haas, A., Eggert, A., & Ulaga, W. (2012). It’s almost like taking the sales out of selling’ –​towards conceptualizing value-​based selling in business markets. Industrial Marketing Management, 41(1), 174–​185. Ulander M., Ahomäki M., & Laukkanen J. (2019). The future of European companies in data economy. Sitra 2019. www.sitra.fi/​en/​publications/​the-​future-​of-​european-​ companies-​in-​data-​economy/​. Vargo, S.L., & Lusch, R.F. (2016). Institutions and axioms: An extension and update of service-​dominant logic. Journal of Academy of Marketing Science, 44, 5–​23. Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Boston, MA: Harvard Business School Publishing, 292. Yin R.K. (2014). Case study research: Design and methods (5th ed.). Thousand Oaks, CA: Sage Publications, 282. Zheng, P., Lin, T-​J., Chen, C-​H., & Xu, X. (2018). A systematic design approach for service innovation of smart product-​service systems. Journal of Cleaner Production, 201, 657–​667.

Part II

Game-​like virtual environments

6  Accelerating design processes using data-​driven models Emil Kurvinen, Iines Suninen, Grzegorz Orzechowski, Jin H. Choi, Jin-​Gyun Kim and Aki Mikkola

6.1  Introduction This chapter introduces data-​driven models in the framework of dynamic simulations and discusses their potential to provide additional value to original equipment manufacturers, end-​users, and other stakeholders in the value chain. A challenge in product processes and in the product development of high-​ technology products arises from gradual development through incremental innovation, i.e., the new product or process is typically based on an earlier generation product or process. Introducing radical changes could result in legislation difficulties and would make gaining official approvals more difficult. Proof of development and testing is usually a requirement for certification. For a physical product, obtaining this proof is expensive and takes time. An example would be crash testing. The complexity of heavy machinery and processes is constantly on the rise, because new technologies are continually integrated and interconnected. In addition, software solutions are being emphasized over hardware solutions, so developing a new machine is not just a job for mechanical engineers. Today, mastery over several disciplines and related standards is required to produce a new product and/​or service. In practice, increased complexity has led to situations in which numerous requirements and interactions must be simultaneously considered. Standards and quality requirements based on emerging technology approaches are becoming more pervasive. As a result, the successful introduction of a new product is becoming increasingly more complicated and expensive. In some scenarios, it has been estimated that the expenses associated with product development and other product processes make it difficult for new players to succeed. This in turn can result in a reduction in competition and work against worldwide economic growth. As the product has become more complex, validation and verification via measurement becomes more important. There are two possible ways to gain insight into a new product. In one, the actual product is instrumented with various sensors and measurements are taken. The benefit of this approach is

66  Emil Kurvinen et al. that the system behavior is real and can be used for proof that the system complies with standards. The drawbacks are the costs, in money and time, of the testing, and that the results are case specific. Because these drawbacks are less significant for a high-​volume product, testing is an appealing in mass production. In the second, simulation models are used to gain insight into product performance. For example, a simulation could examine the influence of various parameters on system behaviors. The benefit of this approach is that it makes it possible to explore different structures cost effectively even before the product is manufactured. The drawback is that to trust the results, both the model and the simulation must be carefully verified. In low volume and customized products, this approach is appealing, because prototyping is no longer required to verify performance. Both evaluation approaches can be applied equally in building the data models. In addition, a hybrid combination of measured and simulated data can be used. See, e.g., virtual sensors (in this book chapter) and Kalman filters. Development in the use of computational models and simulations is rapidly progressing. Product development has changed radically over recent decades as it has become more computationally intensive (Dede et al., 2014; Kurvinen, 2016; Pyrhönen et al., 2013; Uzhegov et al., 2016). The use of computational models and simulations has remarkably increased the efficiency of research and development from concept design through selection of details to market release. As explained in other chapters of this book, computational models can be successfully used in many product processes such as marketing, service business, and user training. In a practical design procedure, decisions must be made regarding a multitude of parameters at every stage of the design task. This is a challenge for the designer, because design decisions significantly influence overall design and performance. Also, because the design target is typically difficult to express explicitly, the use of numerical optimization methods may not help to realize a successful product or service. Due to this suboptimal exploration of design space topologies and parameters, obtained results may be unsatisfactory. Software has become more dominant in product development, but time-​to-​market demands have remained the same or have even been shortened. Because of this schedule constraint, software development must begin early on in the design process, even before the first prototype is built. To ensure the overall optimal fulfilment of needs and constraints associated with a product, it is crucial to develop alternative tools that can extensively support the design, use, and service of a product. To this end, this book chapter introduces a concept based on data-​driven models. In this book chapter, data-​ driven models are introduced in the framework of multibody system dynamics. Their application in product development, product use, and service businesses are discussed.

Accelerating design processes  67

6.2  Concept of a data-​driven model Data-​driven modeling is based on information and communications technologies. It is currently undergoing fast development. In data-​driven models, data are provided for use in computational intelligence and in machine learning (ML) methods, e.g., for system design or software design purposes. These methods feature a wide toolset that ranges from neural networks via Bayesian networks to random decision forests. Because the data represent a fundamental basis for decisions and conclusions, quality needs to be high and the amount of data should be sufficient. Simulation tools are often used in current R&D tasks, especially in high-​technology products, therefore simulation models provide a basis for virtual data collection. The benefit of virtual data collection is that radical changes can be studied and how parameters affect performance can be determined. Moreover, this information, which can include the effects on system behavior of faulty condition, can be stored. With the help of advanced simulation and sensing combined with communication technologies, it is possible to collect enormous amounts of data for use and to use this data to improve or optimize the product lifecycle. This offers new opportunities for companies to improve existing services and to develop new ones. In addition, advanced simulation, sensing, and communication enable and enhance the product’s capability to sense its operational environment and transfer that information to a central location for use in operational optimization. For example, a manufacturer of mobile machinery can collect operational records obtained from inboard electronics. These records can be used to optimize the service needs of existing products and minimize their energy consumption. They can also be used to examine safety and many other aspects in the development of future products (Alaei et al., 2019). In effect, new service innovations enable product-​focused companies such as the manufacturing industry to increase the share and stability of revenue and profit, improve customer relationships by meeting the customer demands more precisely, and increase lifecycle management performance. New innovations, such as data-​driven models, need a vast amount of data and the technology still needs further development. However, new innovations help companies to differentiate themselves from their competitors and strengthen their market position as solution providers. Product-​ focused companies can expand their business by offering, for example, environmentally aware products, which are capable of producing even more data that become useful to the market as a whole. This in turn improves the usability of high-​tech products and their lifecycle operation. One of the problems that arises with new types of services is that customers need to accept the collection of private data, which can be a difficult or sensitive topic for some. How best to handle the collection of private data is still an open issue. Although some companies have already successfully introduced data-​driven industrial services, most manufacturing companies still struggle with the actual

68  Emil Kurvinen et al. service innovation process of classical service development and need guidance and case examples before evaluating the potential benefits. In contrast to traditional industrial services such as maintenance and repair tasks, data-​driven services require advanced technology and new competencies such as extensive IT knowledge. However, the transformation of a business from being product-​and manufacturing-​centric to being more software-​focused requires innovation and new expertise. And all this must be integrated. Besides, the field of data use in services is new for most manufacturers and adds a high degree of complexity to the development of services, because various internal and external stakeholders (e.g., IT departments, legal stakeholders as well as other partners and customers) must be integrated into the process (Kampker et al., 2018). In the following sections, the data-​driven paradigm is explored in detail. 6.2.1  System modeling Data-​driven modeling has gained popularity due to an increase in computational performance and advances in statistical and ML methods (Montáns et al., 2019). It is applied across many science and engineering fields including mechanical engineering (Choi et al., 2020), industrial processing, fluid and particle dynamics (Sanchez-​Gonzalez et al., 2020), bioengineering, and chemistry. Especially, in mechanical engineering the data-​driven approach was adopted recently, as the availability of data and interest in complex systems and materials has emerged (Montáns et al., 2019). Based on prior knowledge assumptions, models can be classified as white box, grey box, or black box. In a white-​box model, the physics behind the process is known and the model is usually theoretically based. A black-​box model is structured based solely on system input and output data. A grey-​box model is partially theoretical and partially structured with data. The common use of a deep neural network (DNN) promotes black-​box modeling, because it is assumed that a DNN can accurately discover dependencies in complex data sets (Chollet, 2018). This approach is user-​friendly, because the modeling phase can be omitted. However, it results in models that are far from optimally efficient and with poor generalization properties. The nonoptimal efficiencies are the result of the large number of parameters that comprise DNNs.The grey-​box approach results in a more compact description. Nevertheless, a DNN can be optimized for a given application (by limiting the number of layers and neurons) and can take advantage of modern computational hardware by exploiting parallel execution. From a user perspective, the lack of generalization behind the data provided for black-​box DNN models may be problematic. In practice it means that the model that has learned a particular system can provide reasonable response only for that system and only for the range of inputs it was trained on. In many practical applications this is adequate, however, to build a versatile data-​driven model of a complex system, important design choices must be made at the very

Accelerating design processes  69 beginning. Engineers must choose what parameters must be explicitly modeled and what is the range of those parameters. To model a car, for example, the number of passengers, their weight, shock absorber stiffness, tire size, chassis mass, etc., must be chosen in advance. For models like a car, there are thousands of parameters to choose from. The more parameters selected, the more difficult it is to construct a data-​driven model. However, the more parameters selected can also result in a more universal model. Nevertheless, it is feasible to modify a neural-​network-​based model, and it often requires less effort and simulation data than it does to build a model from scratch. Recently a new network modeling approach has emerged that allows for generalization outside of the data provided –​a physics-​based neural network, i.e., network-​based simulators. Such simulators use the data to directly to learn the physics behind behaviors. Graph networks are an important family of these methods (Sanchez-​Gonzalez et al., 2020). In a graph network approach, the system is divided into basic components such as particles, bodies, and nodes. Next, the neural networks learn how the components interact with the environment and with each other. A model constructed using this approach can be reused in the simulation of a completely different system if it models the same phenomena. Compared to a black-​box approach, network-​based simulators require special modeling and training techniques to mimic the system of interconnected components (usually in the form of a directed graph) and to learn the rules instead of behaviors of the system. As such, network-​based simulators are grey-​ box systems. They have been successfully applied to model fluid dynamics and particle dynamics phenomena, which are both known to be computationally expensive and numerically challenging. Data-​driven models are often trained on experimental data, however, data coming from virtual simulations are also useful. For this reason, there are three main approaches based on data sourcing: those based on measured data, those based on virtual test data, and those based on a hybrid approach where the model is first trained using virtual data and then using measurement data. Using experimental data is the most straightforward. The model is constructed based on system measurements. However, models based solely on virtual data are also useful, mostly because they are more efficient. Complex systems may take days or weeks to solve, which may be impractical in many applications. Therefore, engineers often create surrogate models1 that mimic simulation models closely but are less computationally expensive. In the hybrid approach, the model is first built using virtual data and then updated with experimental data.This approach is profitable, because virtual data are often easier to obtain, and experimentation costs time and money. Due to various factors, however, a virtual model may not be sufficiently accurate. Also, a model pretrained on virtual data requires less experimental data than when it is training from scratch.The hybrid approach makes it possible to achieve a high degree of accuracy with less effort. Data-​driven models also make it possible to better understand and utilize the growing amount of raw data that is generated and collected during the

70  Emil Kurvinen et al. lifecycle of a modern machine. The data-​driven approach extracts relationships from the data and offers accurate system responses without relying on classical laws and equations. Frequently, the data-​driven model approach results in efficiencies that are higher than those of classical approaches. However, identifying the appropriate application is challenging, a lack of understanding of the underlaying laws can be a drawback, and model effectiveness and reliability are strongly case and objective dependent. Nevertheless, the data-​driven paradigm is developing rapidly and becoming widely applicable. Recent advances in physics-​based data-​driven formulations will only reinforce that trend. 6.2.2  Neural-​networks-​based data models and other methods The combination of ML and big data has been spotlighted as a robust tool for data-​driven modeling in science and engineering. ML can easily find patterns in a big data set. An artificial neural network is a versatile tool with excellent approximation capabilities that is often equated with the data-​ driven models themselves. It can mimic arbitrarily complex systems with reasonably small memory requirements and limited information about the system, and it makes the best possible use of provided data (Dreyfus, 2005). A traditional neural network can be more difficult to use. Mainly due to hardware restrictions, it usually comprises a limited number of computational units (neurons), and to obtain a satisfactory data model, its hyper-​parameters must be properly tuned. However, rapid computer hardware development, especially of graphics processing units (GPUs) and tensor processing units (TPUs),2 has made possible the advent of the DNN. A DNN, which is based on the artificial neural network with multiple hidden layers between input and output layers, enables the handling of complex nonlinear functions with multi-​dimensional input data. A well trained neural network provides precise pattern recognition based on data sets in real time. These features of the ML approaches, big data recognition and the real time estimation of nonlinear functions, are attractive to dynamics and control engineers who are handling nonlinear system dynamics applications such as railways, vehicles, gaits, robotics, tracking, etc., with real-​ world data. DNN promises to solve most problems using a more general architecture, therefore limiting the number of hyper-​parameters from which to choose (Chollet, 2018). They also require far less data preprocessing in comparison with earlier solutions. For example, it is perfectly fine to input raw sensor data or raw camera image into a DNN and still expect to obtain excellent quality output. The DNN architecture, with multiple layers, will take care of the filtering and processing of the input data. Moreover, as ML develops, standard network patterns and complete pre-​trained networks have become available. One of the best developed areas in the DNN community is image processing and pattern recognition, which boast ready-​made solutions trained on millions of images that can be applied in, e.g., imaged-​based control units.

Accelerating design processes  71 However, neural networks have limited prediction capability.3 Therefore, responses from a neural-​network-​based model given input far removed from the data it was trained on may lead to unreliable system responses. Aside from neural networks, there is a whole family of methods that can be used to construct data-​driven models of many kinds. These include support vector machines, classification and regression trees, Naïve Bayes, and random decision forests. These methods will not be described here in detail. Interested readers are referred to the literature, e.g., Brunton and Kutz (2019). 6.2.3  Data-​driven models in the multibody framework The multibody framework provides methods for computationally efficient and accurate sources of data. It enables achieving realistic behaviors under real-​time constraints so that the virtual representation of a machine behaves like the actual machine. Depending on the objective, multibody-​framework-​based simulation models can provide surrogated models of differing complexity. These can be classified as physics-​based multibody dynamics modeling for local components (which means the nonlinear system identification) or system level multibody dynamics modeling and simulation. Conventionally, data-​driven multibody dynamics models have been used successfully for noise data filtering, reliable black box modeling from experimental data, uncertainty quantification in a probabilistic sense, nonlinear parameter identification in governing equations, etc. (Choi et al., 2020). The aim of these data-​driven approaches is system identification to construct multibody models that accurately reflect the physics. The model for identification has focused on local component approximation (joints, bushings, bearings, contact surfaces, etc.) based on a single and/​or a few points. Recently in a more advanced approach, massive multipoint approximation based on a small number of data points is being used in global approximation. Points from the whole domain of interest are utilized. System level multibody dynamics modeling and simulation is the more interesting approach for multibody system dynamics. The robust features of the ML approach can make it possible. Using a DNN-​based surrogated model, time-​varying results such as displacement, velocity, and acceleration can be predicted in real time without directly solving the governing equations of multibody dynamics. Among the various DNN methods, Feed Forward Networks, with hidden layers and nonlinear activation functions, may be the simplest approach. The reliability of the surrogated model mainly depends on identifying the appropriate hyper-​parameters such as the number of hidden layers, the batch sizes, the number of epochs, the types of optimizers, etc. Consequently, well-​ trained surrogated simulations based on a multibody framework can accurately estimate time-​varying system responses in real time. The approach promises excellent potential for virtual reality, dynamics and control, digital twinning, cyber-​ physical systems, etc. However, in the ML multibody approach, the

72  Emil Kurvinen et al. quality of the data used in the training process is critical and prerequisite for the reliability of the trained surrogated model.

6.3 Applications of data-​driven models There are various potential uses for data-​ driven models. With data-​ driven models, the development process becomes accessible to the broader spectrum of stakeholders. Figure 6.1 illustrates the different areas where data models can be employed and their potential benefits and accessibility to the broader stakeholders. One area where the data models can be exploited directly is in design optimization, for example, of a genetic algorithm where the best combination that fills the objective the best is identified. In addition, the data models can be used in control design for reinforcement learning or fuzzy systems. This makes it possible to develop control algorithms that yield the desired performance. These are only a few practical examples. The potential of using models like these in other applications is significant. The research is active and taking shape, and the main focus is on identifying the cases where the value gained is high enough to warrant implementation from the business perspective. The data models can be at different complexity levels. On the smallest scale a single component can be approximated with a neural network. It may be, for example, a complex nonlinear spring-​damper element, sophisticated friction model, or some magic formula. On a larger scale, a sub-​system (or complete system) can be replaced by its surrogate. As system size and complexity increases, it becomes more difficult to design the data model architecture, the solution becomes less universal, and the demand for data from simulation grows. Large systems, which are intrinsically complex, highly nonlinear, and have many more parameters, are more difficult to accurately and efficiently solve. However, the benefits are also greater.

Accessibility

New business opportunies Maintenance & service

Supports sales and purchase Enhanced operaon

Research and product development Potenal uses and benefits

Figure 6.1 Data-​driven models and their potential uses and benefits and accessibility.

Accelerating design processes  73 The potential uses and benefits associated with data-​driven models will be discussed in more detail in the following paragraphs. 6.3.1  Research and product development Because they can influence a manufacturing company’s competitive success, sales, adaption, and renewal; research and product development are essential. The R&D approach offers benefits for both the product development cycle and the product itself. For example, good R&D results in better concept design and material savings in the final product. These can be optimized based on data from a larger pool of potential users than would be available using classical marketing research methods. This data-​driven method can be used in companies that have a complex and expensive production process. Factories that are responsible for developing agricultural machines, for instance, apply a lot of effort and resources during the model development process. Therefore, a tool that can minimize risks and test machine settings before producing the first actual prototype is well received. This is how the data-​driven methods can be utilized during the R&D process. Because the simulation model makes it possible to quickly do design iterations, data-​driven methods have proven to be effective for machinery design. Receiving customer feedback effectively during the early phases of product development is an important benefit of a data-​driven process, as it enables the involvement of a large group of potential users in the development process. To better involve potential users though, game-​like elements can be added to the testing platform. Indeed, gamification can boost the commitment of test users and even encourage the participation of yet a larger number of participants (Khadim et al. 2018, 2020). 6.3.2  Enhanced operation Data-​driven models can be applied to a company’s different operations to enhance their operational and performance levels. For a machine operation, it is essential to have information regarding the machine state to optimize its performance (Liu et al., 2020). This can be accomplished in practice by using a large number of sensors. Information from sensors can be combined to get an understanding of the machine state. However, this requires that a large number of sensors be used. Alternatively, the machine state can be determined using a limited number of sensors combined with a data model. Data-​driven technology is evolving quickly and finding its way into specific industrial applications, such as data-​driven models, and its accurate physics-​ based representation resolves real-​time problems by producing meaningful and timely data on product behaviors. That being said, this new field of technology is not fully mature and is currently exploited only for limited uses, so there are many benefits yet to be discovered and proven.

74  Emil Kurvinen et al. 6.3.3  Maintenance and service Digitalization has impacted the way that companies do business in different ways. It has also started new trends. The easy availability and mobility of information brings the world to us and also expands the marketplace so that internationalization and cooperation can expand businesses and succeed in the near future. The new technologies brought by digitalization, such as the industrial internet and artificial intelligence and data-​driven technologies, are rapidly changing both products and, in particular, related services. Data-​driven technologies can bring new service-​business opportunities and increase revenue. They also enable new service and solutions business in terms of software, IT, and expert services. The rapid development of digitalization technologies and the subsequent changes in the market provide opportunities for new forms of service business that can be supported, e.g., through research and development (Cheng et al., 2020). 6.3.4  New business opportunities Data-​driven models essentially offer easy access to the dynamics of a product. This feature can be used to offer new services and previously unattainable levels of product features for the customer. The data-​driven models based on simulation approaches enable overcoming the limitations in the scalability, data processing, experimentation, rate of changes, as well as spatial and temporal restrictions inherent to operating only on the basis of tangible, physical products and processes. The concept of combining business and technology is far from traditional, and therefore it requires making radical changes in current work processes (Ikävalko et al., 2018). 6.3.5  Supporting sales and purchase Data-​driven modeling can also be used to study different scenarios in a timely manner. Therefore, it has the potential to support product or process sales to verify and demonstrate performance in required situations. In case of simulation-​ based data systems, it also provides the opportunity to operate the product using the customer’s configuration prior to manufacturing (Alaei et al., 2018). Because this way of doing things is notably different, there are major challenges to adopting data-​driven modeling to enable new business possibilities. In current business models, products or processes are stationary. With simulation-​based data modeling, they should be considered more as a part of the environment, because they can interact closely with their operating environments. Because the new data models involve training data, they will be quite reliable and dependable. However, as a result, there is higher demand for accurate data, so to be able to make correct informed decisions, the validity of the data needs to be carefully verified.

Accelerating design processes  75

6.4  Conclusions Data-​driven services are new and are resulting in innovative business models that can improve the existing business models of many manufacturing companies. They hold great potential for the creation of unique selling points, and they improve the differentiation of companies in highly competitive markets. But the technology is still raw and needs to be further developed. Digitally connected products and services can enable new business opportunities for data-​driven services that can support company growth, markets, or the commercialization of products when data-​driven services are embedded and integrated into existing processes. Data-​driven models can create value and new business opportunities for companies in different ways. The increase in value reflects on every part of the supply chain, from the provider to the customer. While quality requirements are becoming more stringent and technology levels are getting continually higher, companies are struggling with product processes that can be helped with data-​driven models. The concept of the model is currently developing rapidly, which is fueling a lot of research and testing.

Notes 1 Surrogate models can be also created with experimental data, but this is less common. 2 Graphical processing unit and tensor processing unit –​hardware units of the modern computers with enormous computational capabilities due to dedicated parallel processing architecture. In comparison with CPU (central processing unit) GPUs and TPUs can process more data per time unit but are restricted in the number and complexity of available operations. 3 Not to be confused with their excellent approximation capabilities. Here prediction refers to providing output form outside of training domain.

References Alaei, N., Kurvinen, E., & Mikkola, A. (2019). A methodology for product development in mobile machinery: Case example of an excavator. Machines, 7(4), 70. Alaei, N., Rouvinen, A., Mikkola, A., & Nikkilä, R. (2018). Product processes based on digital twin. In Berns, K. et al. (Eds.) Commercial vehicle technology (pp. 187–​194). Wiesbaden: Springer Vieweg. Brunton, S. L., & Kutz, J. N. (2019). Data-​driven science and engineering: Machine learning, dynamical systems, and control (1st ed.). Cambridge, United Kingdom: Cambridge University Press. Cheng, J., Zhang, H., Tao, F., & Juang, C. F. (2020). DT-​II: Digital twin enhanced industrial internet reference framework towards smart manufacturing. Robotics and Computer-​Integrated Manufacturing, 62, 101881. Choi, H.-​S., An, J., Han, S., Kim, J.-​G., Jung, J.-​Y., Choi, J. … Choi, J. H. (2020). Data-​ driven simulation for general purpose multibody dynamics using deep neural networks. Multibody System Dynamics, accepted. Chollet, F. (2018). Deep learning with Python. New York: Manning.

76  Emil Kurvinen et al. Dede E. M., Lee, J., & Nomura, T. (2014). Multiphysics simulation: Electromechanical system applications and optimization. London: Springer. Dreyfus, G. (2005). Neural networks methodology and applications. Berlin: Springer. Ikävalko, H., Turkama, P., & Smedlund, A. (2018). Value creation in the internet of things: Mapping business models and ecosystem roles. Technology Innovation Management Review, 8(3), 5–​15. Kampker A., Husmann M., Harland T., Jussen P., & Steinbauer M. (2018) Six Principles for Successful Data-​ Driven Service Innovation in Industrial Companies, IEEE International Conference on Engineering, Technology and Innovation (ICE/​ ITMC), Stuttgart, 2018, pp. 1–​8 Khadim, Q., Kaikko, E. P., Puolatie, E., & Mikkola, A. (2020). Targeting the user experience in the development of mobile machinery using real-​time multibody simulation. Advances in Mechanical Engineering, 12(6), 1687814020923176. Khadim, Q., Kurvinen, E., Kaikko, E. P., Hukkataival, T., & Mikkola, A. (2018). Real-​ time simulation model for dynamic analysis of three-​wheel counterbalance forklift. International Journal of Vehicle Systems Modelling and Testing, 13(2), 109–​124. Kurvinen E. (2016). Design and simulation of high-​ speed rotating electrical machinery, Doctoral thesis, Lappeenranta University of Technology, School of Energy Systems, Mechanical Engineering, Finland. Liu, Q., Leng, J., Yan, D., Zhang, D., Wei, L., Yu, A., … Chen, X. (2020). Digital twin-​ based designing of the configuration, motion, control, and optimization. Journal of Manufacturing Systems. Available online 28 April 2020. Montáns, F. J., Chinesta, F., Gómez-​Bombarelli, R., & Kutz, J. N. (2019). Data-​driven modeling and learning in science and engineering. Comptes Rendus Mécanique, 347(11), 845–​855. Pyrhönen, J., Jokinen, T., & Hrabovcova, V. (2013). Design of rotating electrical machines (2nd ed.). Chichester: John Wiley & Sons. Sanchez-​Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., & Battaglia, P. W. (2020). Learning to simulate complex physics with graph networks. 37th International Conference on Machine Learning. Uzhegov, N., Kurvinen, E., Nerg, J., Pyrhönen, J., Sopanen, J. T., & Shirinskii, S. (2015). Multidisciplinary design process of a 6-​slot 2-​pole high-​speed permanent-​magnet synchronous machine. IEEE Transactions on Industrial Electronics, 63(2), 784–​795.

7  Gamification and the marketing of agricultural machinery Suraj Jaiswal, Anssi Tarkiainen,Tuhin Choudhury, Jussi Sopanen and Aki Mikkola

7.1  Introduction Traditional approaches to product development and to consumer marketing can be improved by taking advantage of society’s interest in game-​like environments. Gamification channels the stimulating potential of game mechanics into a non-​game context to motivate user involvement (Hamari & Koivisto, 2015). In complex machinery, operators can play an important role in determining design needs and maintenance requirements. By introducing a game-​like environment to product development, the simulation models for complex machinery can be better used to motivate user engagement in the product development process. In addition, a gamified simulation model can serve marketing and sales by prompting earlier and more fruitful customer interaction and promoting better customer feedback regarding products under development. Its continued application can improve the customer experience throughout the machine life cycle and lead to novel new business opportunities. According to Koivisto and Hamari (2019), gamification refers to “a design approach of enhancing services and systems with affordances for experiences similar to those created by games”.These game-​like affordances may be related to, for example, enabling progression tracking, establishing a social setting that includes networking, cooperation, and competition, and promoting immersion such as role play, the use of avatars, and in-​game rewards. These affordances motivate users toward targeted behaviors with experiences such as enjoyment, flow, and accomplishment. For more than a decade, gamification has been applied both in academics and industry (Dehghanzadeh, Fardanesh, Hatami, Talaee, & Noroozi, 2019; Deterding, Khaled, Nacke, & Dixon, 2011; McDonald, Musson, & Smith, 2007). In academics, gamification is used as an advanced digital tool for teaching (Dehghanzadeh et al., 2019; Kurvinen, Kaila, Laakso, & Salakoski, 2020; Liu, 2017). In industry, it is currently used mainly for training (Brough et al., 2007; Li, Grossman, & Fitzmaurice, 2012; Stadnicka & Deif, 2019).

78  Suraj Jaiswal et al. Many papers in the literature describe the use of gamification in product development. For example, one paper proposed a gamified design methodology to improve product development by engaging user needs, emotions, and personality (Signoretti, Martins, Rodrigues, Campos, & Teixeira, 2016). The role of game mechanics in this user engagement process was also studied (Leclercq, Poncin, & Hammedi, 2017). Note that game mechanics are the regulations and procedures of the game that help to define the game-​like environment for the intended users. In a concept design phase, gamification has helped to evaluate team-​confidence and decision-​making ability (Petersen & Ryu, 2015). In some cases, it has been also used to analyze the design requirements for a mobile working machine (Jaiswal, Islam, Hannola, Sopanen, & Mikkola, 2018). In general, an agricultural machine system is complex (Jaiswal, Korkealaakso, Åman, Sopanen, & Mikkola, 2019). Such complex systems can be modeled using a detailed physics-​based approach such as multibody dynamics (Avello, Jimenez, Bayo, & Jalon, 1993; Jaiswal, Rahikainen, Khadim, Sopanen, & Mikkola, 2020). This helps to provide a realistic dynamic simulation of the machine. In other words, it provides a digital platform that can be coupled with the gamification concept. This gamified digital platform can be used to improve the design of such complex systems by using operators’ performance and feedback on the simulation model. Operator experience is not part of the traditional design process (Pahl & Beitz, 2013; Schulte, Weber, & Stark, 1993), which strives to define all design requirements at the beginning even though some may not be determined until later.This gamified approach has the potential to make the design process more straightforward, because the effect of different configurations on final product performance can be studied early. By its nature, such a platform can engage users from the early design phase. A gamified digital platform can offer additional advantages in terms of various aspects of marketing such as research, analysis, and product sales and promotion. From the marketing perspective, gamification is considered a value-​ added service in the form of feedback and interaction tools for users (Huotari & Hamari, 2011). So far, gamification for marketing has been applied mainly in consumer markets, where the focus is on improving loyalty, establishing positive word-​of-​mouth, and promoting engagement in brand communities (Hwang & Choi, 2020; Xi & Hamari, 2020; Zichermann & Linder, 2010). Such targeted behavioral outcomes are strongly linked to branding. At first glance, this might not seem to apply to business-​to-​business (B2B) markets, where purchase decisions are typically influenced by attributes that are more concrete than brand such as price and delivery. However, the importance of B2B branding has been recognized, and this importance is growing (Leek & Christodoulides, 2011). For example, Bruhn, Schnebelen, and Schäfer (2014) found that customer-​to-​customer interactions in brand communities provide customers with functional, experiential, and symbolic benefits that also increase brand loyalty in B2B markets.

Gamification and marketing  79 The objective of this study is to demonstrate a gamification platform for complex working machinery to streamline the design process and marketing aspects.To this end, a farm tractor is considered as a case example.The tractor is modeled using a multibody dynamics approach, i.e., a physics-​based modeling approach. The demonstration includes an affordance that enables progression tracking. Operators can customize their tractors to improve performance, and the tractor model offers three engine options. Incorporating game elements such as goals, curiosity, challenge, fantasy, and control is intended to motivate the users to engage in the simulation process. Within this setting, user experiences, game data, a survey, and interviews are assessed. Based on the findings, the advantages of integrating the gamification platform into B2B brand communities and enabling social and immersive game-​like affordances are discussed. The remainder of this chapter is arranged as follows. The procedure for modeling an agricultural machine is described in Section 7.2. Section 7.3 gives an overview of the gamification concept and its implementation. Section 7.4 provides the example of a farm tractor in a deformable-​ground environment. It also includes a discussion of possible business opportunities. Finally, the conclusions are offered in Section 7.5.

7.2  Modeling an agricultural machine A realistic dynamic simulation model of an agricultural machine can be developed using the multibody system dynamics approach. In this approach, several equations that can be solved in real time are employed to describe the dynamics of the machine. The dynamic equations may couple different engineering areas such as hydraulics and control systems. The real-​time capability enables the solution of the complex equations in a time less than the simulation time-​step of the model. This can provide users with a sense of continuously operating the machine without any computational glitches. The literature offers several formulations to describe the multibody simulation of a working machine (Avello et al., 1993; Bae, Han, & Yoo, 1999; Bayo, Jalon, & Serna, 1988; Bayo & Serna, 1989; Jalon, Alvarez, Ribera, Rodriguez, & Funes, 2005). In this study, the simulation employs a semi-​recursive formulation based on a velocity transformation (Avello et al., 1993), which results in a computationally efficient approach suitable for real-​time applications. Figure 7.1 proposes a design process that can be used to facilitate a user-​parameterized simulation model. 7.2.1  Design process for parameterization When parameterizing a multibody model, a base model is made first, and the parameterized parts are provided as add-​ons (Mohammadi, Kurvinen, & Mikkola, 2019). Model requirements such as geometries and the kinematic and dynamic properties of the machine are collected to build the base model. The

80  Suraj Jaiswal et al. Start Model requirements 3D Modeling

No

Model parameterization ?

Yes Model parameterization

Parameterized parts > 5 ?

Yes

Use of spreadsheet interface No Separate files for parameterized parts Assembly of parameterized parts Feasibility analysis No

Model feasible ?

Yes Testing real-time simulation model Model requirements met ?

No

Yes End

Figure 7.1 A design algorithm to build a parameterized multibody model.

design specifications are collected for model parameterization. Depending on the number of parts to be parameterized, a separate file approach or a spreadsheet interface approach is taken. When the number of parameterized parts is less than or equal to five, for example, separate XML files are used along with the base model, which is also an XML file (Mohammadi, 2017). Otherwise, a spreadsheet interface is used, which is connected to the XML file of the base model using a script written in any programming language, such as Python (Jaiswal, 2017). Based on the parameterization requirements, the parts are assembled to carry out a feasibility analysis. If the feasibility analysis succeeds, the model is tested, and the design is iterated based on the model requirements.

Gamification and marketing  81 7.2.2  Environment modeling The dynamic behavior of a customized real-​ time multibody model is dependent on the description of the virtual environment (Jaiswal et al., 2019). To ensure a realistic dynamic simulation, it is essential to have an accurate description of the environment as well as the machine. Depending on simulation requirements, modeling certain environmental components makes it possible to represent the physical environment graphically. These components can contain complicated geometries and texture mapping. For this study of agricultural machinery, a deformable terrain environment can be modeled to facilitate the dynamic simulation in real time. This can be accomplished by combining mesh-​based and particle-​based methods, as already shown in Jaiswal et al. (2019).

7.3  Gamification Gamification is a process of introducing game-​related elements in a non-​game context to produce an enjoyable game-​like experience (Werbach, 2014). In other words, gamification helps to motivate users to participate in various tasks and activities that can be otherwise non-​attractive. The engagement of users is one of the prime advantages of gamification.With the addition of gamification, there comes a sense of challenge that results in new insights being realized that can be applied to non-​game-​related activities. The gaming aspect encourages the basic human instinct to accept challenges, overcome hurdles, and ultimately win (Aparicio,Vela, Sánchez, & Montes, 2012). For product development, a gamification implementation should provide positive experiences for the users. In turn, these potential future users of the simulated machine will come away with a positive impression. In this way, marketing of the machine product begins even before its initial design is complete. Gamification coupled with digital platforms can be most effective by providing an interactive and social platform. The prime aspect is to motivate and engage users to achieve a goal. By allowing users to explore and utilize the product at the beginning, feedback is generated that can help the product designers target and achieve a more user-​centric product. Furthermore, a user-​ specific game design may improve user satisfaction leading to increased productivity and end-​user creativity (Werbach & Hunter, 2012). Several approaches to implementing the concept of gamification have been proposed in the literature. In this study, however, the approach introduced by Aparicio et al. (2012) has been used. For an effective gamification process, Aparicio et al. (2012) proposed the following steps: • Identification of the prime objective of the task or activity that needs to be gamified. • Identification of one or more transversal objectives that should capture the interest of users –​based on these objectives, a system is developed using

82  Suraj Jaiswal et al. game mechanics that can help to enhance the motivation and interest of the users. • Selection of the appropriate game mechanics to achieve the objectives and facilitate user motivation –​according to the self-​determination theory by Ryan and Deci (2000), instilling intrinsic motivation in users is accomplished by satisfying their psychological and social needs such as autonomy, competence, and relationships. Some examples of game mechanics for autonomy include avatars, fantasy, a configurable interface, privacy, and notification control. For competence, examples include optimal challenge, positive feedback, intuitive controls, points, levels, and leader boards. For relationships, groups, messages, and connections to social networks are important. • Analysis of the usefulness of the implemented gamification based on factors, such as fun, user satisfaction, service quality, and quality indicators –​ fun can be analyzed by examining playability metrics. This can be achieved by testing the users based on specific metrics and based on a questionnaire on the gamified process. A heuristic evaluation by experts can be used as an alternative to specific metrics. Effectiveness can be analyzed with a service quality model that uses predefined quality parameters. Here, a comparison can be carried out between the values of the quality parameters prior to gamification and after the implementation of gamification. 7.3.1  Elements of a game Game elements are introduced in a gamified application. A list of crucial game elements was identified by Read (2009). Key elements include goals and obstacles (which can be explicit contexts and enforced rules), challenges (which can be limited resources or a time constraint), and fantasy (which can be avatars and a realistic three-​dimensional environment). However, Deterding, Dixon, Khaled, and Nacke (2011) concluded that compiling a universal list of game elements is not possible. Other possible examples of game elements include leader boards, ranks, badges, difficulty index, levels, team play, and playfulness. In this study, the simulation model for a customizable agricultural machine, the farm tractor, offers a choice of three different engine models. The game elements introduced in this study are a goal, curiosity, challenge, fantasy, and control. Figure 7.2 illustrates the gamification algorithm employed. 7.3.2  Methods of data extraction The quantitative information extraction procedures begin by recording the game data and analyzing it to support decision-​making on a particular phenomenon (Render, 1997). The recorded game data serve as quantitative information for the study. Interviewing operators is one of the viable methods for extracting qualitative information (O’Leary, 2017). Methods used can include factual interviewing, narrative interviewing, confrontational interviewing, and focused-​group interviewing (Kvale, 2008). In this study, semi-​structured

Gamification and marketing  83 Start Engine selection ?

No

Yes Goal and rules description Continue Yes

Fuel is over ? No Rollover ?

Yes

No No

Goal achieved ? Yes End

Figure 7.2 Gamification algorithm employed in this study.

face-​to-​face interviewing can be used to collect feedback from the users about the playability of the gamified application.

7.4  Case example of a farm tractor The farm tractor model shown in Figure 7.3 is used as the case example for this study. As explained in Section 7.2, the tractor is modeled using a semi-​recursive multibody formulation. The tractor model has nine degrees of freedom (DOFs). Six DOFs are for the translation and rotation of the tractor in three-​ dimensions, one DOF is for the steering mechanism, and two DOFs are for the lifting and tilting of the front-​loader. The front-​loader is controlled by four double-​acting hydraulic cylinders, two each for the lift and tilt mechanisms.The tractor model includes user parameterization. Operators can select one of three different engine options. To facilitate the dynamic simulation of the tractor model, a deformable sand field environment is described.This environment was introduced by Jaiswal et al. (2019). 7.4.1  Gamification of the farm tractor model The tractor model in Figure 7.3 is gamified by incorporating a goal, curiosity, challenge, fantasy, and control, as explained in Section 7.3.1. The rules, settings, and boundary conditions of this gamified application depend on the

84  Suraj Jaiswal et al. Fence

Front-loader

Cabin

Sand particles

Bucket Frame Contact patch during the forward manuever

Deformable sand field

Figure 7.3 Real-​time tractor simulation model in a deformable sand field environment.

Brake sign

Gear indicator

Fuel gauge Roll inclinometer Bucket tilt indicator

Tachometer

Bucket weight indicator

Speedometer

Bucket height indicator

Figure 7.4 The gamified graphical user interface of the tractor model showing the restricted field of view for the driver.

game mechanics used by the tractor model. In tractor operations, because of a restricted field of view, the operator has limited information about the position, angle, and weight of the bucket. Therefore, the goal of the gamified tractor model is to load and transfer approximately 500 kg of sand particles from a pile of sand onto the ground near the fence. The user interface developed to achieve a gamified experience for the tractor simulation model is shown in Figure 7.4. Curiosity is introduced to the gamified tractor model by restricting the field of view of the driver, as is the case in an actual tractor. Challenge is introduced by limiting fuel and rolling angle. Both can be monitored via the fuel gauge and

Gamification and marketing  85 roll inclinometer. Fantasy is introduced by running the simulation in a real-​time simulator with a motion platform.The simulator gives realistic feedback during maneuvers and operations. Control is introduced using the bucket height, tilt, and weight indicators to provide users with the accurate position, angle, and weight of the bucket. To provide better tractor control, the simulator screen includes a tachometer, a speedometer, and gear and brake indicators. As users drive the gamified tractor, their data can be recorded. Based on the time required to achieve the stated goal, and other factors such as engine selection, fuel consumption, and rollover occurrence, a leaderboard can be compiled to monitor relative performance. The leaderboard can track, for example, the performance of 30 or more users comprising both experienced and inexperienced. In addition to the leaderboard, impressions about performance and other aspects of the user experience can be collected by making a questionnaire available beforehand and by organizing structured face-​to-​face interviews with the users afterwards. 7.4.2  Product development opportunity What is learned from the users and what is recorded from their operation of the simulator can be used to modify, optimize, or validate the design of the tractor model. For example, a user-​to-​user comparative analysis of front-​loader arm movement could be carried out. Based on relative performance as established by the leaderboard, tractor designers could then adjust front-​loader arm length, for example, to optimize tractor efficiency. Jaiswal et al. (2018) explored the possibility of identifying key functionality design requirements for mobile working machinery. In addition to tractor functionality aspects, the user game data can also be used to identify key engine parameters, such as average fuel consumption. Using these data, machine design and operation-​related bottlenecks can be identified and optimized. Features that most users deemed unsuitable can be initially identified from the game data and further confirmed through the interviews. Based on the findings, the product concept could be revised and these unwanted features could be eliminated. Furthermore, the gamified simulator experience can help to identify the driver interface features preferred by the users. For example, they may prefer to see machine data indicators such as the speedometer, inclinometer, or fuel gauge as a heads-​up display (HUD). Understanding this preference early on could be invaluable for companies who are willing to integrate HUD-​ based technology into their vehicles. 7.4.3  Marketing opportunity This study result in a gamified digital machine platform that most companies can integrate into the design and validation of agricultural machine products under development. Building a brand community and content marketing is not new to the heavy machinery industries. For example, John Deere is a well-​ known international farming equipment company. John Deere has worked to

86  Suraj Jaiswal et al. build brand identity and a brand community since the nineteenth century with its customer magazine “The Furrow” (Pulizzi, 2013). The gamified digital platform also supports marketing by providing an easy and inexpensive demonstration of a particular agricultural machine.The parameterization capability of the machine simulation model gives it the capability of reaching a larger audience, irrespective of the availability of components. Moreover, potential customers can be given the opportunity to test a machine they are interested in and exercise it in various scenarios such as the deformable sandy environment used in this study. In addition, the proposed gamified digital platform of a tractor can be integrated into traditional digital marketing channels such as an online customer magazine or social media platforms. In the demonstrated solution, the gamified affordances enabled achievement or progression tracking, that is, users were able to customize their tractors to improve performance. Stimulating user competitiveness in this way can help to motivate users to make more use of the gamified platform. Affordances that enable social and immersive experiences (such as sharing game-​play video, encouraging customization of the tractor, and providing avatars for game play) can be used to motivate brand-​relationship behaviors. Shared user content is a specific form of word-​of-​mouth, which is known to positively influence attitudes and behaviors, as shown in the study by Herold, Sipilä, Tarkiainen, and Sundqvist (2018). Furthermore, the gamified digital platform allows users to exercise control over and associate themselves with the machine, which helps them to develop psychological ownership (Jussila, Tarkiainen, Sarstedt, & Hair, 2015).

7.5  Conclusion The objective of this study was to demonstrate a gamification platform for complex machinery that can be used to streamline the design process and improve aspects of marketing and sales. A farm tractor was modeled as a case example using a physics-​based multibody dynamics approach. This provided a realistic dynamic simulation of the tractor in real time. The parameterization design process allowed users to select an engine from three different engine options. The tractor model was gamified by incorporating the game elements: goals, curiosity, challenge, fantasy, and control. The role of gamification in this demonstration was to motivate users to engage with the simulator. In the gamified tractor model, the goal was to load and transfer a moderate amount of sand particles from a pile of sand onto the ground near the fence. The user experience can be collected in the form of performance data and data collected from a questionnaire and face-​to-​face interviews. These data serve to identify needs and optimize tractor design. The gamified tractor offers interesting marketing opportunities when integrated into digital marketing channels. The gamified application can have extensive outreach across versatile media platforms. Future studies can focus on studying the varying user

Gamification and marketing  87 experiences under similar conditions. Furthermore, future studies could investigate potential misuse of the gamification concept in the context of marketing.

Acknowledgment(s) The volunteered support of Dr. Emil Kurvinen in the review process of this study is highly appreciated. This work was supported in part by the Business Finland [project: Digital Product Processes through Physics Based Real-​Time Simulation –​DigiPro], and in part by the Academy of Finland under Grant #316106.

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8  Added value from virtual sensors Janne Heikkinen, Emil Kurvinen and Jussi Sopanen

8.1  Introduction The chapter introduces the concept of virtual sensors in the context of simulation and explains how they can add value for original equipment manufacturers (OEMs), end-​users, and other stakeholders in the value chain. The concept of virtual sensors is known to some extent by engineers working in research and development (R&D), and virtual sensors are applied in the R&D phases of new equipment development. However, their potential to provide valuable insights for other facets of business operations such as the maintenance and repair businesses and other after-​sales services is currently undervalued and unexploited. This book introduces different aspects of digital tool use for different phases in the value chain for machinery manufacturing and sales. Virtual sensors are one of the tools in the digital toolset. Whereas the book introduces different opportunities for simulation methods on a general level, this chapter gives an overview of a specific method for using a physics-​based digital system model seasoned with actual measured data. The objective of this chapter is to introduce the concept of the virtual sensor. The concept is simple, but its implementation for machinery requires understanding the limitations introduced by the methods used to build the models that are deployed by virtual sensors. In addition, to better understand the accuracies and the practical limitations of the introduced methodology, it is important to understand what kind of data can be captured from existing systems and used as input for the simulation models. The importance of the chapter topic arises from the fact that not every behavior of a complex physical system can be economically measured or measured at all, because geometry limitations or environmental conditions can hinder or prevent access. Instead of taking measurements using physical sensors, a physics-​based digital twin makes it possible to use digital methods to gain virtual access to the system of interest. Understanding the novel business opportunities and added value made available by taking a virtual approach can directly lower end-​product costs and give additional insights about the machine. These additional insights can help, for example, to estimate machine

Added value from virtual sensors  91 risk in detail, and therefore lead to an increase in after-​sales services as discussed in Zheng et al. (2018). The chapter is structured as follows. First, the concept of virtual sensors is introduced. The introduction includes the general overview of virtual sensors explaining the challenges of using the hardware sensors for which the virtual sensors are providing a cost-​effective alternative. The chapter also discusses the capabilities of the virtual models on a general level, focusing on their capabilities to conduct virtual measurements of the behaviors in physical systems. The limitations of the technology are introduced and discussed on a detailed level to clarify both capabilities and limitations. At the end of the chapter, business opportunities introduced by virtual sensors are discussed in general. Finally, recommendations are introduced for different stakeholders including business managers and entrepreneurs, academia, educational institutions, and policy makers.

8.2 Virtual sensors: context and background Digital twins belong to the group of cyber-​physical systems (CPS), where the integration of a cyber world and the dynamic physical world are represented. In general, a CPS is divided into two parts: the digital twin, a virtual counterpart of the physical system, and the actual physical system including its hardware sensors and connection to the control system. The machine system includes the control system and hardware sensors whether or not the machine is human operated or automated (e.g., a programmed process without human interaction). The digital twin is directed toward engineering aspects, whereas the virtual models and data are the primary focus (Tao et al., 2019). Usage of the term “digital twin” increased rapidly after its definition by NASA in 2010 (Shafto et al., 2010), although the term had been defined earlier in 2002 (Grieves, 2005). According to NASA, the digital twin is a method of predicting the structural behavior of an aircraft by simulating and analyzing its behavior via its digital model. Since then, the term has evolved to include more aspects and other general approaches including Industry 4.0, machine learning, wireless communication, and cloud computing (Negri et al., 2017). The definition of term “digital twin” and its evolution can be found from the review of Negri et al. (2017). The benefits of these systems originate from ideally having all parts connected in real time, allowing them to operate based on each other’s data (Al-​Ali et al., 2018). In 2019, the definition of digital twins expanded, and the concept now applies to the product it represents throughout its lifecycle, i.e., from design to manufacturing and operation (Lu et al., 2020). Today, the term “digital twin” covers so many aspects that comparison of various implementations has become difficult. This has resulted in the need to evaluate and compare digital twins by metrics (Autiosalo et al., 2019) and to introduce categorization based on the sophistication/​maturity level of each (Madni et al., 2019). In this book, the digital twin is presented in the context of its physics basis and featuring its real-​time capabilities. The physics-​based digital twin uses the

92  Janne Heikkinen et al. laws of physics as boundary conditions resulting in virtual models that can accurately imitate the behaviors of real machinery. Physics-​based real-​time simulation models have been used widely for mobile working machinery. For example, see the excavator model in Alaei et al. (2019), the agricultural tractor model in Jaiswal et al. (2019), and the forklift model in Khadim et al. (2018). In addition, the real-​time capability of digital twinning makes it possible to run the simulation model in parallel with the actual machine. That is to say, the model can replicate machine maneuvers as the machine carries them out. The virtual sensor is one tool in the digital-​twin toolset that can be used to improve machine operation by giving the operator a better understanding of the machines’ dynamic behaviors. In the product development process and later in the product lifecycle, condition monitoring and fault diagnostics provided by digital twinning are promising features that can be exploited to improve the performance of machinery (Negri et al., 2017). Real-​time simulation can give the operator an experience similar to that of actual machine operation. In addition, it makes it possible to study, in real time, how different parameter settings will affect the machine’s dynamic behaviors. It also enables timely analysis of the machine’s condition during or after fault events. Simulation tools offer the possibility of studying systems in real time using computationally efficient simulation methods. At the cost of accuracy, some of the deployed methods provide computational efficiency by employing a simplified methodology. However, the most advanced simulation methods, such as multibody dynamics simulation, are capable of simulating behaviors in real time with rather complex models and high accuracy. Additionally, the real-​ time simulation models can run in parallel with the actual machine making it possible to provide real-​time information from the virtual sensor readings to be used for predicting machine condition and assisting in decision-​making during controlled maneuvers. A simulation model that takes into consideration the accuracy needed and the complexity of the studied system is required to perform real-​time simulation. Computational capacity is increasing continuously and at a fast pace, and more complex simulation methods can be used for real-​time simulation due to the continuous development of simulation algorithms and methodologies. Lately, faster-​than-​real-​time simulation has become available. This new capability offers an interesting opportunity to improve machine behavior predictability in the near future via simulation, e.g., by using a simulator to teach operators how to run the machinery so that mechanical stresses are minimized, which will result in longer machine life and possibly increased performance. According to Shenghui et al. (2011), virtual sensors can be defined as software algorithms that utilize available measurements from the machinery to compute an estimate for the physical quantity of interest.Virtual sensors are also called soft sensors, because they are based on software. Virtual sensors do not directly sense the hardware as do traditional sensors (Fortuna et al., 2007). With respect to the information provided, virtual sensors can function the same way

Added value from virtual sensors  93 as any traditional hardware-​based sensor. To add a virtual sensor, however, it is not necessary to purchase and install more instrumentation or add weight to the machine.To realize these cost and weight benefits, a validated simulation model is required that can estimate the measured quantity with an accuracy sufficient for decision-​making purposes. The virtual sensor can also be used to provide additional machine data. For example, insights could be made available into the dynamic behavior of the machine as introduced in by Inamdar et al. (2016). In addition to the monitoring of machine functions, the virtual sensor concept can also be expanded to include the observation of human behaviors in the virtual models. This makes it possible to study the human movements needed to carry out assembly tasks, for example. This capability could be used to assess, e.g., the ergonomics or timing of tasks in the manufacturing process (Gaisbauer et al., 2020).

8.3 Virtual sensors as a part of the product offering The traditional approach to monitoring the condition of machinery has been to attach physical sensors to the locations that are most fragile or provide the most valuable information about machine performance or condition. These measurements are obtained using physical hardware sensors. Hardware sensor development continues at a rapid pace, and sensor pricing has decreased. Sensors with modest accuracy are cost-​efficient. Even sensors with wireless capability are both economical and readily available. However, hardware sensors are prone to failure, wireless sensors are usually battery operated with limited battery capacity, and wired sensors require direct connection to the data acquisition system. These deficiencies can be overcome by using virtual rather than hardware sensing.To exploit their full capability and to get accurate measurements, however, the implementation and continuous use of virtual sensors requires another type of engineering expertise above and beyond that needed to implement a hardware-​based sensor solution. The following sections introduce the technical background basics needed to enable effective use of virtual sensors and some specific features that are associated with the used methods. 8.3.1 Technical methods enabling virtual sensing To use any sort of virtual sensing, one must understand the available and applicable technical methods and tools.There are a handful of methods that have been developed over the years. Some of the simplest methods rely on linear analytic equations, which make them attractive for analyzing simple cases where a linear relationship exists between the measurements and the observed parameter. On the other hand, advances in computational power and the development of more advanced numerical methods make it possible to conduct more detailed studies. The selection of methodology is always a trade-​off between computational efficiency and accuracy.

94  Janne Heikkinen et al. Another significant feature that must be considered when planning to use the virtual sensing approach is if the virtual measurements can be done online or offline. Online calculation means the measurements must be performed in real time, whereas offline measurements means that the measurements are done independently of machine operation, which introduces an opportunity to use more accurate simulation methods since computation time is not a limiting factor. Typically, the most accurate model comprises a combination of different modeling techniques, i.e., analytical equations, numerical solutions, and pre-​ calculated parameters. The model used as a virtual counterpart of the physical system can also be included in the product data management system as a part of the product data. 8.3.1.1  Analytical methods Usually, the analytical methods rely on equations that were developed decades or even centuries ago. Analytical equations are appropriate for simple cases where the interaction between different parts of the system are simple and where only a few parts are included.The relationships between these bodies can be simply described, e.g., using linear relationships. Analytical methods are very efficient from a computational perspective. However, for more complex systems or when the interaction between different bodies cannot be simply described, the accuracy of analytical methods is not sufficient. Analytical equations could be used as virtual sensors, for example, to estimate the weight of different objects using the linear relations between force (weight), displacement, and spring stiffness. As a practical case example, the weight of cargo loaded into a truck could be estimated knowing the spring stiffness of the truck suspension and the measurable push of the truck body after loading. Body displacement can be measured accurately using a very simple and cost-​efficient linear potentiometer –​familiar from the elementary school physics –​and the analytical equation would be used to define the weight of the cargo without introducing complex and expensive piezo-​electronic force sensors that would enable direct weight measurement.The accuracy of the weight estimated would not be as high as that provided by force sensors for this specific purpose, but the accuracy would be sufficient for most daily operations. Although this may be an oversimplified example of a virtual sensor, it introduces the basic principle that the physical relationship between different measurements can be used to convert easily available information into a more valuable format. 8.3.1.2  Numerical methods Numerical methods are usually used for more complex systems due to the limited capabilities of analytical methodologies.The development of numerical methods has been significantly accelerated due to achievements in developing

Added value from virtual sensors  95 microchips and other electronics enabling very powerful numerical computation. One of the numerical methods used most in mechanical engineering that can be applied to execute virtual sensing is the finite element method (FEM). FEM is typically used to analyze mechanical structures and their behaviors by determining loads that occur during operation. The objective is to ensure that the structure can withstand the forces, pressures, and other loads without damage. Systems are modeled in FEM using a number of finite elements that represents the system as a whole so it can be analyzed via numerical computation. Typically, a significant number of elements are needed to accurately describe a complex system, and the square of the number of elements is proportional to the computational effort that is required to solve these numerical problems. In the scope of the book, there is no need to get into more details of the FEM and all the features of it, but on a general level, it provides a good basis for performing virtual sensing of object behaviors that cannot be straightforwardly measured if at all. The method gives access to the interior of structural elements not reachable using any hardware sensor. One might question if FEM can be considered a virtual sensor due to its solid footprint as a numerical tool in machine analysis, however, in the context of virtual sensing, FEM makes it possible to build a virtual replica of a physical system and provide a virtual environment completed by measured input data enabling the analysis of computationally inaccessible but interesting system performance parameters. Another method that has undergone significant development recently is simulation using multibody system dynamics. In multibody systems simulation, a mechanical system can be described as multiple rigid non-​deformable bodies connected to each other by joints and forces. From the computation perspective, the system analysis solution is iterative. The state of the system –​ its positions, velocities, and accelerations –​is solved numerically using matrix operations, which makes the method computationally expensive. However, continual increases in computational power and advanced algorithm development have brought about significant improvements in computational capability and efficiency, which in turn have now made it possible to solve multibody systems in real time or even faster than real time. Another advanced technique in multibody systems simulation enables the modeling of flexible bodies by including the description of body deformations. This technique makes simulation via multibody system dynamics more accurate and adds the ability to estimate mechanical component durability. The opportunity to calculate multibody systems faster than real time makes it possible to predict the behaviors of upcoming machine movements based on history data and the control signal from the controller to perform maneuvers or operations related to the expected working cycles. With faster-​than-​real-​time solutions, the virtual model can predict, in advance, the optimal path to move a container from one place to another. The optimal path can be determined in

96  Janne Heikkinen et al. terms of duration, fuel efficiency, mechanical durability, or other factors. These factors can be defined by the operator, machine manufacturer, or another relevant stakeholder responsible for the performance of the machine, lifecycle costs, safety, or any other important feature of the machine operation. 8.3.2  Opportunities/​benefits and challenges of virtual measurements Virtual measurements can also be categorized based on computational pace. The categorization can be as straightforward as dividing the measurement into offline and online measurements. The definite factor controlling into which category a method falls is whether or not the measurement can be made in real time. Online measurement is available if computation time for a one-​second simulation is less than one second. If not, the virtual measurement falls into the offline category. The following subsections introduce the opportunities and challenges of both categories. 8.3.2.1  Offline virtual measurement –​slower than real time Offline measurements can be slower than real time. In other words, minutes or even hours of computational effort can be applied to solve a one-​second virtual measurement. The system model can be accurate enough to include non-​linearities for interactions between different parts of the system, or it can include different aspects of the system environment. On the one hand, more intensive computational methodology can result in greater virtual measurement accuracy. On the other hand, it results in lower computational efficiency. Offline virtual measurement can be used to study a wide range of different features of the details of a single component or of a complete machine assembly. Virtually, any existing simulation method can be used to perform offline virtual measurements, even methods capable of faster-​than-​real-​time solution, i.e., online measurement. Offline simulation enables the construction, for example, of lookup tables, where the parameters in the simulation model are pre-​calculated and their effect on virtual sensor values are known in advance. During actual machine operation, the lookup tables could be used to guide movements instead of having to perform full simulations in parallel to determine the needed information. The main challenge of offline measurements is the reliability of the virtual model. Accuracy is good, and it reflects the design selection of the user instead of introducing limitations from the computational perspective. Because of its offline nature, specifically measured data is needed beforehand to validate the model and the virtual sensor measurements. This must be an intentional procedure, which makes it inflexible and not applicable for systems that experience heavily fluctuating states over their lifecycle. For systems with states that fluctuate over time, the validation of the virtual model becomes problematic. An account manager or another responsible person must make sure that the model is updated and follows the actual conditions of the machine.

Added value from virtual sensors  97 8.3.2.2 Online virtual measurement –​faster than real time In contrast, online virtual measurement requires the application of faster-​than-​ real-​time simulation methods. Every simulation time step must be solved in less time than the time-​step simulated, i.e., a one-​second time span must be solved in less than one second. Online measurements are enabled by real-​time or faster-​than-​real-​time simulation methods. Real-​time simulation makes it possible to engage the operator directly as part of the simulation model. To better engage a human as a part of a simulation, the simulation environment can be equipped with a regular display, virtual 3D glasses (such as Oculus), or even a motion platform that not only captures the visual aspects of the simulation, but also provides haptic feedback. Real-​time simulation can be used to adjust the driving parameters or control algorithms simultaneously when the machinery is running. It enables simulating multiple future scenarios in parallel using, for example, server clusters or multiple CPU cores. Based on the simulation results, the best scenario can be applied to maximize performance, extend service life, or prevent failures. With real-​time simulation, simulators can be developed that give the operator an experience that feels like operating real machinery in the field. Making online measurements or simulating real-​ time operation require computationally efficient methods. Analytical methods are the most applicable, but advanced computational analysis routines can also be used to perform multibody simulation in real time. However, multibody simulation does not deal well with unexpected events where the numerical methods are unable to resolve fast enough to sustain real-​time simulation or cannot solve the problem at all resulting in a software crash. Online measurements can also rely on lookup-​table approaches where the tables have been prepared using offline simulation methods. For example, the nature of contact between two parts can be pre-​calculated using offline methods, and the forces in the real-​time simulation can be recorded based on the pre-​calculated and tabulated data for different operational scenarios or conditions. The main challenges presented by online measurements relate to measurement accuracy. Successfully making online measurements requires simplifications to be made to maintain the necessary computational pace. These simplifications affect the accuracy of the results. This shortcoming can be minimized if the virtual model is continuously updated by feedback from the machine in operation. Moreover, it is important to continuously estimate the estimated error of the virtual measurement. Often case-​specific tailoring of model parameters is required. 8.3.3  Business opportunities introduced by virtual sensors Virtual sensors can provide added value in the different phases of a product lifecycle for different stakeholders in the value chain. Their most obvious use is as alternatives to physical sensors. Virtual sensors are typically used to

98  Janne Heikkinen et al. monitor the system conditions and performance, and the monitored readings are evaluated and following actions taken by qualified engineers. Even though modern hardware sensors are connected to cloud services and monitoring can be done remotely, a large percentage of existing sensors must be connected manually to a data acquisition system. Furthermore, the bandwidth needed to collect all the data is often limited, so data are not collected continuously when the equipment is in operation. Instead, data are gathered according to a planned measurement schedule such as once per day for ten minutes, for example. Virtual sensors offer the opportunity to do the heavy numerical calculation and data streaming in an internet of things environment with virtually no limit to computational power or bandwidth. That makes it possible to monitor the machine system continuously, observing the changes of measurements as operational parameters or conditions change and collecting the data from hard-​to-​access locations. Having better predictability of system performance and durability makes it possible to schedule maintenance as needed, which extends service intervals and reduces maintenance costs. It also helps to avoid expensive unpredicted service breaks due to failing machinery. OEMs can guarantee better predictability as a selling argument to the customer. The predictability can serve as a competitive advantage, or OEMs can improve their service businesses with the advanced monitoring capabilities. Service businesses can provide an additional recurring revenue stream raising the monetary value of a single installed unit and forging another link in the value chain via the upgraded value proposition. Alternatively, virtual sensoring can be sold as an after-​sales product like software updates. This is a value-​add proposition for the customer, because maintenance costs associated with detecting physical sensor faults and replacing the faulty sensors can be significant over the life of a product. Virtual sensors also offer other valuable features. There are many R&D activities that occur before the mature product stage of the product lifecycle.With virtual sensors, the R&D team can more comprehensively consider the operational features of the existing equipment fleet. Data made available by monitoring machine fleet operations reveal actual changing operational conditions and environmental aspects ranging from in what industry and geographical area the equipment is operating to what is the expertise and experience of the operators. The better understanding of the affecting parameters provided enables better product design that takes into consideration specific conditions that can be further accounted for in control algorithms, user manuals, or service instructions, for example. From the manufacturing perspective the virtual sensor concept has the potential to reduce the number of physical sensors in the end product, which decreases manufacturing cost and the number of parts in the product bill of materials.

8.4  Conclusions The chapter discussed the virtual sensor concept in the context of physics-​ based digital twins. The virtual sensor can operate offline or online depending

Added value from virtual sensors  99 on case-​specific needs. In certain dynamic systems such as mobile machinery, for example, online sensing is required to get valuable operational insight. In static systems with only a few affecting parameters, offline virtual sensors can be used. In a product cost structure, the virtual sensor requires a different kind of investment compared to current sensor approaches, and professional personnel are needed for implementation and to ensure that accuracy is and remains at the desired level. The operating costs of virtual sensors are minimal and implementing them does not require changes to actual system components. However, the virtual model must be periodically updated as machine wear affects operation over the product lifecycle. Recent developments in the technology, simulation algorithms, and computational capacity have made it possible for simulation models to include sufficient detail to represent complex machine systems with a high degree of accuracy and computational efficiency. The fastest analysis methods are based on analytical equations that describe the system through linear relations. The more complex numerical analysis methods are more computationally heavy, but they allow model complexity to be significantly higher without sacrificing accuracy. The achievable level of accuracy and computational efficiency is usually a trade-​off between these two. Depending on computational capability and the required effort, virtual measurement can be either offline or online. Offline measurement is slower than real time, i.e., the computational effort to simulate a certain time span takes more CPU time than the length of the time span itself. In contrast, online virtual measurement is faster than real-​time meaning that calculation is performed in a shorter time span than the overall length of the simulation. The introduction of virtual sensors and their use in different applications promises to further develop and increase the customization level of different products for customer-​ specific needs. Virtual sensors also help machine manufacturers by providing additional insights into machine operations. Because the interactions between internal and external behaviors can be monitored, increases in efficiency can be achieved, for example, by better focusing machine design on specific requirements and system-​level performance. From a broader perspective, the virtual product makes it possible to operate the machine simulated at or near its physical limits within its virtual environment. As a result, less actual machine testing is required, which reduces labor and cost and the test results documentation needed to further utilize test results. However, making use of virtual sensors requires a different engineering skill set. Company management must recognize and accommodate this need to take full advantage of the virtual sensing concept. Virtual sensors require qualified engineering work, and the models need to be maintained continually. Because the concept of digital twins is evolving and digital-​twin research is currently very active, the formulations are still being finalized, and standardization is ongoing. The current lack of standardization is slowing integration of the technology and consequently, general benefits are not well understood. Pioneering companies are now becoming actively involved in development of

100  Janne Heikkinen et al. the standards. For example, the ongoing “Digital Twin manufacturing framework –​ISO/​CD 23247” is currently being established. For business owners and entrepreneurs being a part of standardization opens a novel opportunity to be in the front line of the future virtual sensor business. Policy makers should also understand the present lack of standardization. The business potential of digital twinning is clear, but practical business implementation of virtual sensors remains case specific. Standardization should also consider virtual sensor quality issues in the same way as product quality issues. In addition, liabilities that could arise from the misuse or misinterpretation of virtual sensor data should be considered so that responsibilities become and stay clear, and so that liability issues do not prevent widespread implementation of virtual sensors. To some extent, academia shares some responsibility to develop methodologies that provide more valuable digital tools for technology businesses, but also demonstrate and define practical limitations of the technology that should be taken into consideration by all the stakeholders.

References Al-​Ali, A. R., Gupta, R. & Al Nabulsi, A. (2018). Cyber physical systems role in manufacturing technologies. AIP Conference Proceedings, 1957(1), 1–​7. Alaei, N., Kurvinen, E., & Mikkola, A. (2019). A methodology for product development in mobile machinery: Case example of an excavator, Machines, 7(4), 1–​15. Autiosalo, J.,Vepsäläinen, J.,Viitala, R., & Tammi, K. (2019). A feature-​based framework for structuring industrial digital twins. IEEE Access, 8, 1193–​1208. Fortuna, L., Graziani, S., Rizzo, A. & Xibilia, M. G. (2007). Soft sensors for monitoring and control of industrial processes. London: Springer-​Verlag, 284 p. Gaisbauer, F., Lampen, E., Agethen, P., & Rukzio, E. (2020). Combining heterogeneous digital human simulations: Presenting a novel co-​simulation approach for incorporating different character animation technologies. The Visual Computer, 1–​ 18. Published online 22 January 2020. Grieves, M. (2005). Product lifecycle management: The new paradigm for enterprises. International Journal of Product Development, 2(1/​2), 71–​84. Inamdar, S., Allemang, R. J. & Phillips, A. W. (2016). Estimation of frequency and damping of a rotating system using MEOT and virtual sensor concept. Special Topics in Structural Dynamics, 6, 23–​35. Jaiswal, S., Korkealaakso, P., Åman, R., Sopanen, J., & Mikkola, A. (2019). Deformable terrain model for the real-​time multibody simulation of a tractor with a hydraulically driven front-​loader. IEEE Access, 7, 172694–​172708. Khadim, Q., Kurvinen, E., Kaikko, E.-​P., Hukkataival, T., & Mikkola, A. (2018) Real-​ time simulation model for dynamic analysis of three-​wheel counterbalance forklift. International Journal of Vehicle Systems Modelling and Testing, 13(2), 109–​124. Lu, Y., Liu, C., Kevin, I., Wang, K., Huang, H., & Xu, X. (2020). Digital twin-​driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-​Integrated Manufacturing, 61, 101837. Madni, A. M., Madni, C. C., & Lucero, S. D. (2019). Leveraging digital twin technology in model-​based systems engineering. Systems, 7(1), 7. Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-​based production systems. Procedia Manufacturing, 11, 939–​948.

Added value from virtual sensors  101 Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2010). Draft modeling, simulation, information technology & processing roadmap. Technology Area, 11. Washington, DC: National Aeronautics and Space Administration. Shenghui, P., Chuan, L., Menghe, L. & Lezhu, C. (2011). Virtual sensor for vehicle sideslip angle based on extended Kalman filter. Third International Conference on Measuring Technology and Mechatronics Automation, pp. 1131–​1134. Tao, F., Qi, Q., Wang L., & Nee, A. Y. C (2019). Digital twins and cyber–​physical systems toward smart manufacturing and Industry 4.0: Correlation and comparison. Engineering, 5(4), 653–​661. Zheng, P., Lin, T. J., Chen, C. H., & Xu, X. (2018). A systematic design approach for service innovation of smart product-​service systems. Journal of Cleaner Production, 201, 657–​667.

9  The technical-​business aspects of two mid-​sized manufacturing companies implementing a joint simulation model Manouchehr Mohammadi, Kalle Elfvengren, Qasim Khadim and Aki Mikkola

9.1  Introduction Companies are increasingly developing their business-​to-​business (B2B) activities to offer more exciting products to end users and customers. However, because B2B business-​model decisions are generally made without input from end users, these companies often do not achieve their sustainable business objectives. Simulation offers an opportunity to increase customer value at different phases of the product lifecycle (Ripley, 2009; Tao et al., 2019). However, little research has been done on the technical-​business aspects of implementing simulation in a B2B framework. Nonetheless, real-​time simulation methods can tightly integrate end users and customers with B2B activity. In addition, many conventional simulation studies ignore the physics of the real world, which limits the possible technical-​business advantages of simulation technologies. Introducing real-​time simulation can eliminate this lack of real-​world physics perspective. Integrated with a modern simulator system, real-​time simulation enables end users and customers to directly participate in the development and testing of products. If two manufacturing companies that are providing complementary equipment for a particular industry segment work together to develop a B2B joint simulation model, their end users and customers can be engaged to help select components or sub-​components to design and test forthcoming products that provide better customer value. Using physics-​based real-​time simulation makes it possible for the end users and customers to experience the dynamic behaviors of the products represented by the joint simulation model. From a preset range of values, they can set the required component parameters to guide the design of optimal, accurate, scalable, and efficient products. In this way, real-​ time joint simulation efforts can contribute to the B2B activities of both participating manufacturing companies. One identified industry need is to better understand the technical-​business aspects of implementing simulation in mid-​sized manufacturing companies.

Implementing a joint simulation model  103 Considering this need, this chapter focuses on a case example where real-​ time simulation models from two different Finnish companies are integrated to increase customer value with respect to B2B activity. Objectives include increasing the participation of the end users and customers, examining the feasibility of using parameterized simulation models, and exploring the technical and business aspects of the joint simulation effort. The case example involves the real-​time simulation models of forestry tractor and harvester crane systems. The chapter is structured as follows. The following section reviews previous research on the topic. The third section (Section 9.3) defines an architecture for building the joint simulation model. In addition, it determines how the combined model can be constructed based on user needs. The case example, combining the simulation models of the Valtra N-​series tractor and the Kesla 6H-​series harvester crane systems, is described and discussed in the fourth section (Section 9.4). The challenges experienced from both the technical and business points of view are reviewed. The section also proposes possible solutions (lesson learned). Conclusions are presented in the final paragraphs.

9.2  Related research B2B activities would be more successful if end users had more input into product development. Customer value should be a key aspect of initiatives to establish customers and end users as stakeholders in B2B activities (Carmona-​Lavado et al., 2020; Lesmono et al., 2020). To achieve and sustain a competitive advantage, customer value in product development must be improved (Anderson et al., 2006; Zhang et al., 2013). Moreover, there should be more participation in product development by end users and customers to better define the value of B2B activities through innovative ideas, skills, education, experience, and feedback (Aschehoug et al., 2019; Orcik et al., 2013). Parameterizing products in this way can increase collaboration between users and manufacturers, profitability, and product quality (Orcik et al., 2013). In practice, the implementation of simulation technologies makes it possible to introduce the needs and choices of end users and customers in product development activities (Karlberg, 2013; Tao et al., 2019). To develop customer-​ oriented products, Boman et al. also suggested including users in product development via simulation (Boman et al., 1998). Simulation use has been proven to be an efficient way to optimize the product development process, and it is widely used in industry (Mahesh, 2013; Oden, 2006). However, end users and customers can only experience the working cycles of already prepared simulation models. They cannot directly design and test new products (Tao et al., 2019). To capture innovative ideas, users should be able to develop and test a new product simulation model while varying and selecting component parameters from a catalog according to application and needs (Goury, 2018; Mohammadi, 2019; Schmit, 2016). However, end

104  Manouchehr Mohammadi et al. user and customer choices are limited by the traditional use of simulation. This is because these simulation methods may not replicate the physics of actual products, or they do not allow a broad selection range of required parameters. However, a multibody-​ based real-​ time simulation model, with its surrounding environment, gives end users and customers the opportunity to experience real-​world operations. These real-​time simulation models are physics-​based models that include solutions to the equations of motion. The experiences and customer feelings offered by these simulations can be beneficial in the product development phase of B2B activities. Real-​time simulation had been employed in multibody systems in various applications such as aviation (DuVal, 2001) and automotive (Dede, 2014; Tavernini, 2009). The hours of work needed to develop a model without any guarantee of achieving useful results is the biggest impediment to extending the use of real-​time simulation (Quesada, 2016). Efforts have also been made to develop customizable/​generic real-​time simulation models that are simple, quickly adapted, and modifiable by end users and customers (Mohammadi, 2019). For instance, Kaikko developed a simulation model to help find electric drive solutions for industrial vehicles exploring technical aspects of real-​time simulation (Kaikko, 2015). Steele also studied a generic model, applicable to several systems, by focusing on the simulation information (Steele, 2002). From a business point of view, reducing build-​up/​ manufacturing time is desirable.Therefore, a number of researchers have worked on improving the efficiency of the generic models to decrease manufacturing times in various areas such as logistics manufacturing (Wy, 2011) and drivetrain modeling (Zhao, 2013). The technical-​business aspects of a B2B joint simulation using customizable/​generic models through real-​time simulation have not yet been discussed. To fill this gap, this study provides a roadmap and details the technical-​business aspects of building the customizable/​generic product of a B2B joint venture through real-​time simulation. It could help to establish a balance between the needs of different partners in a B2B activity to achieve better success.

9.3  Methodology: assembling the joint simulation models 9.3.1  Developing customer-​oriented B2B products Two reasons to use joint real-​time simulation in product development are to minimize the need for physical prototype testing and to better involve users in the development process. Adding the user input increases the likelihood of manufacturing products that are customer-​oriented (Karlberg, 2013; Xu & Li, 2013). Traditionally, B2B product development has relied on the use of digital tools, such as design software, by the development team without much input from users. In fact, there is typically no clear connection between the digital tools and the target users.

Implementing a joint simulation model  105

Figure 9.1 The traditional and proposed product development approaches in a B2B venture: (a) traditional and (b) proposed joint real-​time-​simulation-​based product development.

Figure 9.1(a) illustrates the lack of user feedback typical for this traditional process path. The new product development concept explored in this study introduces a direct connection between the users and product development. This connection can be accomplished by using the digital tools as the bridge between product development and the users by including user experience in the model (see Figure 9.1(b)). 9.3.2  Preparation of B2B parameterized real-​time joint simulation model Users can participate early on in the product development process by helping to parameterize product simulation models to make them adaptable. As development progresses, they can vary and select component parameters, from a range of allowed values, that best meet their application and needs and then run simulations to test the resulting product behaviors. The product that results from this user-​based product development approach should provide significantly improved customer value. This parameterization approach can work even better and provide even more customer value when applied to a B2B joint simulation model that has been developed by two manufacturing companies supplying complementary equipment for a particular industry. For an industrial vehicle, there can be choices related to tire type and size, hydraulic forces, engine output, tractor parts, forestry machine parts, excavator, forklift, etc. Users can readily select their intended types and construct the model. Consequently, based on parameterization concept, an industrial vehicle can possess different assemblies with different functionalities. Real-​time simulation methods can demonstrate these various combinations of machines in the real world. End users and customers, in turn, can decide which combination best fits the requirements of the job.

106  Manouchehr Mohammadi et al.

Figure 9.2 Assembling two models, each with various component/​specification options, to construct a joint simulation model –​the check marks in the figure show which types were selected for this particular simulation.

User Interface

The Script (Python Script)

Real-time Simulation

Figure 9.3 Parameterization procedure.

9.3.3  Combining parameterized models and the optimized model Figure 9.2 demonstrates how a joint simulation model can be constructed by combining two separate real-​time simulation models. Each model can comprise a number of different component types or specifications. Varying these sub-​model parameters makes it possible to extract an optimum configuration that is fully customer-​oriented. Determining optimum components and specifications is important. Many scholars have been working to define optimum models and methods via simulation (Alfieri et al., 2015). A well-​executed simulation model optimization makes it possible to find the appropriate balance between technical and economic issues. Simulation optimization helps a manufacturer produce efficient products with less effort, which consequently reduces the cost of manufacturing (Fleischer & Krauße, 2013; Fu et al., 2005). A user-​friendly interface can be designed to help users select their intended product configuration. Using a script, the selected configuration can then be implemented in the real-​time simulation software. In fact, the written script acts as a bridge between the user interface and the real-​time software. Figure 9.3 illustrates the concept of parameter implementation in real-​time simulation software. For better user interaction,visuals or Excel sheets can be used to provide options to end users and customers to select among the different models, components, and sub-​components.This can be done via an advanced programming interface

Implementing a joint simulation model  107

Figure 9.4 A user interface constructed in Excel –​users can easily select their intended parameters/​specifications.

Figure 9.5 (a) The harvester crane system showing the crane mounted on the truck trailer, (b) the tractor with its trailer.

Figure 9.4 shows an example of a user interface constructed in Excel. As mentioned earlier, users can simply select their intended parameter/​specification and implement it in the simulation model.

9.4  Joint simulation of industrial mobile machines This chapter introduces the joint simulation model developed by combining the models for a forestry tractor system and a harvester crane system manufactured by two different case companies. Three parties collaborated in its development: the forest technology company, the tractor manufacturer, and a Finnish simulation technology company. The tractor system is based on the Valtra N tractor. The harvester crane system is based on the Kesla 6H. Figure 9.5(a) shows the harvester crane system. It comprises a truck, the crane, and a trailer. Figure 9.5(b) shows tractor and tractor trailer system. The joint simulation involves the modeling of the systems, components, and hydraulic circuits of the two different product systems. Simulation software, developed by the Finnish simulation technology company, was used to

108  Manouchehr Mohammadi et al. construct the real-​time joint simulation model. In the software, the equations of motion for the simulation models were constructed based on the semi-​ recursive method (Slaats, 1991) and were solved using the Runge–​Kutta time integration scheme (Yang, 2015). The hydraulic circuit systems were modeled using lumped fluid theory (Watton, 1989). 9.4.1 Technical-​business challenges of joint simulation/​joint simulation challenges This section details the technical-​business challenges of the joint simulation of the tractor and crane models. The primary technical challenges were: •

choosing the appropriate parameters from the joint simulation models to be provided for user selection • establishing a feasible range of parameters from which the end users and customers can request changes • testing joint-​model stability and joint simulation models over the range of parameters • developing a new optimum simulation model out of the tractor and the crane models • carrying out the user selection and testing of the optimum simulation model. According to Jääskeläinen (2017), varying factors motivate different actors toward joint product development collaboration. The common motivators included business success and cost savings. Good motivation, clear goals, and an understanding of the benefits of collaboration from all parties involved are necessary for successful joint simulation modeling. Exercising the simulation models, the participating companies will learn more about issues that arise and how flexibly their solutions can fit together (e.g., the tractor and the trailer combination). For example, consider a simulation model with two parameterized specifications and three types for each of the specifications. There would be six different combinations for this simulation model. Users can assemble models by selecting different parameterized specifications.The issues found can help to develop better solutions and synergy between products. Also, these benefits can also lead to advantages for the customer (better products), which can lead to more business benefit. There may be challenges involved in implementing joint modeling and achieving business benefit. A lack of resources is the first issue usually faced. To build the models, companies need to bring on the requisite modeling expertise. Fitting together two different simulation models can be difficult. Bringing on these resources to get the job accomplished is an investment. Furthermore, support is needed from decision-​makers to get the project budget and resourcing approved.

Implementing a joint simulation model  109 If an R&D manager needs support from a superior, they must present a cost-​ benefit analysis that can demonstrate the simulation efforts are going to result in a benefit to the business. Costs are easy to estimate but the future benefit from a business point-​of-​view is much harder to justify. The monetary value of benefits is often qualitative in nature (Boardman, 2010). Another challenge is that there are two or more companies involved, so they must both share a mutual understanding of the possible benefits offered and the effort needed. Establishing an appropriate time-​ based strategy or schedule is always a challenge in any collaboration between multiple parties. In general, each party focuses mainly on its outcomes. For instance, each manufacturer will strive to improve its R&D (Nelson, 1986), its patent portfolio (Adams, 2001), and its process development (Mansfield, 1995). The priority objective for collaborating universities is scientific research. Interactions between manufacturing companies and universities lead to information exchange and the publication of joint research articles (Boardman, 2009). Providing industry funds to accomplish an industry-​academia project will increase collaboration, but it will also result in higher industry party expectations (Bozeman, 2001). Gulbrandsen and Smeby showed that researchers with industry grants are more effective at accomplishing, which is beneficial for both parties (Gulbrandsen & Smeby, 2005). 9.4.2  Real-​time joint simulation solutions 9.4.2.1  Selection of optimal parameters In the forestry industry, the truck and the trailer are designed with a specific number of optimal parameters. These parameters fall into different categories: hydraulic parameters, pulling forces, physical specifications (e.g., trailers’ mass, center of mass), etc. Many parameters included in the truck and trailer simulation models are based on the needs of end users and customers. Concerned B2B companies further shortlist the chosen parameters by checking their feasibility in the individual simulation models and the real world. In the discussed case example, as shown in Table 9.1, optimal parameters are braking torque of the motor, number of rear tires, boom length, and cylinder diameter. 9.4.2.2  Optimal range of parameters As Table 9.1 demonstrates, each parameter has maximum and minimum values/​ numbers. For instance, the maximum and minimum values for the boom length of the forestry vehicle model are 4.35 m and 5 m, respectively. If these parameters are not optimized appropriately, some of the joint models with parameterized specifications/​optimized components can fail. For example, consider a joint model with the maximum boom length and the minimum cylinder diameter. If the cylinder diameter is not adequate to

110  Manouchehr Mohammadi et al. Table 9.1 Major parameterized components/​specifications for the two case models –​ each has a certain number of values that users can select The tractor Braking torque for the motor (Nm)

Number of the rear tires

The forestry vehicle 5,000 15,000 25,000 35,000 40,000 2 4

Boom length (m) Cylinder diameter (mm)

4.35 4.55 5 1.105 1.155 1.195 1.205

provide the required hydraulic force, the joint model will not operate properly. In any parameterized simulation model, there are a specific number of combinations, which have a probability of a failing, that are called “critical combinations”. The critical combinations include a maximum value for one component/​specification and a minimum value for another directly or indirectly related component/​specification. Tractor and crane simulation models are tested at these maximum and minimum specification values. 9.4.2.3  Joint-​model stability Another challenge is to calculate the type and place of the constraint between the tractor and the trailer (see Figure 9.6). The joint type and its distance from the two models should be calculated precisely because it plays a crucial role in joint-​model stability. The joint type should restrict undesirable degrees of freedom and simultaneously allow all intended movement directions. In this case, the modeling experts decided to use spherical joints to allow independent joint motions in all directions. A spherical joint drives a point from the trailer on its corresponding point from the tractor without constraining in its orientations. In addition, to overcome any instability and dispose the distance calculation for point A, two frog legs are designed to increase the stability of the main-​ booms, the cabin, and the trailer. As Figure 9.7 demonstrates, the forces acting on the frog legs are supplied by two hydraulic cylinders that sit on the two sides of the frog legs. 9.4.2.4  Feasibilities of joint simulation combinations Using the number of parameters from Table 9.1, a number of possible combinations of joint simulation models can be modeled. Critical joint simulation models are tested in this case study for feasibility and functionality. In this way, end users and customers can select the components/​specifications of joint simulation in the provided range according to the application or needs of

Implementing a joint simulation model  111

Figure 9.6 The location of the connection spot (Point A) between two models.

Figure 9.7 Frog-​leg stabilizers to prevent the main-​booms, the cabin, and the trailer from falling down to the right and to the left.

the B2B product and test the self-​specified innovative product using different combinations. As mentioned earlier, among several parameterized combinations, there can also be infeasible parameterized simulation solutions (critical combinations). The B2B venture product development team must identify which solutions are not manufacturable or will not operate properly in the real world.

112  Manouchehr Mohammadi et al. 9.4.2.5  User selection of parameterized model/​user designing and testing of simulation model End users and customers can assemble different combinations of parameters in the joint simulation model and find the best optimal combination as per needs. Figure 9.8 schematically shows how these models can be joined. For the tractor model, e.g., a maximum motor braking torque = 15000 Nm, six tires, and four gears were selected. For the forestry vehicle, the boom length = 5 m, piston diameter = 125 mm, and cylinder length = 1,155 mm were selected. These optimal types can be provided to the end users and customers on the simulator screen. Virtual reality and augmented reality tools can also be integrated with the simulator driven real-​time simulations to tightly engage users. In the case of an infeasible simulation solution, end users and customers can report to the product development team. They have an opportunity to rectify their choices and specify a new feasible joint simulation model. Joint simulation efforts help to develop a closer communication between the end users and customers as well as product development team of a B2B venture. 9.4.3  Collaboration benefits and issues on the alliance In the case study discussed, the joint model can be divided into two major sub-​systems. The puller sub-​system, the first, is related to driving the entire vehicle. The second is the trailer and cranes. In the joint simulation effort, the tractor system is the first sub-​system and the harvester crane system is the second sub-​system. Modeling for each sub-​system is the responsibility of the corresponding experts. These subsystems can be interconnected to construct a joint simulation-​model (see Figure 9.9). Each sub-​system can be designed and developed separately. All sub-​systems will be assembled and prepared in the simulation model. Close interaction is needed between parties to define critical combinations. Depending on the objectives, the collaboration between parties may take on many forms including, for example, research contracts and monthly meetings. However, meetings between industrial and educational parties usually will occur informally, and their outcomes cannot be easily measurable (Hagedoorn et al., 2000). Moreover, goals for each involved party can often differ. Some parties usually have more research-​oriented scientific goals, while others have very clear-​cut business-​related objectives. This can lead to a situation where companies are not committed to the collaboration, even though the benefits are considerable (Kiron, 2017). Another benefit of these collaboration situations is taking advantage of the experts and tools of the different parties involved and finding mutual interest areas. In the case described in this chapter, the university and the company partners had extensive open dialogue. Solving the technical modeling case was in the common interest of all collaborators, and the modeling was straightforward. The parties had a signed research agreement detailing how these results could

newgenrtpdf

Max. motor braking torque

4.35 m

1,5000 Nm

4.55 m

Boom length

2,5000 Nm

5m 4

No. of tires

3 No. of forward gears

A joint simulation model

120 mm

Piston diameter Kesla forestry vehicle

125 mm

4 5

1,105 mm 1,155 mm

Cylinder length

1,195 mm

Figure 9.8 Schematic of the procedure on how two models can be assembled and joined together.

Implementing a joint simulation model  113

Valtra tractor

115 mm

6

114  Manouchehr Mohammadi et al.

Figure 9.9 The process of constructing the joint simulation model comprising several sub-​systems –​sub-​systems are specified using the parameterization technique.

Implementing a joint simulation model  115 be used, so there were no concerns about the intellectual properties of the possible new solutions. Having a written agreement on the utilization rights of the results is important, and this agreement should be made between the collaborating organizations before the collaboration begins. In the described joint simulation case, the involved companies gained some understanding regarding each other’s product. The joint simulation model offered a platform where these two companies could learn and discuss each other’s products and joint venture issues. The communication bridges built during this collaboration will enable more thinking and discussion in the future with the potential of introducing new mutual business collaboration ideas. When companies start to plan possible cooperation in simulation modeling, they should be aware of the typical challenges related to the management of such collaborations. Lack of resources (money, time, and simulation experts) are a common issue in these kinds of development projects. Additionally, a common vision of simulation modeling goals between the collaborating parties should be clear.The simulation teams should also have strong support from their management. When both parties have a clear understanding of the challenges, there is a better opportunity to actively plan for suitable means and resources to avoid common pitfalls. To set common goals and avoid gaps in communication, collaborating teams should have common workshops, monthly meetings, discussion platforms, etc. This way, the sharing of information between collaborators will be open and timely. Understanding the motives and goals of each party helps to set the goals for the joint simulation effort. It is also easier to spot potential problems and react in a timely manner. Deeper collaborations can also help to identify possible long-​term business opportunities and benefits (related to customer value and the utilization of the joint models for customer interaction).

9.5  Conclusions In this paper a joint real-​time simulation model of a combined tractor and harvester system was developed and the technical-​business aspects of joint simulation were discussed.The model is composed of two different simulation models from two companies: a forestry company and a tractor manufacturer. The joint simulation model was designed to be flexible using parameterization. Table 9.2 summarizes the challenges faced and makes recommendations. Collaboration between companies provides opportunities to take advantage of input from skillful experts in different fields. Furthermore, universities are able to convey updated knowledge and cutting-​edge ideas to industries. However, projects established with teams comprised of people from different companies present a number of challenges that should be taken into consideration. Cooperation with customers can help designers to minimize the challenges and eliminate the barriers in different design and manufacturing phases.

116  Manouchehr Mohammadi et al. Table 9.2 The challenges found and recommended solution procedures Target

Challenges

Possible solutions

Technical aspects

-​ Developing an optimal joint simulation model out of two (or more) sub models that have been optimized for their environment. -​ Locating the connection point between the sub models -​ Employing a parameterization technique to offer different model combinations -​ Establishing the functionality of all joint-​simulation model combinations -​ Communicating fully and at the right time with all parties -​ Defining how the modeling leads to customer value -​ Determining modeling costs -​ Deciding how to use the new models in the future

-​ Optimal sub-​system models were developed with the collaboration of the experts from all parties. -​ The experts held frequent meetings to be made aware and to consider the overall situation to prevent problems and rework arising from the numerous critical combinations. -​ The critical combinations were tested and analyzed to ensure the functionality of all possible combinations. -​ Close interaction with customers was maintained to keep awareness of their needs throughout the work to develop customer-​oriented models.

Business aspects

-​ Common goals were established in workshops involving all parties. -​ Efforts were made to increase the common understanding of business benefits. -​ Support from management was solicited and obtained.

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Part III

Capturing customer value and user experience

10  Implementing digital twins to enhance digitally extended product-​service systems Ilkka Donoghue, Lea Hannola and Antti Sääksvuori

10.1  Introduction The strategic driver for manufacturing companies of increased growth and improved margins is causing them to focus on developing new services. The traditional approach of offering services as a transactional add-​on to products or providing spare parts has been replaced with a systematic approach. The servitization (Kohtamäki et al., 2018) of products is transitioning to a holistic Product-​Service System (PSS) (Baines et al., 2007). PSS places demands on Business to Business (B2B) manufacturing companies, but also presents new business opportunities for them. The challenge they face can be divided into two areas: (1) engaging with the customer over the customer lifecycle and (2) moving from an asset-​based view to better understanding the customers’ business and value proposition. Also, the business opportunities offered by this approach include (1) establishing deeper relationships over the customer lifecycle and (2) facilitating the outsourcing of business processes to a B2B manufacturing company. The introduction of information management technology has added another dimension to building extended digital service offerings that are based on data or knowledge about how the PSS is being used or should be used to establish a digitally extended PSS. To move from an inside-​out view of transactional product sales to providing continuous customer lifecycle service, a B2B manufacturing company must have periodic touchpoints along the lifecycle.These touchpoints can be realized by collecting lifecycle data for delivered products (assets) and then simulating the behaviors of these assets in a virtual environment (Grieves, 2019). Product Lifecycle Management (PLM) offers a way to ensure that the digitally extended PSS offered and the asset delivered can be managed systematically from the design phase all the way through the operations phase and to its end of life (Donoghue et al., 2017). However, it is not clear how this can be accomplished via PLM. One approach that has been proposed is the implementation of a digital mirror of the real-​world product (Grieves, 2006). The traditional goal of PLM has been to provide a management system that enables continuous product management over the product lifecycle with cross-​functional responsibilities

122  Ilkka Donoghue et al. within the organization (Stark, 2006). However, this results in different parts of the organization taking responsibility for different phases of the lifecycle, which typically results in a situation where lifecycle phases are managed and executed separately. For example, Product Development owns real-​time simulation information, PLM design data, and processes. Delivery owns marketing, sales, and supply chain data and processes, and Services owns assets, services, and service data and processed based of the Internet of Things (IoT). In this chapter, digital twins are used to facilitate information-​based services using information from the real-​time simulation of the digitally extended PSS and data collected from assets in operation. To enable alignment between the digital twin and the assets, connectivity should exist between the two. This can be accomplished if the Industrial IoT transfers data and information to provide insight into the current status of the PSS so it can be optimized to achieve the desired performance outcomes. The study reported here was carried out to better understand the current situation for the case companies in these different areas and to collect evidence of existing digital-​twin-​based services that are information intensive. Learning how the subject companies use this insight to build better real-​time simulation models that can be applied to services was a second area of interest. The work is based on semi-​structured interviews with eight B2B manufacturing companies to examine information-​based services, simulation models, and IoT strategies. This chapter in structured and divided into the following sections. The first is the review of the related published research, followed by a discussion of the research methodology and approach for data collection, leading into the presentation of results for the research based on the interviews. Finally, general discussion and conclusions drawn are offered along with suggestions for further research directions.

10.2  Related research Baines et al. (2007) regard a PSS as a special case of servitization where the value of the product to the customer is increased with the addition of services. This establishes a concept of focusing on an outcome based on sale-​of-​use rather than sales of the product alone. Goedkoop et al. (1999) define the key elements of a PSS as (1) a tangible physical commodity manufactured to be sold, (2) an activity done for or on behalf of the customer that has an economic value based on a commercial contract, and (3) a system that is a collection of elements and the relationship formed between the product and associated services. Therefore, the customer pays for the sustainable use of the product, which is enabled through the integration of services across the lifecycle. This results in a PSS that is a system of tangible products and intangible services. Mont (2000) extends the definition to include information or data-​based services and connectivity through the addition of supporting networks and infrastructure. Mont (2000) also includes customer satisfaction and environmental

Digitally extended product-service systems  123 impact reduction along the PSS lifecycle. Therefore, the focus of PSS is to support business models that provide periodic customer touchpoints following initial tangible product and delivery service sales. This results in a Long Tail Business Model (Gassmann et al., 2014) where the focus is on B2B manufacturing companies that sell small numbers of systems, but have a large portfolio of products and services that makes a delivered solution (asset) possible and can offer the services needed to maintain the solution across its lifecycle. This also provides an opportunity, via continued engagement with the customer, to sell continuous services over the lifecycle of the PSS and offer sustained value to the customer. Sääksvuori (2019) has shown that a PSS can be digitally extended to include digital services and data. It can be divided into different domains and several layers. He also suggests that digitalization changes the way product and services are designed, which results in more value for the manufacturer and customer (Sääksvuori, 2015). Digitalization impacts the electromechanical composition of the PSS and how the PSS is manufactured. It enables new ways to optimize the supply chain and provides a periodic touchpoint over the customer lifecycle that fosters a continuous customer–​provider relationship. Grieves (2006) introduced the concept of the digital twin. The digital twin has been part of the PLM vision, and it is also seen as a key element to the development of new digital business models for business growth (Donoghue et al., 2019). Grieves (2019) extended his previous concepts with the introduction of a dual Smart Connected Product System (SCPS) that can exist in the real world and the digital world at the same time. Grieves (2019) argues that the physical twin and the digital twin are connected continuously throughout the lifecycle with operational data being collected from the physical twin and sent to the digital twin to verify that the SCPS is operating within its performance parameters or identify when service activities should be done to sustain process performance as agreed upon with the customer. Alternatively, the digital twin can use multibody-​physics-​based real-​time simulation (de Jalon & Bayo, 1994) to model the anticipated behavior of the physical twin in advance or to transmit to the digital twin information such as software upgrades, setup changes, or operational adjustments. Grieves introduced three digital twin definitions that are related to the PLM lifecycle concepts introduced by Stark (2006), Donoghue et al. (2017), and Grieves (2006). These new digital-​twin definitions are (1) the Digital Twin Prototype (DTP), used for the development of the product and all its variants, (2) the Digital Twin Instance (DTI), a digital copy of the instances delivered to the customer, and (3) the Digital Twin Aggregate (DTA), a collection of all the DTI used to aggregate information about the versions and variants delivered to gain insight about their operational and service correlations (Grieves, 2019). The conclusions given by Donoghue et al. (2019) suggest that to successfully implement more digital-​twin-​based operations, a B2B manufacturing company must find a balanced way to collect data from the assets and learn more about

124  Ilkka Donoghue et al. how the digital twin is used to verify new services, and therefore minimize the risk of collecting so much data that it cannot be aggregated, assessed, and used by the business to create value. The connectivity of SCPS (Grieves, 2019) or digitally extended PSS is critical to building value and developing new digital business models where the customer and B2B manufacturing company can cooperate through periodic touchpoints along the lifecycle (Donoghue et al. 2019). The result is a way of working in which time, location, or personnel do not hinder the cooperation. Verdugo et al. (2017) proved that PSS lifecycle value increases when the IoT is used to offer smart and digital services.The identified benefits were operational efficiency, risk minimization, sustainability, and value.To understand the role the IoT has in supporting PSS-​driven business, Gubbi et al. (2013) defined the IoT and its role as follows. the Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework, developing a common operating picture for enabling innovative applications. (Gubbi et al. 2013) Basirati et al. (2019) believe the IoT encourages the realization of new ideas, and that it can simplify PSS development and provide new digital-​business models, closed-​loop lifecycle management, and services provided via the IoT for PSS. These opportunities can also be extended to the SCPS (Grieves, 2019) for B2B manufacturing companies collecting sensor data from their installed base (asset) for use in analytics resulting in monitoring services, for example, and providing autonomy of the SCPS or PSS. McEwan and Cassimally (2013) state that connectivity is based on the IoT and can be thought as being dependent on the following aspect. IoT = PSS + integrated sensors, controllers, actuators + internet (+ digital twin). McEwan et al. (2014) did not focus on what happens on the other side of the internet once the data were collected. In their model, they did not introduce the digital twin concept. This modified statement is based on Grieves (2019) concept for the SCPS to include digital twins. Grieves (2019) and Donoghue et al. (2019) both highlight that the IoT is essential for the connectivity and transmittal of bi-​directional data. Grieves (2019) and Donoghue et al. (2019) agree that the smart product, PSS, or SCPS also incorporate smart to the concept in both the physical twin and digital twin forming a foundation for an intelligent, connected digital twin. Smart products and smart digital systems are not new concepts, but connecting them upgrades an isolated smart system into an SCPS and a digitally extended PSS. The intelligence difference is similar to a factory robot that is managed by a central manufacturing execution system and internal local intelligence that can shut down the robot when it malfunctions versus an Unmanned Aerial Systems (UAS) that has edge machine learning and is connected to other UAS’s and operational systems that

Digitally extended product-service systems  125 use Artificial Intelligence (AI) managed by a centralized intelligent digital twin (Grieves, 2019).

10.3  Research methodology Research data were collected between October 2017 and March 2018. From the data, eight companies were selected based on how well their situation aligned with the goals of the research. For these companies, qualitative research integrating theoretical and empirical case study data (Yin, 2013) was carried out through semi-​structured interviews. Additional data from four other companies that had been collected from 2011 through 2019 were also included. For these four case companies, design research methodology (Hevner, 2007; Hevner & Chatterjee, 2010) was applied, and data were also collected using semi-​structured interviews.This approach was selected to better understand the start state of the case companies and gain insight into which were able to transition to information-​based services, digitally extended PSS, or SCPS through the adoption of digital twins. The research process included two phases: (1) mapping the status of existing services and (2) examining changes over time as each company moved towards information-​ driven services or digitally extended products. The eight core case companies are all B2B manufacturing companies that deliver complex PSS having long operational lifecycles, which are either delivered configure-​to-​order or engineering-​to-​order.

10.4  Results The results from the interviews were collected and organized into three categories that define the capability of the companies to create business value from digitally extended products, information-​based services or Smart-​Connected-​ Product-​Systems (SCPS).The method to categorize company status is based on the Sääksvuori (2019) digitalization process, which is divided into three areas of interconnected domains that facilitate digitalization. Table 10.1 is divided into four columns. The first Case Company column lists the anonymous case companies. The second, Business Process Digitalization, shows the state of core business-​process digitalization, information, and Information System (IS) architecture. The third column is the Connected Smart Product Systems, where data collected from the physical and digital twins are used for simulation and performance verification. It is also used to send information back to the physical twin for performance adjustments and/​ or improvements. Finally, the fourth column lists Digital Services, which are divided into three Tiers. Tier 1 is Remote/​Monitoring Services, Tier 2 is PSS optimization based on digital twins, and Tier 3 shows services offered to customers who are not traditional clients (information as-​a-​services). The goal of Table 10.1 is to quickly visualize the state of business capabilities to better understand how to leverage benefits from digitally extended products and gain insight into how far away they currently are from the

126  Ilkka Donoghue et al. Table 10.1 Digitalization framework for digitally extended PSS and SCPS Case Business process digitalization company

Connected smart PSS

Digital services

Points

Integrated Digital Analytics Data Digital Tier 1 Tier 2 Tier 3 Total business interfaces for collection Twin process steering RO SD MN VA QE VI MT VL Max

B(3) B(3) D(1) A(4) A(4) A(4) B(3) B(3) A(4)

D(1) C(2) D(1) D(1) B(3) A(4) A(4) A(4) A(4)

D(1) D(1) D(1) D(1) B(3) B(3) B(3) B(3) A(4)

D(1) D(1) D(1) B(3) B(3) B(3) B(3) A(4) A(4)

B(3) B(3) B(3) B(3) C(2) C(2) C(2) C(2) A(4)

D(1) D(1) D(1) C(2) B(3) B(3) B(3) B(3) A(4)

B(3) B(3) B(3) B(3) C(2) D(1) D(1) C(2) A(4)

D(1) D(1) D(1) D(1) D(1) D(1) D(1) D(1) A(4)

14 15 12 18 21 21 20 22 32

SCPS (Grieves, 2019). The following criteria are used to map the current needs of the case companies. The capital letter (A –​D) signifies digitalization characteristics. The number in the brackets (1 –​4) is used to define the numeric value that each company scores. D(1) indicates that data are not available to support the existence of this business capability. C(2) means the capability is partially needed in more than one but less than half of the business lines. B(3) indicates that the case company needs the capability for more than half of its business lines. A(4) denotes that more than 80% of its business lines still need the capability. Table 10.1 results show that Integrated Business Process in the Business Process Digitalization column is an important need for all case companies scoring between B(3) and A(4). The Digital Interfaces result falls into two groups. The first group includes those with the D(1) score, which means the questions or the interviews did not address this area. The second group companies need to build a foundation to establish integrated digital touchpoints. The Analytics for Steering column results are also divided into two groups.The first four case companies score D(1), and the second four score B(3) indicating a need to implement Analytics to Steering. Again, the D(1) score for the first group means the questions or the interviews did not reveal insight to support the need contain clear questions to uncover this need. The results from Connected Smart PSS falls into the two sub-​areas Data Collection and the Digital Twin. Once again, the results highlight that data collected from the various engagements differ, but they are not as polarized here. For the first four case companies, apart from case company ‘VA’, the Data Collection need is not evident. Data Collection focuses on using the IoT to collect data from the assets, and scoring D(1), data from selected companies RO –​MN did not reveal this need. However, the B(3) indicates that data

Digitally extended product-service systems  127 collection is a need for case company ‘VA’. Nevertheless, the web pages for these companies claim that they offer these services to customers with data collected from the assets. Cases Companies QE to VL scored between B(3) and A(4) also showing that Data Collection is a need, and they are already developing and offering this capability to customers. The sub-​column Digital Twin result indicates that all the case companies have needs in this area with results ranging from C(2) to B(3). This results for this area is subject to how the case companies define a digital twin.The first four companies recognize the need and are implementing digital twins in the DTP form by applying real-​time simulation and PLM. The other companies operate with digital twins of various maturity levels that include PLM and simulation elements.They lack the connectivity between the digital twin and the physical twin that Greives (2006) defines for SCPS where information is transmitted to the physical twin or where data collected from the asset is used systematically in real-​time simulations. And based on maturity level, the definition of the digital twin can vary among companies. The last column Digital Services is divided into the three sub-​headings Tier 1, Tier 2, and Tier 3. Tier 1 is Remote/​Monitoring Services. The case companies from ‘VA’ to ‘VL’ show a need for these services C(2) –​B(3), but ‘RO’ to ‘MN’ do not show this need. The company web pages of all the reviewed companies show they all offer these type services and there is ongoing work to develop these business capabilities as a customer offering. Tier 2 is PSS optimization with the use of digital twins. This is seen as a business opportunity for companies ‘RO’ through ‘QE’ and ‘VL’. However, this opportunity emerges through the discussion in the interviews and was not something that the case companies identified themselves upfront before the free discussions occurred. For companies ‘VI’ and ‘MT’, the research did not reveal evidence of a need. Finally, Tier 3 includes services offered to customers that are traditionally outside of the traditional customer base. The data from both research areas do not suggest recognition of this need. Based on the interviews and discussions with the different case companies, this does not seem to be area were any activity is ongoing. The result D(1) indicates that the need cannot be identified from the existing data, however, it does not mean that the areas are not needed or that ongoing activities are not present at this time. The results for each case company from Table 10.1 can be averaged against the maximum points to reveal the relative situation for each company as shown in Table 10.2. The over-​all average for the companies is 55.86% with the lowest average being 37.50% and the highest, 68.75%.This result could indicate that the questions and the collected data vary because of the engagement types. Data collection for the first four companies (‘RO’ to ‘VA’) was based on focused questions around real-​time simulation, whereas, for the other group (‘QE’ to ‘VL’), data collection was based on Design Research, and the authors were actively involved in and gained better access to the internal goals for a longer period of time.

128  Ilkka Donoghue et al. Table 10.2 Case company averages based on the adapted framework Case company

Total points

Average (%)

RO SD MN VA QE VI MT VL Average

14 15 12 18 21 21 20 22 32

43.75 46.88 37.50 56.26 65.63 65.63 62.50 68.75 55.86

10.5  Discussion and conclusions The main contributions of this chapter are addressing the shift beyond the multibody-​ physics-​ based real-​ time simulation to digital twinning. After establishing the presence of this shift, the chapter attempts to discover the link between product-​service systems and the digital twins. This link exists in the PLM vision (Stark, 2015) (Grieves, 2006) (Donoghue et al., 2017) and PSS research (Baines et al., 2007). The objective to find a theoretical base for digitally extended PSS and SCPS exists based on the related research from Grieves (2019) and Donoghue et al. (2019) and Stark (2006) and the results of this chapter. The framework that was applied from Sääksvuori (2019) to test the shift towards a digitally extended PSS and then towards and Smart-​ Connected Product System (SCPS) cannot be proven without doubt based on the research data. It did not include or support the needs of all elements presented in the framework. However, there is strong indication that the case companies are moving towards a digitally extended PSS and/​or SCPS. There is also evidence in Sääksvuori (2019) that one of the companies is clearly moving toward this strategy where the extended product can be divided into the hardware, software, product lifecycle services (PLS), asset efficiency services (AES), Digital Services, and Data. The recommendations for business and entrepreneurs are to take a holistic view of how the different areas presented in this chapter are related and how they must be approached through continuous development roadmaps implemented consistently to achieve a business model built on SCPS. Businesses must focus on getting their internal business processes digitalized before they can move to digitally extended products and SCPS. Academia has a role in researching this area further and discovering more established and verified concepts that businesses can apply in digitally extended PSS and SCPS. Clearly, most companies are developing the different aspects in silos, and a strategy and/​ or roadmap is often missing.

Digitally extended product-service systems  129 Real-time connectivity for data and information exchange

Connected Real World Solution

Connected Virtual World’s Digital Twin IoT Platform

Dynamic physics-based real-time simulation of the physical assets, its environment, processes and or systems used for various purposes over its lifecycle The digital twin is connected to its real-world companion over the lifecycle and maintained in tandem with the physical version forming a Smart Connect Product System (SCPS).

Figure 10.1 Intelligent PSS.

Finally, all the related research presented looks at the same phenomena from differing vantage points. Figure 10.1 proposes a model that companies can use to understand the logical elements when integrated to form an intelligent Product Service System (iPSS) that combines the physical and digital twin with an IoT platform for connectivity and the integration of Machine Learning produce a digital twin capable of simulating the real world in real time or faster than real time to maintain solution performance and business sustainability (Figure 10.1). The conclusions that can be drawn from this chapter are that the digital twin is an integral part of the future success of a B2B manufacturing company and that value can be achieved once the physical twin and the digital twin are connected and data/​information can flow in real time or close to real time. Because this transformation depends on digital information and company data, success depends on integrating IS systems with the SCPS or iPSS. To achieve this, companies must develop new capabilities to change the nature of digitally extended PSS they offer. An obvious step for the digitally extended PSS is to include AI either for human-​to-​system interaction or for Machine-​to-​Machine (M2M) operations. In these cases, the digital twin can be used to simulate reality faster than the real time giving insight into what could happen in time to take corrective actions before risks materialize.

Acknowledgments This study was part of the DigiPro project and received funding from Business Finland and the SIM research platform (Sustainable product processes through SImulation) at LUT University, Finland. The authors would also like to thank all the involved companies for the collaboration.

130  Ilkka Donoghue et al.

References Baines, T. S., Lightfoot, H. W., Evans, S., Neely, A., Greenough, R., Peppard, J., … & Alcock, J. R. (2007). State-​of-​the-​art in product-​service systems. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 221(10), 1543–​1552. Basirati, M. R., Weking, J., Hermes, S., Böhm, M., & Krcmar, H. (2019). IoT as PSS Enabler: Exploring Opportunities for Conceptualization and Implementation. In Paper Presented at the Twenty-​Third Pacific Asia Conference on Information Systems (PACIS 2019), Xi’An, China. Donoghue, I., Hannola, L., & Papinniemi, J. (2017). Product Lifecycle Management Framework for business transformation. 24th International Conference on Production Research, July 30–​August 3, 2017, Poznan, Poland. Donoghue, I., Hannola, L., & Mikkola, A. (2019). The value of digital Twins and IoT based services in creating lifecycle value in B2B manufacturing companies. Portland International Conference on Management of Engineering and Technology, 25–​29 August 2019, Portland, USA. DOI: 10.23919/​PICMET.2019.8893904. Gassmann, O., Frakenberger, K., & Csik, M. (2014). The Business Model Navigator. Pearson Eductation Limited, ISBN 978-​1-​292-​06581-​6. Goedkoop, M., Van Halen, C., te Riele, H., & Rommens, P. (1999). Product Service-​ Systems, ecological and economic basics. Report for Dutch Ministries of Environment (VROM) and Economic Affairs (EZ). Grieves, M. (2006). Product Lifecycle Management. Driving the Next Generation of Lean Thinking. McGraw-​Hill. Grieves, M. W. (2019).Virtually Intelligent Product Systems: Digital and physical twins. Complex Systems Engineering: Theory and Practice, 175–​200. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645–​1660. De Jalon, J. G., & Bayo, E. (1994). Kinematic and dynamic simulation of multibody systems: the real-​time challenge. New York: Springer Science & Business Media. Hevner, A. R. (2007). A three cycle view of design science research. Scandinavian Journal of Information Systems, 19(2), 4. Hevner, A., & Chatterjee, S. (2010). Design science research in information systems. In Design research in information systems (pp. 9–​22). Springer, Boston, MA. Kohtamäki, M., Baines,T., Rabetino, R., & Bigdeli,A. Z. (2018). Practices in servitization. In: Kohtamäki, M., Baines, T., Rabetino, R., & Bigdeli, A.Z. (Eds.). Practices and tools for servitization. Springer International Publishing AG: 1–​21. McEwen, A., & Cassimally, H. (2013). Designing the internet of things. John Wiley. Mont, O., (2000). Product Service-​Systems. The International Institute of Industrial Environmental Economics (IIIEE), Lund University, Stockholm, ISSN 1102-​6944. Sääksvuori, A., (2019). Digital and Modular Services as Part of the Offering in Manufacturing Industry. Krios Business Consulting Oy, Espoo 2019. Sääksvuori, A., (2015). Extending Products Digitally. Sirrus Publishing, ISBN 978-​952-​67529-​4. Stark, J. (2006). Product Lifecycle Management –​21st Century Paradigm for Product Realisation. Springer-​Verlag, London, ISBN 1-​85233-​810-​5.

Digitally extended product-service systems  131 Stark, J. (2015). Product Lifecycle Management. In Product Lifecycle Management (Volume 1) (pp. 1–​29). Springer, Cham. Verdugo Cedeno, J., Papinniemi, J., Hannola, L., & Donoghue, I. (2017). Developing Smart Services by Internet of Things in Manufacturing Business. 24th International Conference on Production Research (ICPR 2017). Yin, R., K. (2013). Case Study Research: Design and Methods. Sage, 312 p.

11  The expected benefits of utilizing simulation in manufacturing companies Insights from a Delphi study Kalle Elfvengren, Manouchehr Mohammadi, Ville Kalliola and Lea Hannola

11.1  Introduction In recent decades, simulation has evolved away from being a tool just for experts and mathematicians and towards being an all-​around technique used in a variety of different areas. The resulting increase in the number of users has contributed to the improvement of simulation technology. Today, it is the fundamental tool driving decisions made on design, validation, and testing for both components and complete products (Boschert & Rosen, 2016). Just some of the fields utilizing simulation include health care, marketing, supply chain, military, and manufacturing. Especially within the manufacturing industry, simulation plays a crucial role in improving the design and performance of entire systems and products (Negahban & Smith, 2014). Despite being a critical manufacturing tool, its use has been limited by the complexity of manufacturing systems and the expertise required to efficiently make use of simulation (Benedettini & Tjahjono, 2008). This chapter deals with simulation modeling in the work-​machine manufacturing industry. The Delphi research method was used to discover the views of industry professionals regarding the benefits and business effects of simulation modeling for mechanical product manufacturing in Finnish companies. A key result of the analysis is an in-​depth view of attitudes and expectations towards simulation models based on the opinions of industry experts. The results also provide insight into how the Delphi method is used to identify new business opportunities that simulation models could bring to manufacturing companies. The Delphi method is commonly used in technological foresight studies, but to our best knowledge, analyzing the business potential of simulation activities related to work-​machines has remained relatively unstudied. The following section reviews prior literature on simulation modeling motives in manufacturing.The third section (Section 11.3) describes the research methodology. Next, the results of a qualitative Delphi study are revealed. Finally, the chapter ends with conclusions.

Expected benefits of simulation  133

11.2  Simulation modeling motives in manufacturing Law (2015) divided the term simulation into two different concepts: dynamic simulation and static simulation. The difference between these two concepts is the way they interact with time. Static simulation focuses at a certain point in time, while dynamic simulation is a process that progresses through time. For the purposes of this study, dynamic simulation will be the focus. Robinson (2014) further defined the meaning of dynamic simulation as “Experimentation with a simplified imitation of an operations system as it progresses through time, for the purpose of better understanding and/​or improving that system”. Providing maintenance services is a key part of manufacturing. Once a product has been made and delivered, maintenance ensures that it keeps operating at an agreed upon level in terms of reliability and safety (Mourtzis et al., 2014). Jahangirian et al. (2010) recognize the potential of using simulation as a tool to support maintenance operations, because simulation can simultaneously provide multiple different functions such as maintenance, production, and inventory control (Negahban & Smith, 2014). This kind of preemptive maintenance and process control through simulation can be further improved upon when done in conjunction with a virtual representation of a physical product –​a digital twin. By feeding actual product data to the digital twin, unforeseen situations can be analyzed, and product operation can become more predictable (Kher, 2017). Achieving profit is a high priority for most companies, which makes it crucial to be as efficient as possible in all your business processes. In the manufacturing sector, this means improving production and productivity as much as possible, which is done by utilizing your machines and production systems at fullest capacity while cutting down the periods of inactivity (Bako & Božek, 2016). Technologies such as simulation are excellent tools to use when you are aiming to speed up product design and decrease the overall time spent in development. The exploitation of simulation-​related technologies provides manufacturers the option of testing and validating multiple different product and process configurations within the manufacturing system, which leads to increased overall efficiency (Mourtzis et al., 2014). Gallois (1993) recognized the importance of efficiency within the manufacturing industry, but he also noted that the customer plays a major role.Wortmann et al. (1997) advised companies to embrace rather than avoid customer-​driven manufacturing. They pointed out that cooperating with the customer as well as tailoring a product after customer needs is important. The central role of prototyping and improving production system customization capabilities was also highlighted. Therefore, not only is it important to be efficient and produce quality products, but it is also important to be flexible and respond to customer needs. Klingstam and Gullander (1999) viewed computer-​based aids, such as simulation, as a potential response to increasing market demands. This need for

134  Kalle Elfvengren et al. a dynamic model to balance available resources with customer needs was also noted by Heilala et al. (2010). They recognized that previous planning methods did not meet present needs and that a simulation model utilizing real-​world data was required to meet the needs of the market. By utilizing a dynamic simulation model, they noted that a more efficient balance between customer needs and available resources was achievable. Fei et al. (2017) agreed with Gallois (1993) that customers should be at the core of product design and that customer involvement is incredibly valuable. He points out that to benefit the most from customer involvement, the interaction should begin as early in the design process as possible. By utilizing simulation alongside the digital twin, the communication between customers and other stakeholders can be sped up through the real-​time acquisition and transfer of data. Moreover, issues the customer has had when trying to utilize the previous generation product can be pinpointed, thus generating more customer value. The potential for generating customer value through co-​development was also noted by Mikkola et al. (2014). This type of exploitation of the digital twin is becoming more prevalent within the manufacturing industry, because it has the potential to open new areas of business through services (Donoghue et al., 2018).

11.3  Research method and data gathering process A qualitative Delphi study was carried out to provide the primary data. The Delphi-​method is a well-​known research method traditionally used in forecasting. It is based on expert panel surveys. The surveys promote discussion, because the individuals in the chosen expert group can present their opinions over the course of several survey rounds. A commonly used definition is found in the seminal work of Linstone and Turoff (1975): “Delphi may be characterized as a method for structuring a group communication process so that the process is effective in allowing a group of individuals, as a whole, to deal with a complex problem”. The Delphi method first applied by the Rand Corporation in the 1950s. The characteristics of the traditional Delphi approach are anonymity, iteration, feedback, and consensus. In Delphi-​research, one of the original main goals was consistency, but today this is seldom seen as necessary or even desirable (Okoli & Pawlovski, 2004; Kuusi, 1999). There has also been some criticism towards the Delphi. For example, Sackman (1975) and Baker et al. (2006) argue that the “experts” may not be sufficiently knowledgeable and that their opinions might not reflect reality.The critique has resulted in attempts to reevaluate the validity and reliability of the method (e.g., Rowe & Wright, 2011). In this research, the Delphi method was expected to be effective in forming a common view of professionals related to the benefits and business effects of simulation modeling of mechanical products in Finnish companies. Delphi is a well-​ structured method for collecting in-​ depth views of experts, and documenting the research process is straightforward.The Delphi study is critically dependent on the quality of the knowledge captured. This study benefited

Expected benefits of simulation  135 Table 11.1 The Delphi panelists Company/​business

Position/​expertise

Elevator and escalator industry

R&D team leader Category manager, machines Team leader, research and simulation Head of R&D Product designer, software developer R&D director Developer, drive systems Managing director Project engineer Managing director R&D engineer System engineer R&D manager Simulation engineer Engineer manager Product Development Manager Dynamic simulation engineer Manager, testing & prototypes Simulation engineer

Tractor manufacturer Forestry and material handling technology manufacturer Material handling machinery Digital twin and simulation solutions provider Electrical drive technology solutions Hydraulic cylinder manufacturer Machinery and equipment for production process in the wood industry Equipment and tools, service and technical solutions for the mining and construction industries Forklift solutions

from a wide range of opinions presenting specialist knowledge of simulation modeling in Finnish industrial companies that use simulation modeling to some extent in some of their operations. The 20 experts chosen were product development engineers, software specialists, business managers, and executives (see Table 11.1). The analysis focuses especially on the effects and possibilities of simulation modeling. It examines discrepancies and views, and at the same time tries to find the relevant arguments behind the views. The Delphi process (Table 11.2) took about three months. The goal of the Delphi study was to establish an expert-​opinion-​based view of the effects of simulation modeling on manufacturing in Finland. Based on this assessment, the goal was to increase understanding of new business opportunities in this area and of innovations that may affect the current situation. The Delphi expert panelists included 20 carefully selected participants from the business sector. Panelist commitment was confirmed before sending the first questionnaire to ensure the experts would participate actively in the entire research process. The instructions and summaries of the results of previous Delphi-​rounds were delivered to the members of the expert group via email attachments. The participants responded to the questionnaires electronically using the MeetingSphere internet portal. MeetingSphere is an electronic meeting software that allows participants to enter their opinions and votes on a common internet page. The response time was set to about three weeks per cycle.

136  Kalle Elfvengren et al. Table 11.2 The developed Delphi process Stage of the process

Content and tasks

Deciding on the goals and scope Planning the content of the Delphi rounds Selection of the expert panel 1st round

Planning the research with academic and firm representatives Deciding on the schedule and implementation 4 rounds and joint meeting Identifying the key issue areas of the research Forming the questions List of potential participants Willingness of the participants to engage in the research What are the companies hoping to gain from simulation/​ digitalization? What benefits have emerged or are expected because of the simulation models? What kind of resisting forces are there in the organization against the simulation functions/​efforts? Summary of the responses Forming the 2nd round questionnaire Possibility to comment on the 1st round summary report Prioritizing benefits and resisting forces (change drivers) based on the 1st round results What kind of business-​related effects can be achieved using simulation in the long term? What benefits are there for the stakeholders in the long term and how to measure these business-​related impacts? How to identify customers who want to cooperate and commit? Summary of the responses: ‘force field’ analysis and summary Forming the 3rd round questionnaire Possibility to comment on the 2nd round summary report Prioritizing business-​related effects based on the 2nd round results by using two criteria (cost-​benefit and attainability) Summary of the responses Summary of the 3rd round responses Possibility to comment on the 3nd round summary report Summary report of the whole Delphi process Presenting the results to the panelists Discussion of the results and their utilization Consensus; argumentation of the most controversial issues

Evaluating the results 2nd round

Evaluating the results 3rd round Evaluating the results 4th round Evaluating the results

11.4  Simulation modeling in manufacturing companies: insights from the expert panel The main findings related to the Delphi research are presented in this section. During the first round, the experts expressed their hopes and goals towards the simulation models, which they are now dealing with in their companies. Table 11.3 offers a summary of the expressed comments, which were divided

Expected benefits of simulation  137 Table 11.3 The typical goals the companies are hoping to gain from simulation modeling Area

Goal

For product development

To understand what making a model means in practice and what could be done with the models The better utilization of the opportunities provided by simulation within the context of product development Examining and testing new and more versatile ideas which would be excluded through traditional design process either due to costs or because they are too time consuming Faster comparison of multiple solutions through virtual models Examining what type of simulation model or hardware is required to achieve a reasonable level of results To speed up the product development process while improving quality To develop methods for customer-​oriented product development To find ways to support user interface development and to gather user feedback from the interface concepts at an early stage To improve product development processes so that through real-​ time simulation you can test and verify the control system and gather feedback on the control of the machine from the customer before building a physical prototype The premise is that simulation will shorten the required testing time and improve the communication between different departments when the simulation models are used by a larger group Testing the new machine models, configurations and sub-​assemblies before production Improving the design of the control system The models should be easily configurable for different purposes; sales and marketing will become easier when it is possible to demonstrate different situations with the models To increase the utilization of real-​time simulation and enable a larger group of people from different functions to benefit from it For sales, the goal is to enable the simulation of certain customer cases to provide a better user experience and to verify the functionality of the solution For education, the goal is to make the process of transferring machine models and user interfaces into the simulator faster and easier To be able to enhance application design and preemptive maintenance To apply the embedded real-​time models in service business, for example by using digital twin models in preemptive maintenance control and malfunction diagnostics To enable the use of real-​time simulations alongside IoT-​systems

For customer-​ oriented product development and testing

For other functions

Related to digital twin and IoT

into four slightly different categories: things affecting product development alone, things affecting customer-​ oriented product development and customer interaction (e.g., testing), things affecting other company functions (e.g., marketing), and aspects related to IoT and digital twins.

138  Kalle Elfvengren et al. In addition, the panelists were asked what benefits have emerged or are expected as a result of the simulation models, and what kind of resisting forces are there in the organization against the simulation functions/​ efforts. The answers were summarized and then added to the second-​round prioritization task. In the second Delphi round, the panelists made assessments about the promoting and hindering aspects of simulation modeling in their companies.These forces working for and against the implementation will either favor or hinder the development of simulation as a work tool and the utilization of simulation models.The panelists evaluated the significance of these forces on a scale of 1–​5 (1 = minor significance, …, 5 = very strong significance). Tables 11.4 and 11.5 show the forces for change seen as significant (the average rating >3). The tables formulate a force field analysis. Force field analysis is a widely used managerial tool that can analyze the hurt points of organizational changes and implementations of new procedures (e.g., Ajimal, 1985). In this case, the force field analysis can be used to find out the important aspects to concentrate on if the purpose is to increase the popularity of the simulation modeling procedures within the company. For change to take place, the driving forces must be strengthened, or the resisting forces weakened.

Table 11.4 Benefits emerged or expected because of the simulation models Forces for change

Average

Shorter implementation and testing times because the control system has already been tested in the simulator Can be spared from a physical prototype manufacturing round if the need for change is already revealed in the simulation Benefits for product development by doing virtual testing The test situation in the simulator is not too predetermined and it is possible to find out things that could not be asked/​noticed in advance Products meet customer needs faster/​cheaper Accelerate failure in planning/​testing and thus learning in product development Testing different product variations more systematically and comprehensively, especially from the point of view of usability A better understanding of the machine’s dynamics, especially in the initial dimensioning phase Enabling hardware and control solutions that would be too expensive/​ time consuming to develop without a virtual model Utilizing simulation will shorten the overall time of product development from idea to market Enables operator training without a physical machine → speeds up the development of operator skills Customer’s test driver’s feedback can be considered before the machine is built, making it easy to make changes Able to enhance application dimensioning and proactive maintenance

4.8 4.7 4.2 4.1 4.1 4.0 4.0 4.0 3.9 3.8 3.4 3.6 3.3

Expected benefits of simulation  139 Table 11.5 Resisting forces against the simulation functions/​efforts in the companies Forces against change

Average

Resource requirements, the most important of which is the encounter between financing, skilled persons, the time spent, and the right target of application Building models is laborious and models often should be very quickly operational and meet the needs of your business Workload required by simulator development Sometimes the lead time required by the simulation work can be a problem in a fast-​paced product development project The development and maintenance of models in product development projects should not be more cumbersome than the current practices Finding the characteristics/​data of the devices so that the ideal device is correct with the right accuracy Technical challenges slow down the use of simulation Multiple parallel simulation and modeling tools in use → finding interfaces and a comprehensive platform is challenging Multiple machine types are simulated so that you cannot easily combine subassemblies “Selling” simulation practices to people in different departments/​fields is always very challenging Engaging colleagues/​decision-​makers in the need for a simulation project → references and examples are required Because of the amount of work required, the costs are too heavy to allow for large-​scale utilization in a smaller company Lack of knowledge; modeling requires knowledge of several different areas

4.1 3.9 3.8 3.8 3.8 3.6 3.6 3.5 3.5 3.3 3.3 3.1 3.1

11.4.1  Benefits of using simulation in the long term for the customer and other stakeholders During the second round, the panelists were asked their opinions on the benefits for the customer and other stakeholders of using simulation methods in the long term. The group members commented that if simulation modeling can accelerate the development of new features for new products, it also benefits the customers. Stakeholders and customers have the chance to be a part of the development phase by providing comments and ideas as they use and test the simulators. Generally, the group members agreed that, with the help of simulation modeling, the likelihood increases that products will better match customer needs and that company stakeholder understanding will improve.This could occur if the stakeholders are given the opportunity to use the simulators. They should also be able to freely give feedback about their experiences to the product development teams. One participant commented that, in the long term, simulation models are coming ever closer to the customer’s operating environment and becoming a process in which everything is part of a virtual model. Most likely this will provide a completely different chance to increase productivity benefitting both the

140  Kalle Elfvengren et al. customer and the developers. The need for customer-​specific configurations will also probably increase. In addition, one participant noted that a customer benefit could result from the decrease in operation interruptions. Maintenance can become need-​focused, thus decreasing costs. Solidly designed and maintained up-​ to-​ date system solutions could benefit the customer. According to questionnaire responses, the expert group assumes that customers will gain a better comprehension of the products and their suitability to satisfy needs if they can take part in simulator testing of the new products under development. In the second Delphi-​round, the group was also asked about the ways to measure and evaluate the long-​term business-​related impacts of simulation activities. One answer highlighted the fact that if these simulation models are used in preemptive maintenance, their impact can be easily pointed out. For example, the truthfulness of automated error notifications, the amount of maintenance stops needed for a solution, and the amount of customer error notifications serve as good measuring points. The following measures and evaluations were noted as having potential. • • • • •

Through turnover and volumes –​Key parameters such as the number of new products, service sales, or changes in market share could be compared between projects that use simulation activities and those that do not. Prototyping and product problems –​The number of prototypes needed and unforeseen product problems during prototype testing could showcase the success rate of simulation during the development phase. Product development schedule and cost –​How simulation activities affect the time it takes to run a product development process and the amount of testing required could be examined. Customer satisfaction –​Customer satisfaction could be monitored via customer feedback and polls, by measuring the number of new customers, and by recording the loss of existing customers. Preventive maintenance –​Quantifying the volume of unforeseen malfunctions, the truthfulness of automated error notifications, the amount of maintenance stops needed, and the amount of customer error notifications received would help to define how simulation activities affect preventive maintenance operations.

In addition, the participants were asked how to identify and get commitment from customers who want to cooperate in simulation activities.The participants stated the following viewpoints. • •

Customers should be prepared to share necessary information and should also be willing to commit their resources to generating, upkeeping, and developing the models. By remaining in contact with the customers, the manufacturer should be able to figure out who is most likely to impact the project. The customer

Expected benefits of simulation  141 is not always the end-​user, for example, in the case of dealer networks, big dealers have a desire to influence. • You get the customer to commit through results and open cooperation. Commitment comes from successful cooperation. • Often a consulting company hired by the customer demands a model of the overall solution. • A customer commits once they can see the impact possibilities and are able to participate in the development. • This could depend on the customer, but simulators should be able to convince the customer of its benefits. Simulators should include indicators about things like productivity that can measure the added value. 11.4.2 The business-​activity related effects of simulation In the second round, the panelists presented their opinions on the long-​term effects of simulation work on company business. These opinions were taken to the third round of the Delphi questionnaire where the panelists evaluated these business effects according to two criteria: costs efficiency and achievability. First, the panelists were asked to estimate the effects of simulation on business activities and their cost and efficiency related impacts. Resources, such as time and money, must be invested to realize business-​related benefits. The panelists used a grading scale of 0 –​6. (0 = very small, …3 = decent, …6 = very big). Then, the panelists were asked to evaluate how realistic it is to expect that these impacts could be achieved.The grading scale was 0 –​6. (0 = difficult to achieve, …6 = easy to achieve). Table 11.6 presents the suggestions for the business-​ related achievements for the company resulting from the implementation of

Table 11.6 Suggestions for business-​related achievements of using simulation in the long term Business-​related achievement

Efficient use of simulators in software testing and development Agreed upon/​Understood specs at an early stage Stronger support of product design and new product development through simulation Involving customers, customer support and other stakeholder groups in the design project through concept simulation → gaining information about customer needs through their involvement in the design process

Costs-​efficiency

Achievability

Mean

SD

Mean

SD

4.8

0.26

4.4

0.25

4.6 4.6

0.16 0.16

4.3 4.3

0.14 0.18

4.4

0.18

3.7

0.22

(continued)

142  Kalle Elfvengren et al. Table 11.6 Cont. Business-​related achievement

Speeding up product development and testing by utilizing simulation Advanced simulation platform provides product development a more in-​depth understanding of the product and its uses Customer can be involved in the product development and testing at an earlier stage → more versatile information transfer and the overall improvement of understanding Unrestricted testing and validation of new ideas for further development Presenting product properties is easier The increase in sales and production volume through improved products Better risk management in product design Simulation connects different functions within the company in a better way Supporting sales and marketing activities through simulation You can simulate how the product functions in customer’s processes through simulation → ability to add in new services Managing real-​world systems through simulators (digital twin) Simulation can find new potential business opportunities while managing the solutions of current endeavors Product customization in project deliveries can be controlled more efficiently An advanced simulation platform can improve the methods of preemptive maintenance: monitoring the condition of systems, ability to focus maintenance in a better way and find the root of the potential problem Forming new customer relationships while managing the old ones through the utilization of the simulation platform. The ability to sell services that provide more value

Costs-​efficiency

Achievability

Mean

SD

Mean

SD

4.4

0.18

4.6

0.14

4.4

0.22

4.1

0.17

4.4

0.22

3.8

0.27

4.3

0.16

4.4

0.14

4.3 4.3

0.18 0.19

4.1 3.6

0.23 0.16

4.2 4.0

0.20 0.18

3.2 3.2

0.17 0.19

3.9

0.24

4.3

0.22

3.3

0.19

3.1

0.17

3.3

0.29

2.2

0.25

3.1

0.27

3.0

0.21

2.7

0.28

2.3

0.30

2.6

0.18

1.9

0.09

2.2

0.19

2.3

0.24

Expected benefits of simulation 143 simulation in the long term. The “Mean” is the calculated average of the given grades, and SD is the standard deviation.

11.5  Conclusions The overall benefits of simulation seem to be centered around product development. The basis of simulation goals related to product development is to gain a more in-​depth understanding of the model and its uses to apply it in the best possible way when the opportunity arises. Moreover, simulation technologies were seen as a way to directly improve product quality or as a way to enhance the functionality of one area such as the control system while reducing product development time. The heavy reliance on physical prototyping within the manufacturing sector was noted, and the industry experts emphasized that more research was needed into what type of simulation model or hardware would provide the same degree of product validation. The expert panelists also expressed interest in customer integration, which was seen as something that could directly bring extra value to product development. They understood the importance of the customer experience to their business, and therefore they felt that developing user interfaces based on feedback gained from their product end-​users was important. Developing new ways to further integrate customers into the product development processes was considered a potential future goal. The experts indicated that user satisfaction can be increased by involving potential customers in the earlier phases of product development. It is better to involve users up front in the design phase and not just later in model testing. With earlier user involvement, designers can construct their models with more and more attention paid to customer wants and needs, and customers can gain more appreciation as to what is and is not practical or possible. For example, if potential users request a vehicle functionality that is not achievable. Real-​time simulation carried out during the design phase will demonstrate to the customer that this function is not practical or possible and prevent the pursuit of an unachievable design requirement saving both time and money. Involving users early will produce an outcome that is more functional, more precise, and more customer oriented. The main driving forces behind the adoption of simulation technology in manufacturing were all centered around its potential to improve all aspects of product development.The resisting forces are mostly resource-​based. Successful implementation demands a heavy investment of both time and resources. The technical complexity of simulation as well as an overall lack of knowledge by management was also considered to be an impediment. Real-​time simulation models make it possible to elevate all specifications and packages (e.g., a hydraulic or steering package) to maximum functionality. With digital twinning, models can be designed and analyzed to determine

144  Kalle Elfvengren et al. the part interconnection efficiencies of a new product design resulting in significant decreases in time spent on production and for maintenance. Real-​ time simulation models can reveal the weaknesses and strengths of a proposed product vehicle before the design has been finalized and a prototype built. Furthermore, simulation models offer the opportunity to inexpensively and safely train future users. The most cost-​effective and achievable benefits of simulation also centered around product development. The experts felt that the resulting overall improvements to the development process would lead to faster development, higher product quality, and better risk management. Using simulation methods to cultivate new customer relationships or to enable product customization was less interesting to the experts and seen as hard to achieve. Despite the lack of interest in customization, the customer aspect of simulation still showed promise to the experts. They felt that simulation could be used by marketing and sales to enhance customer presentations.The potential to incorporate customers into product development was also something the experts felt was achievable and cost-​effective. In this chapter, we brought together expert estimates of the potential impacts and added value brought by the implementation of simulation systems in manufacturing. The chapter also examined the suitability of the Delphi method used to the gather the data needed to carry out the research. A four-​phase Delphi process was designed that comprised anonymous written survey rounds. The research process was laborious and took about three months. The planning, the time it took for panelists to reply to the questionnaires, and the time needed for the analysis of the responses took a lot of time.The characteristics of the Delphi method; such as participant expertise level, response anonymity, feedback, and its iterative nature; guaranteed that the research process proceeded well. The expert panel clearly brought the manufacturing industry and digitalization/​ modeling expertise needed to complete the research. The combined opinions of individual experts formed a surprisingly smooth description of the expected goals and benefits of simulation modeling as well as the barriers and long-​term business-​related benefits. The easily achieved consensus may have been a result of the relatively homogeneous background of the participants. A limitation of this research is that the panel did not include people from different backgrounds and experience, e.g., professionals with experience in a variety of organizations, a diverse work history, variation in age and education, etc. Had the panel of experts been more heterogeneous, it is more likely the results would have been more confrontational, insightful, and creative. In conclusion, the Delphi study helped to increase the understanding of the theme. The result was easily exploitable material, such as a force-​field analysis. Delphi results are always impacted by the strong group of experts involved in the contribution of the research process as well as careful planning and execution. The research provided a theoretical contribution by showing once again the suitability and strength of the Delphi as research method.

Expected benefits of simulation  145

References Ajimal, K. (1985). Force field analysis: A framework for strategic thinking. Long Range Planning, 18(5), 55–​60. Baker, J., Lovell, K., & Harris, N. (2006). How expert are the experts? An exploration of the concept of ‘expert’ within Delphi panel techniques. Nurse Researcher, 14(1), 59–​70. Bako, B., & Božek, P. (2016). Trends in simulation and planning of manufacturing companies. Procedia Engineering, 149 (pp. 571–​575). Elsevier, Nový Smokovec. Benedettini, O., & Tjahjono, B. (2008).Towards an improved tool to facilitate simulation modelling of complex manufacturing systems. International Journal of Advanced Manufacturing Technology, 43(1–​2), 191–​199. London: Springer. Boschert, S., & Rosen, R. (2016). Digital twin –​The simulation aspect. In P. Hehenberger & D. Bradley (Eds.), Mechatronic futures –​Challenges and solutions for mechatronic systems and their designers (pp. 59–​74). Switzerland: Springer Cham. Donoghue, I., Hannola, L., Papinniemi, J., & Mikkola, A. (2018). The benefits and impact of digital twins in production development phase of PLM. International Conference on Product Lifecycle Management: Product Lifecycle Management to Support Industry 4.0. pp. 432–​441. Fei, T., Jiangfen, C., Qinglin, Q., Meng, Z., He, Z., & Fanyuan, S. (2017). Digital twin-​ driven product design, manufacturing and service with big data. International Journal of Advanced Manufacturing Technology, 94, 3563–​3576. Gallois, P. M. (1993). The new industrial challenge: Towards a lean production. Proceedings of the 5th International Conference on Advances in Production Management Systems, pp. 3–​10. Heilala, J., Montonen, J., Järvinen, P., & Kivikunnas, S. (2010). Decision support using simulation for customer-​ driven manufacturing system design and operations planning. In G. Devlin (Ed.), Advances in decision support systems (pp. 235–​260). Croatia, InTech. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L., & Young, T. (2010). Simulation in manufacturing and business: A review. European Journal of Operational Research, 203(1), 1–​13. Kher, S. (2017). Simulation for the digital twin ecosystem. Ansys Advantage, Issue 1, 14–​17. Retrieved from www.ansys.com/​-​/​media/​ansys/​corporate/​resourcelibrary/​ article/​ simulation-​for-​the-​digital-​twin-​ecosystem-​aa-​v11-​i1.pdf Klingstam, P., & Gullander, P. (1999). Overview of simulation tools for computer-​aided production engineering. Computers in Industry, 38(2), 173–​186. Kuusi, O. (1999). Expertise in the future use of generic technologies: Epistemic and methodological considerations concerning Delphi studies. Government Institute for Economic Research, Helsinki, p. 284. Law, A. M. (2015). Simulation modeling and analysis. McGraw-​Hill Education. Linstone, H., & Turoff, M. (1975). The Delphi method: Techniques and applications. Addison-​Wesley, Boston. Mikkola, A., Hannola, L., Handroos, H., Rantanen, H., & Ullako, K. (2014). Sustainable product processes through community-​based real-​time simulation. Lappeenranta University of Technology, Lappeenranta. Mourtzis, D., & Doukas, M. (2014). The evolution of manufacturing systems: From craftsmanship to the era of customisation. In V. Modrák & P. Semančo (Eds.)

146  Kalle Elfvengren et al. Handbook of Research on Design and Management of Lean Production Systems (pp. 1–​29). Hershey, PA: IGI Global. Negahban, A., & Smith, J. (2014). Simulation for manufacturing system design and operation: Literature review and analysis. Journal of Manufacturing Systems, 33(2), 241–​261. Okoli, C., & Pawlovski, S. (2004). The Delphi method as a research tool: An example, design considerations and applications. Information & Management, 42(1), 15–​29. Rowe G., & Wright, G. (2011). The Delphi technique: Past, present, and future prospects –​Introduction to the special issue. Technological Forecasting & Social Change 78(9), 1487–​1490. Sackman, H. (1975). Delphi critique. DC Health, Lexington, MA. Robinson, S. (2014). Simulation: The practice of model development and use. Palgrave MacMillan. Wortmann, J., Muntslag, D, & Timmermans, P. (1997). Why customer driven manufacturing. In J. Wortmann, D. Muntslag & P. Timmermans (Eds.) Customer-​driven manufacturing (pp. 33–​44). Dordrecht: Springer.

12  Integrating the user experience throughout the product lifecycle with real-​time simulation-​based digital twins Qasim Khadim, Lea Hannola, Ilkka Donoghue, Aki Mikkola, Esa-​Pekka Kaikko and Tero Hukkataival

12.1  Introduction Sustainable business models stress the need to increase customer value throughout the product lifecycle from product design and development to production, service, and disposal. In traditional product development using conventional technologies, users have access to physical prototypes, but they cannot experience the product during its design and development stages. Accordingly, conventional product development does not involve users in decision making throughout the entire product lifecycle. This can result in expensive and unsuitable products that do not fully meet user needs (Tao et al., 2019) (Armendia et al., 2019). Utilization of digital technologies such as a physics-​based real-​time simulation of the physical product potentially allows users to be involved throughout the product lifecycle from product design to product disposal. Previous literature on digital twins has focused primarily on the modeling and data management aspects. Work that has considered the modeling perspective of digital twins has generally focused on the product and processes without considering the physics and connections to the physical counterpart (Tao et al., 2019) (Armendia et al., 2019). Data management studies, in turn, have mainly used digital-​twin information to describe product processes and information flows. While providing valuable information, important elements of user experience are absent in such studies. A multibody-​ based digital twin involves the dynamic solution of the equations of motion for the physical product. It can provide, as a single source, information about its physical counterpart. This information can be shared with end users and customers to co-​create and increase the customer value of product-​service systems during the various stages of the product lifecycle. In addition, virtual reality (VR) or augmented reality (AR) tools can be integrated with a multibody-​based digital twin to enhance the user experience (UX) in

148  Qasim Khadim et al. an immersive environment. Thus, users and potential users can experience, virtually, the working cycles of the real-​world counterpart via VR/​AR technologies, and help to develop more efficient management of product development processes. The engagement of end users and customers in product development and enhancement processes can generate innovative ideas and provide valuable insights enabling changes and improvements in future products and related services (Tseng et al., 2010). Moreover, taking into account the user experience and customer needs throughout the entire product lifecycle by the UX and data generation with multibody-​based digital twins may enable radical innovations in competitive markets (Orcik et al., 2013). An industrial need is the necessity to explore the potential of digital technologies (e.g., VR) for testing the UX of end users and customers and for co-​creating customer value in a product and/​or service. This chapter focuses on the user experience with VR technologies in a real-​time simulation based on multibody system dynamics. Therefore, the objective is to explore the role of users in multibody-​based digital-​twin utilization throughout the product lifecycle, including design, production, service, and the end-​of-​life stages.To this end, a real-​time simulation of an industrial 3W, 2.0-​ton, EVOLT 48 counterbalance forklift truck using VR tools is described, and a methodology is proposed that enables the integration of the UX throughout the different stages of the product lifecycle. The rest of this chapter is structured as follows. The next section describes the multibody-​based digital twin and highlights key literature related to product lifecycle analysis, user experience, and the co-​creation of product value through the UX. A methodology that can enable the integration of user experience into the product lifecycle using multibody-​based digital twins is then introduced. Next, a case study of a 3W, 2.0-​ton, EVOLT 48 forklift truck is taken as an example to illustrate multibody-​based digital-​twin UX integration into the different phases of the product lifecycle of a forklift truck, such as design and development, production, service, and disposal. Conclusions are drawn in the final section.

12.2  Related research 12.2.1  Multibody definition of a digital twin Placing the digital twin at the core of the digitalization of product development offers a way to simulate the behavior of a product over its lifecycle (Tao et al., 2019) (Grieves, 2014). As claimed by the National Aeronautics and Space Agency (NASA), the concept of a mobile machine twin was first introduced in 1960 during the Apollo program (Tao et al., 2019). In that example, engineers used the physical space of a space craft to analyze internal conditions.With the development of modern computer systems and simulation methods, the physical space is being replaced with a simulation model or virtual space. The

User experience through digital twins  149 simulation model is a representation of a physical system that can execute real-​ world behaviors in a computer simulation. Further, advanced information and networking technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data, and cloud computing enable real-​time information transfer between the physical and virtual spaces of a digital twin (Tao et al., 2019). Considering these novel data transfer technologies, Grieves (2014) introduced three dimensions of the digital twin: the physical space, the virtual space, and the connections between the two spaces. Recently, Tao et al. (2019) added data and services as a dimension of the digital twin. These definitions, however, do not consider the physics of the real world and user involvement in the simulation model of the digital twin. A multibody model can be seen as a physics-​based digital replica of the physical world that can simulate working conditions and update its status continuously from multiple sources. The solution of a multibody model can be synchronized to real-​time operations. The model can be used throughout the product lifecycle as a real-​world counterpart.The multibody model may include details of the hydraulics, electrics, mechanical actuators, tires, and physical contacts of the system. Additionally, the use of multibody equations of motion with state estimation theories, such as the Kalman filter, permits digital twin data to be generated that cannot be measured directly with sensors for technical and economic reasons (Sanjurjo, 2016). By utilizing real-​world identical controls and Human Machine Interface (HMI) systems, a multibody model can involve potential users in the working cycles of the machine.The use of immersive methods in the simulation models allows stakeholders to evaluate, optimize, and predict the states of the physical space of the digital twin. When the digital twin has connectivity to the real world, i.e., where data are exchanged, digital business models can be identified to generate new revenue streams and customer value (Donoghue et al., 2019). 12.2.2  Product lifecycle The business literature presents numerous decision and innovation process models that describe how companies develop or should develop new products or services. Koen et al. (2002) divides the innovation process into three areas: the fuzzy front end, new product development, and product commercialization. In most models, the UX in product development focuses mainly on the new product development phase. However, the phase after product commercialization can also be important for enabling companies to understand how products are used (Varsaluoma, 2018). For example, companies can benefit from understanding how to develop a positive user experience and learning how these user-​experience goals are expressed by customers over time (Varsaluoma, 2018). The different phases of the product lifecycle are defined in the literature in different ways with various terms and categorizations. Grieves defined (2005)

150  Qasim Khadim et al. Product Lifecycle Management (PLM) as an information-​ driven approach integrating people, processes/​ practices, and technologies across the entire product lifecycle, i.e., design, manufacture, deployment, maintenance, removal of product, and final disposal. Stark (2006) introduced the manufacturer and user viewpoints into the product lifecycle. The manufacturer of a product sees a product lifecycle as starting from the generation of the product idea to its production, realization, support, and services and finally to its retirement. The user of a product sees a product lifecycle as starting from the acquisition of the product, continuing through its usage, and ending at the moment when the product stops being used and is disposed of. Terzi et al. (2010) divided the product lifecycle into three phases: Beginning of Life (BOL) –​developing and delivering the product, Middle of Life (MOL) –​ operating and maintaining the product, and End of Life (EOL) –​removing the product from support and service in a controlled fashion. Donoghue et al. (2018) have identified that companies may also have a Product Lifecycle Management framework. This framework includes three lifecycle phases and the interactions between the different product layers that need to be managed with different business processes. 12.2.3 The user experience New products may fail to meet customer and end user requirements. To avoid such market failures, product value should be determined by users and the provider through efficient innovation interaction between the product developers and the users (Orcik et al., 2013). In this kind of co-​development, at the product development stage, potential users can interact with the product and give opinions about its performance based on their knowledge, skills, and experience (Orcik et al., 2013). The experiences of users during this interaction, i.e., the user experience, becomes a key factor to adding product value and achieving competitive advantage (Hildén et al., 2016). Consideration of the UX in new products can produce innovative ideas and allow industries to discover new dimensions of their products and services. Consequently, a UX-​based approach can enhance a company’s competitiveness and profitability and improve the quality of the products and services offered (Orcik et al., 2013). The UX not only comprises the pragmatic functions of the product, but also the affective and cognitive demands of end users and customers. Affective and cognitive demands are the psychological needs, cognitive capacities, choices, and emotional responses of the users (Zhou et al., 2013). 12.2.4  Co-​creating product value with the UX and multibody-​based digital twins Several researchers have implemented digital twins in immersive and interactive environments as a part of efforts to improve user experiences at different stages of the product lifecycle. For instance, an Augmented Reality (AR) system was

User experience through digital twins  151 used in Schroeder et al. (2016) to display digital-​twin data at the marketing stage based on the concept of a cyber-​physical system (CPS). In the work, end users and customers had access to physical machine sensor data via web services, and they could explore an industrial plant and its devices in real time. In work by Laaki et al. (2019) considering the product service stage, users were able to perform remote surgery operations by employing a digital twin of a robotic arm in a Virtual Reality (VR) system. In the study, the VR system was used to engage users to perform a surgery operation in the virtual environment. A physical robotic arm executed a similar operation in the real-​world. A 4G network transmitted the data between the physical and virtual spaces of the digital twin. In other work, VR and AR systems have been used in engineering (Posada et al., 2015), art (Rechowicz et al., 2018), gaming (Kosmadoudi et al., 2013), and architectural (Schroeder et al., 2016) applications at different stages of the product life cycle. However, these digital twins were developed to serve specific user needs, and they do not include the physics in the simulation models. For complex machines of the type considered in this chapter, studies investigating the co-​development of products using the UX and digital twins have been limited to the product design and development phases (Auricht & Stark, 2015). To fill this gap, this study utilizes a multibody-​based digital twin that can be used to include customers and end users throughout the product lifecycle. The digital twin enables UX information to be generated via the real-​time simulation of a complex machine.

12.3  Enabling user experiences in the product lifecycle with an immersive multibody-​based digital-​twin approach Figure 12.1 presents a multibody-​ based digital-​ twin methodology that could be adopted to include user experiences throughout the entire product lifecycle. The steps of the methodology are explained in further detail in the paragraphs below. 12.3.1  Developing a user-​centered virtual space of a physical model The multibody simulation model presents all the components and sub-​ components of the physical model in a computer model. Like the physical system, the virtual duplicate may include rigid and flexible bodies, hydraulics, electric drives, tires, power transmission elements, forces, friction, particles, and the HMI and controls.The multibody equations of motion include contact and collision models to describe the dynamics of the simulation model. Following advances in multibody formulations, standard computer systems can solve the complex equations of motion in real time at a time step of 0.5–​2 milliseconds (Jalon and Bayo, 2012; Jaiswal et al., 2019). In short, the multibody model simulates the realistic behavior, properties, and physics of the real world in real time. The real-​world counterpart, i.e.,

152  Qasim Khadim et al.

6. Product life management services

5. Real-time communication between physical and virtual spaces of digital twin

4. Recommended user experiences in physical product

Enhancement of meaured data

Physical space of digital twin

1. Developing a user-centered virtual space of a physical model

Virtual space of digital twin

2. User selection of components design data

3. User immersive methods

Figure 12.1 Methodology to enable UX integration into the product life cycle using multibody virtual and physical spaces of a digital twin.

the virtual twin simulation model, can exist even before the manufacturing of the actual product in the product development and commercialization stages. Combined with sensor data from the physical space, the multibody simulation model guides users throughout the product lifecycle and enables them to evaluate, optimize, control, and predict real-​world working cycles in real time. 12.3.2  User selection of component design data Design data describing different components and sub-​ components of a multibody model are collected, combined, and analyzed. Such data comprises positions, masses, and inertias of bodies, hydraulics, electrics, power transmission element parameters, and friction coefficients representing the physical systems. Combining data from different sources enables designers to analyze product performance with respect to user needs in the multibody simulation model. At this stage, end users and customers participate in the design process and test the features of the product in the simulation model. User comments regarding the multibody model can help designers discover relationships between design data and user preferences. The companies may also be able to use these relationship patterns in future products.

User experience through digital twins  153 12.3.3  Immersive methods for generating user input In addition to computer system controls, multibody-​based digital twins can engage end users and customers by using immersive virtual environments. To be able to assess the HMI and usability of the physical system, the multibody-​ based digital twin can be integrated with gaming controls, simulators, VR, AR, mixed reality (MR), and haptics. Using these external devices, user senses and perceptions are fully immersed in the virtual environment. Consequently, the multibody-​based digital twin can perform the pragmatic functions of the product and meet the affective and cognitive needs of end users and customers. A number of immersive environments enabling technologies are discussed below. 12.3.4  Simulator or motion feedback platform Real-​time simulations can be done in simulators to provide end users and customers with a cockpit experience. Physical system controls and ergonomics can be constructed on the simulator such that the HMI of the simulation corresponds to real world experiences.The simulator machine can also be equipped with a motion feedback platform to provide the feeling of the terrain path in the simulation. Additionally, using a cave-​like environment, where extra display screens on the moveable platform immerse the human body in the simulation, can give users a more intensive visual experience. This virtual environment can give users a more realistic experience with VR/​AR glasses. 12.3.5 VR, AR, mixed reality glasses, leap controllers and haptics End users and customers can be immersed into a virtual world through a head-​ mounted display (HMD). These VR, AR, and mixed reality glasses take the human inside the virtual space. Using these external tools, users can gain a deeper understanding of the real-​world counterpart and its environment. Many types of HMD are available on the market, such as the HTC Vive system and Oculus Rift and XR-​1 glasses. Several immersive devices could possibly be integrated into a real-​time simulation to provide a user-​immersive environment. Leap controllers and haptics further enable users to perceive a more realistic experience in the simulation environment. 12.3.6  Manufacturing of the physical product The manufacture of the physical product can be planned based on the experiences and recommendations of the users of the virtual product. User experiences related to the dynamics of the simulation model are added by means of actuators, controls, and sensors. The physical model may contain hydraulics, electrics, pneumatic and mechanical actuators, and tires to execute requests made by users of the multibody model in the immersive environment. Sensors can collect physical product data and input it into the simulation model. This

154  Qasim Khadim et al. model can be used in the service stage of the forklift, as shown in Figure 12.1, where the virtual replica of a physical 3W, 2.0-​ton, EVOLT 48 forklift in a VR environment is presented. User experiences in the physical version of the forklift can also be tracked in the reference link (EDiA, 2019). 12.3.7  Real-​time communication between the physical and virtual spaces of the digital twin Before the release of a new product that has been developed based on user experiences, the physical and virtual spaces need to be interconnected so that digital twin information can be used in different phases of the product lifecycle as needed by end users and customer. Network communication, cloud computing, and network security are the key enabling technologies for transmitting data back and forth between the physical and virtual twins. Physical product sensor data is stored in data cloud storage using network technologies such as quick response (QR) code, radio frequency identification (RFID), barcodes, wireless fidelity (Wi-​Fi), Bluetooth, etc. The data can be accessed via the 4G network. The multibody model enables end users and customers to monitor, coordinate, and control the real world of the digital twin.This data communication must be secured for successful management of product lifecycle related services. 12.3.8  Product life management data The multibody-​based digital twin generates big data during the service and end of life phases, and this data can spur development of new product-​related services. The data can include product component data, product-​environment interaction data, environment data, product user data, and control data. As mentioned earlier, the data can be used in the real-​world counterpart with the aid of sensors and IoT services in real time. By using VR/​AR immersive technologies, the multibody-​based digital twin enables users to predict, optimize, simulate, and experience the states of the physical space with contact and collisions in the environment during the product’s life. For instance, product component data can notify stakeholders of the need to take actions related to predictive maintenance of the product. Similarly, using product state data, more precise decisions about the reuse or retiring of a product can be taken. Additionally, industry can utilize user experience history from the lifecycle of previous multibody-​based digital twins in future products and other projects to gain competitive advantage. 12.3.9  Enhancement of measured data Due to integration of the equations of motion with a state observer estimator, the multibody simulation can provide information about the internal states of the system based on a smaller amount of sensor data from the physical system (Sanjurjo, 2016). In this way, the multibody-​based digital twin can provide

User experience through digital twins  155 detailed information about the state of the physical system, which in some cases can reduce sensor costs. The multibody-​based digital twin can reduce the cost of management of many digital product processes compared to conventional digital twin technologies. For instance, it is possible to predict the wear and tear of tires from accurate information about vehicle tire friction.

12.4  Industrial case study: the UX in different phases of the product lifecycle with a multibody digital twin The case company of this study wants to explore the possibilities of using digital technologies, especially digital twins utilizing VR technologies, to integrate end users and customers into the management of the product lifecycle. The case company has identified the following challenges. •

Co-​developing new products with users to strengthen the customer feedback loop and include innovative ideas in the final product will require a new approach. • Because the manufacturing, testing, remanufacturing, and retesting of the prototype demands significant time, money, and effort; product lead time to market can be long. • New materials and manufacturing solutions must be developed to accommodate the product’s operation in different working environments. • Repair and maintenance services to end users and customers will be required to gain a competitive advantage in the market. • Decisions regarding the reuse or disposal of products in an eco-​friendly way for a safe working environment will have to be worked out with potential users. In this study, these challenges have been addressed by developing and implementing a multibody-​ based digital twin of a 3W, 2.0-​ ton, EVOLT 48 counterbalance forklift. A parameterized real-​ world counterpart of the multibody forklift model was prepared using multibody equations of motion. The digital model included actual physical dimensions, hydraulics, electric and mechanical actuator data, tires, and contact parameters for a realistic user experience in the real-​time simulation. A motion feedback platform and VR/​ AR immersive methods tightly integrated the twins and allowed end users and customers to experience the functions and behavior of the forklift in the digital world. The following subsection details the integration of users in the forklift lifecycle using a multibody-​based forklift simulation model. 12.4.1  New product development approach: user co-​creation of a new forklift mast system in the virtual space The main challenge faced by the case company is to shorten the product development process while simultaneously including end users and customers

156  Qasim Khadim et al. in co-​creation of product value. The case company currently uses a physical prototyping method to develop new products. In this approach, end users and customers test physical prototypes after manufacturing, which increases product development cost, and the effort and time required. Customers and end users can only comment on the performance of the product after purchase, which extends the customer feedback loop. The multibody simulation permits end users and customers to directly test the new product developed by the company. This UX-​driven approach was used in the development of the 3W, 2.0-​ton, EVOLT 48 counterbalance forklift. The approach is presented schematically in Figure 12.2. Important user experiences related to the forklift are listed by number.Two-​sided arrows highlight the integration of end users and customers through the multibody virtual space with the product development team. As Figure 12.2 illustrates, end users and customers directly participate in the product development process and comment on the performance of the forklift. Important UX elements related to the forklift are driving experience, mast system loading and unloading, visibility through the mast system, mast wobbling, 360° electric steering, forklift stability, forklift controls and ergonomics, and the working environment of the forklift. The forklift can lift a

3. Visibility through mast

2. Mast system loading and unloading

1. Driving experience

4. Mast wobbling

customers

Multibody virtual space

8. Forklift stability in turning operation

5. Driver ergonomics

6. 360° electric steering system

7. Testing forklift in different environments

Figure 12.2 UX-​driven product development of a 3W, 2.0-​ton, EVOLT 48 counterbalance forklift using multibody real-​time simulation.

User experience through digital twins  157 maximum of 2000 kg. The driving experience includes forklift forward and backward movements and the braking action. The stability of the forklift under minimum and maximum loads while turning was tested by users in the simulation model. Another UX-​related aspect is the smooth reduction of speed going into corners and the smooth increase on exiting corners. Agility and the ability to turn quickly as well as turning circle are also important. Finally, the visibility and clarity of the displays used is a further important aspect of user experience. The loading experience is how the mast behaves under load. An important behavior mentioned was the smoothness and accuracy of the lifting. Smoothness corresponds to continuous movement or extension of the mast when the operator uses the switches. Additionally, mast wobbling provides users and customers with a realistic experience in the real-​time simulation. 12.4.2  Commercialization: user testing of the parameterized model in different environments Because of its physical prototype culture, production and development of iterations of the physical prototype take time within the case company so product lead time to market is long. Furthermore, new products are often introduced after a significant drop in product sales. This culture hinders the case company from achieving a sustainable business model in a competitive environment. By implementing a multibody digital twin, as shown in Figure 12.3, the company can involve the end users and customers with continuing development of the 3W, 2.0-​ton, EVOLT 48 forklift during the marketing stages. From many options, end users and customers can select the best combination of forklift machine and mast system on the motion platform. Using VR/​AR immersive technologies, end users and customers can test each new product configuration in simulated working environments as per their needs and future requirements. By respecting the user choices and needs in the virtual model, the case company avoids physical prototyping, which reduces the product lead time to the market. Additionally, for product commercialization, the use of the simulation for marketing, sales, and training can be seen as offering improvements from both the cost and time perspective and establishing a positive customer experience (Donoghue et al., 2018). The added value is to offer a continuous journey from marketing to delivery and training (Donoghue et al., 2018). 12.4.3  Manufacturing: utilizing the user-​based multibody model in production Using the multibody digital model, industrial companies can prepare materials charts and machine components for use in the simulation world. The end users and customers choose optimum materials and components when designing a

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Multibody virtual space of 3W 2.0 ton EVOLT48 in LUT University

Multibody virtual space of 3W 2.0 ton EVOLT48 in warehouse

End users and customers

Simplex mast

Duplex mast

Triplex T riipllex mast mastt

Figure 12.3 Accelerating the marketing process of the 3W, 2.0-​ton, EVOLT 48 forklift with the introduction of a multibody simulation model.

machine configuration as per their needs in the working environment. Based on the selected configuration, parameters can change in the real-​time simulation. In this way, end users and customers can test a wide range of different configurations on the simulator. Following selection by the user, the manufacturing company can order the appropriate raw materials and components for manufacturing and production of the final product. Furthermore, end users and customers can track the status of manufacturing via an online portal with a private radio frequency identification (RFID) or barcode provided by the manufacturing company. 12.4.4  User-​related product services in the operation phase: updating the virtual space of the digital twin with real-​world information Based on the exact machine dimensions and features selected, it is possible to support users in the commissioning and delivery of the physical version of the

User experience through digital twins  159 digital twin. In addition, the real-​time multibody-​simulation-​driven motion feedback platform can be used to provide training services even before the start of manufacturing of the physical product. As mentioned earlier, a real-​time secured connection is built between the virtual and real-​world spaces of the digital twins to monitor, optimize, and predict field data. For instance, decisions about predictive maintenance of hydraulic systems can be made by collecting sensor field data. In such an approach, the lifecycle efficiency online system analyzes the sensor data and calculates the lifetime of the hydraulic cylinders (Mevea, 2019). Similarly, the digital version of the twin can be used remotely to monitor and control real-​world operations in difficult and dangerous working conditions (Mevea, 2019). The multibody-​based digital twin will ensure the safety of workers and improve quality, productivity, and performance of industrial operations. The overall cost of multibody digital driven product processes can be much less than conventional product processes. 12.4.5  End of product life: retiring the product based on user data generated in the digital twin As noted earlier, one challenge when using conventional methods is that users and manufacturers do not have a direct relationship during product development or during product use. Manufacturers are compelled to make independent decisions about the retirement or repairing of the physical product. Real-​time simulation enhances product lifecycle management by building a relationship between the manufacturer and users as the multibody-​based digital twin continuously receives real-​time updates from the real world from sensors, the IoT, cloud computing, and network security services. Therefore, the manufacturer can inform users about poorly functioning parts, and the users and manufacturer can cooperate on maintenance of the physical version of the multibody-​ based digital twin, if necessary. Finally, if the physical product is no longer fit for further work, users and manufacturers can together agree on a retirement date for the physical product to ensure a safe working environment.

12.5  Conclusion Utilization of a multibody-​based digital twin makes it possible and straightforward to integrate end users and customers into the various phases of the product lifecycle. The direct involvement of users in the product development process enables companies to codevelop appropriate and competitive products. Replacing the physical prototype culture can considerably reduce product lead time to market, which enables companies to gain a real competitive advantage. Real-​time communication between the physical and virtual parts of a digital twin, in turn, can enable companies to optimize the working cycles of the physical product. Additionally, for work in dangerous conditions, end users and customers can control the working cycles of the physical product remotely using the multibody-​based digital twin.

160  Qasim Khadim et al. The valuable real-​ world data generated by integrating the physical and digital twins enables monitoring of degrading or malfunctioning parts of the physical product, thus allowing them to be used optimally before being repaired or replaced, which reduces maintenance costs. Timely removal of worn parts can also lead to reduced energy consumption. Furthermore, the prediction of breakdown or maintenance needs can help end users in decision making and reducing downtime. The multibody-​based digital twin can be integrated into different information technologies such as the IoT, big data, artificial intelligence, machine learning, cloud computing, etc. to provide additional services. Moreover, the multibody digital twin data can assist stakeholders dispose of products in an eco-​friendly and efficient way. Finally, the multibody digital twin and user data can be used by companies to develop more user-​and environmental-​friendly products, as well as products that are compatible, competitive, and adaptable. Ultimately, the engagement of end users and customers throughout a product lifecycle by utilization of a multibody digital twin can enable companies to achieve sustainable business models in a competitive market environment.

Acknowledgments This study was part of the DigiPro project and has received funding from Business Finland and the SIM (Sustainable Product Processes through Simulation) research platform of Lappeenranta-​Lahti University of Technology LUT, Finland. The authors would also like to thank the companies involved for their cooperation. We would like thank Dr. Emil Kurvinen for contributing in reviewing the idea of user experiences in the product lifecycle. We also like to thank Mevea Ltd. and Rocla Oy for giving permission to use data from their website.

References Armendia, M., Cugnon, F., Berglind, L., Ozturk, E., Gil, G., & Selmi, J. (2019). Evaluation of machine tool digital twin for machining operations in industrial environment. Procedia CIRP, 82, 231–​236. Auricht, M., & Stark, R. (2015). MINARGUS: Test tool for User Experience measurement and parameter modification within ADAS simulation. Procedia CIRP, 36, 83–​88. Donoghue, I.D.M., Hannola, L.T., & Papinniemi, J.J. (2018). Product lifecycle management framework for business transformation. LogForum, 14 (3), 293–​303 Donoghue, I., Hannola, L., & Mikkola, A. (2019). The Value of Digital Twins and IoT Based Services in Creating Lifecycle Value in B2B Manufacturing Companies. 2019 Portland International Conference on Management of Engineering and Technology (PICMET), (pp. 1–​6). EDiA, M. (2019, 4). Test Mitsubishi EDiA em FB14ANT. (Logisticsinside, Editor) Retrieved from www.youtube.com/​watch?v=X5vsFrfot5Y

User experience through digital twins  161 Grieves, M. (2005). Product Lifecycle Management: Driving the Next Generation of Lean Thinking. New York: McGraw Hill. Grieves, M. (2014). Digital twin: manufacturing excellence through virtual factory replication. White Paper, 1, 1–​7. Hildén, E.,Väätäjä, H., Roto,V., & Uusitalo, K. (2016). Participatory development of user experience design guidelines for a B2B company. Proceedings of the 20th International Academic Mindtrek Conference, (pp. 49–​58). Jaiswal, Suraj, Pasi Korkealaakso, Rafael Åman, Jussi Sopanen, and Aki Mikkola. (2019). “Deformable terrain model for the real-​time multibody simulation of a tractor with a hydraulically driven front-​loader.” IEEE Access (IEEE) 7: 172694–​172708. De Jalon, J. G. & Bayo, E. (2012). Kinematic and dynamic simulation of multibody systems: the real-​time challenge. New York: Springer Science & Business Media. Koen, P. A., Ajamian, G. M., Boyce, S., Clamen, A., Fisher, E., Fountoulakis, S., Seibert, R. (2002). Fuzzy front end: effective methods, tools, and techniques. The PDMA toolbook 1 for new product development. Kosmadoudi, Z., Lim,T., Ritchie, J., Louchart, S., Liu,Y., & Sung, R. (2013). Engineering design using game-​enhanced CAD: The potential to augment the user experience with game elements. Computer-​Aided Design, 45, 777–​795. Laaki, H., Miche, Y., & Tammi, K. (2019). Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery. IEEE Access, 7, 20325–​20336. Mevea. (2019, 4). Mevea success stories. (T. Eskola, Editor) Retrieved from Mevea: https://​ mevea.com/​success-​stories/​ Orcik, A., Tekic, Z., & Anisic, Z. (2013). Customer co-​creation throughout the product life cycle. International Journal of Industrial Engineering and Management, 4, 43–​49. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., de Amicis, R.,Vallarino, I. (2015). Visual computing as a key enabling technology for industrie 4.0 and industrial internet. IEEE Computer Graphics and Applications, 35, 26–​40. Rechowicz, K. J., Diallo, S. Y., D’An, K. B., & Solomon, J. (2018). Designing modeling and simulation user experiences: an empirical study using virtual art creation. 2018 Winter Simulation Conference (WSC), (pp. 135–​146). Sanjurjo, E. (2016). State observers based on detailed multibody models applied to an automobile, Doctor thesis, University of A Coru~na, Coruna, Spain. Schroeder, G., Steinmetz, C., Pereira, C. E., Muller, I., Garcia, N., Espindola, D., & Rodrigues, R. (2016).Visualising the digital twin using web services and augmented reality. 2016 IEEE 14th International Conference on Industrial Informatics (INDIN), (pp. 522–​527). Stark, J. (2006). Product Lifecycle Management: 21st Century Paradigm for Product Realisation (2 ed.). Springer, London. doi: https://​doi-​org.ezproxy.cc.lut.fi/​10.1007/​b138157 Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Nee, A.Y. (2019). Digital twin-​driven product design framework. International Journal of Production Research, 57, 3935–​3953. Tao, F., Zhang, M., & Nee, A. Y. (2019). Digital twin driven smart manufacturing. London: Academic Press. Terzi, S., Bouras, A., Dutta, D., Garetti, M., Kiritsis, D., & others. (2010). Product lifecycle management-​from its history to its new role. International Journal of Product Lifecycle Management, 4, 360–​389. Tseng, M. M., Jiao, R. J., & Wang, C. (2010). Design for mass personalization. CIRP Annals, 59, 175–​178.

162  Qasim Khadim et al. Varsaluoma, J. (2018). Approaches to Improve User Experience in Product Development: UX Goals, Long-​Term Evaluations and Usage Data Logging, Doctoral thesis, Tampere University of Technology, Tampere, Finland. Zhou, F., Ji,Y., & Jiao, R. J. (2013). Affective and cognitive design for mass personalization: status and prospect. Journal of Intelligent Manufacturing, 24, 1047–​1069.

Part IV

Value for business

13  The digital twin combined with real-​time performance measurement in lean manufacturing Mira Holopainen, Juhani Ukko, Minna Saunila, Tero Rantala and Hannu Rantanen

13.1  Introduction Lean production refers to a business philosophy that aims at eliminating waste and creating value (Womack and Jones, 1996). According to van Assen and de Mast (2018), visualization plays an important role in Lean production. In fact, in their article they say that the application of visual controls and techniques is a marked feature in Lean. One of the most prominent visualization techniques used in modern production is the digital twin, which is a digital replica of a product, process, or system. Effective application of modern visualization techniques such as the digital twin can improve the transparency of manufacturing processes, influence people’s behavior, enable continuous improvement, encourage shared ownership, and support management by providing more accurate information (Tezel et al., 2009). The rise of new visualization techniques such as the digital twin provides new platforms that can also enable performance measurement systems to react more rapidly (cf., Horváth and Szabó, 2019;Yin and Qin, 2019). Other studies have focused on the importance of visual performance measurement to lean manufacturing and continuous improvement (Eaidgah et al., 2016; Bititci et al., 2016; Tezel et al., 2009). The utilization of visualization as a part of management activities of companies also promotes communication (Larsson et al., 2017; Bititci et al., 2016; Eaidgah et al., 2016) and can enhance information flow (Eaidgah et al., 2016). In addition, the utilization of visual management systems can support ongoing strategy development and implementation, facilitate performance measurement activities, and enhance collaboration (Bititci et al., 2016). Implementing digital twins offers a number of advantages that can improve the management practices of manufacturing companies in general (Liu et al., 2019; Min et al., 2019; Wang et al., 2019; Zhou et al., 2019). However, research into the advantages that digital-​twin-​based real-​time performance measurement

166  Mira Holopainen et al. can bring to lean processes is lacking. Thus, the purpose of this chapter is to explore the benefits of digital twins and real-​time performance measurement in the lean processes of a manufacturing company. By examining the processes of an assembly manufacturing company as a contextual example, this chapter focuses on the potential benefits of using digital twins from the perspectives of the subject case company’s maintenance services process and its stakeholders. Looking at the empirical real-​life case will make it possible to gain an in-​depth understanding of the advantages that the utilization of digital twins and real-​ time performance measurement provide. The rest of the chapter is structured as follows. First, the contextual background of the chapter is presented including literature from lean management and lean processes, as well as from performance measurement and the utilization of digital twins as a part of performance measurement activities. Presented next are the empirical examination of the digital twin and real-​time performance measurement application and the methodological choices and data gathering. Finally, the results are presented and discussed before offering conclusions.

13.2  Context and background 13.2.1  Lean approach and performance measurement Lean thinking is about eliminating waste and creating value.Womack and Jones (1996) summarized the lean approach as five key principles: specifying value, identifying value streams, making value flow (by eliminating waste), letting the customer pull value, and pursuing perfection or continuous improvement. In turn, Pavnaskar et al. (2003) claim that true advances, however, come from exposing manufacturing waste. For this purpose, Shingo (1992) identified seven different types of manufacturing waste: overproduction, waiting time, transport, inventory, motion, defects, and processing. Similarly, Shah et al. (2003) showed that the lean production philosophy focuses on avoiding seven cardinal wastes and on respecting customers, employees, and suppliers (Schonberger, 1986). In this study, the focus is both on the processes (eliminating the waste) and on promoting the daily work of employees and management and improving customer relationships.The lean approach aims to shift responsibility to lower levels rather than depending on direction from leadership. It promotes a team-​based multi-​ talent work environment to ensure operational flexibility, and it encourages continual training, learning, participation, and empowerment (Olivella et al., 2008). According to Eaidgah et al. (2016), taking information to the process owner level can impact daily workflow by giving workers more responsibility for their own processes, enabling their participation in decision-​making, and encouraging them to participate in continuous improvement projects. Adopting lean processes requires big changes to the management of operations and production. For best success, management practices, methods, and tools should be completely overhauled. Vartiainen (2007) suggested that adapting to new working methods means adjusting the physical environment

Real-time performance measurement  167 to meet the requirements of the task, enabling the digital environment to use different spaces and make knowledge and information sharing possible, and altering the social environment to support the new working methods. In this context, performance measurement systems are also needed to rapidly adjust to the changes in the operating environment brought about by adopting lean processes. The performance measurements should ensure the company achieves its purpose, plans and targets, and organizational control. Any process in which a person (or group of persons) intentionally affects what another person, group, or organization will do should be monitored (Tannenbaum, 1968). According to Nudurupati et al. (2016), performance measurement systems must be more dynamic to respond to constant changes in the external environment. They explain that organizations must deal with different varieties and volumes of data to gain a competitive advantage, which forces them to refocus their measurement efforts to include evaluation of performance over a wider network involving various stakeholders.Traditionally, performance measurement has suffered from an inability to capture real-​time data to represent actual situations (Hwang et al., 2017). In general, there seems to be a consensus that the fundamental purpose behind performance measurement may be changing. The emphasis on control is diminishing, and the emphasis on learning is increasing (Bititci et al., 2012). 13.2.2  Digital twins and performance measurement The rise in digital technologies, such as digital twins, makes available new platforms to enable the rapid reactive ability of performance measurement systems to facilitate learning (Horváth and Szabó, 2019; Yin and Qin, 2019). According to Horváth and Szabó (2019), for example, applying continuous real-​time performance measurement data to corporate managers can improve their decision-​making and the quality of their employee and company performance appraisals. Yin and Qin (2019) suggest that a smart performance measurement system may provide flexible and customized operation as well as interoperability and intelligent real-​time feedback features to measure, monitor, and improve collaboration in product design. In general, there has been scant empirical research into how the new digital technologies, such as digital twins, support the way organizations measure performance. Most digital twin studies focus more on the engineering of the platform and less on its application (Martinez et al., 2018). However, there is some empirical evidence revealing the general level benefits of digital twins. Defined as the digital replica of the physical assets, digital twins provide huge potential for interoperability and fusion between the physical world and the digital world of production (Liu et al., 2019). Digital twin models assist companies in adapting to the changing operating environment (Qi et al., 2018; Min et al., 2019), making decisions (Liu et al., 2019;Wang et al., 2019), reducing design time (Tao et al., 2019; Zhong et al., 2015; Wang et al., 2020), optimizing production (Zhong et al., 2015; Qi et al., 2018; Bao et al., 2019; Min et al., 2019;

168  Mira Holopainen et al. Wang et al., 2019; Zhou et al., 2019), and improving financial value (Zhong et al., 2015; Min et al., 2019). Tao et al. (2019) present that the digital twin is mostly used for fault diagnosis, predictive maintenance, and performance analysis. Relatively few efforts have been devoted to more innovative design processes or innovations. Zhou et al. (2019) suggest that production optimization can be accomplished via an intelligent analysis and decision-​making process enabled by dynamic knowledge and skills. Information collected and stored by a digital twin is presented in a digital form to discover, analyze, manage, and optimize to improve production performance (Bao et al., 2019). Digital twins reduce challenges caused by physical distance and thus assist in detecting machinery status and monitoring component health (Qi et al., 2018). A digital twin also facilitates customer engagement by giving them the opportunity to see both the physical and digital version of a product (Tao et al., 2019). This engagement reduces design time, because product details are shared with customers early on giving them the opportunity to influence development and ensure the product fulfills their requirements (Wang et al., 2020).

13.3  Methodology This chapter explores the benefits of the digital twin and real-​time performance measurement for a lean manufacturing company. The research was based on a qualitative case study conducted with a Finnish assembly manufacturing company. A qualitative research approach is appropriate when the study focuses mainly on the perceptions and experiences of people. Empirical data were gathered in 2019 using several qualitative data collection methods including focus groups, semi-​structured interviews, observation, and the utilization of secondary data. A player in the drive technology industry, the subject company delivers customized drive technology solutions to its customers. This includes maintenance services and products from other OEMs. Maintenance services represents a significant and continuously growing share of the annual turnover, which is about 41 million euros at present. This research study focused on the maintenance service process for gear motors and industrial gear units. The process comprises many different steps, beginning with the customer service request and product receipt and ending with product delivery and billing. Responsibility for carrying out these steps is shared among various departments. Significantly, suppliers and customers also influence the process. Data collection for the study was divided into four main phases: two focus groups, observation and utilization of secondary data, and semi-​ structured interviews.The research process began with two focus group meetings between representatives from the subject case company and the research group to discuss the context and the planned content of the research. This was followed by two weeks of observation by the research team of the maintenance services process. This observation and utilization of the company’s own secondary data helped

Real-time performance measurement  169 the team to develop a preliminary understanding of the process steps and the current state of the maintenance services process and subsequently identify any process development needs. The last phase, semi-​ structured interviews complemented this view. The aim of the interviews was to collect information on the current state of the maintenance services process, establish a possible target level for the maintenance services process, and define the benefits to the company of the digital twin and real-​time performance measurement. Initially, the focus was on what kind of information is needed by workers and management from different departments within the company and what challenges are faced in daily operations. At the same time, a possible target state was defined. Target definition focused on getting information from the interviews on how workers want to carry out daily operations, what kind of process information they would want to see, and what would be possible indicators to monitor and control processes in real time. Finally, the last part of the interview explored the benefits of the digital twin and real-​time performance measurement of the maintenance services process for the company, its employees, and management. In total 13 interviews were carried out in the offices of the subject company to learn about their maintenance services process steps from different department level perspectives and to ensure that worker voices were being heard. Representatives from the operational level, sales, and management were interviewed. More specifically, the interviewees included two service technicians, two service engineers, one dispatcher, one service manager, two sales engineers, two area sales managers, one sales manager, one factory manager, and the CEO. All interviews were recorded and transcribed. Interview durations were from 40 min to 80 min. After collection, data were analyzed independently by a single researcher, and then the research group as a whole discussed the results to establish a common view. Finally, the results were discussed with the company.

13.4 Advantages of the digital twin and real-​time performance measurement A digital twin provides real-​time information about process operation; the current state of the process, its history, and possible future directions.This chapter examines, how real-​time performance measurement of the lean process and process information could improve operational process performance, support Lean principles, and promote the daily work of employees and management. The advantages that a digital twin and real-​time performance measurement brings to the subject case company are examined from the perspectives of the stakeholder and the process. 13.4.1  Advantages to stakeholders Real-​time lean-​process performance measurement and process information can be used for different purposes. With a digital twin, the company can monitor

170  Mira Holopainen et al.

Management

• • • • • •

A tool for daily management Facilitate sharing of responsibility and control Support fact-based management Support decision-making Facilitate the management of operations Give better understanding of the company`s current situation

Worker

• • • • • • •

Improve information retention Improve and harmonize understanding and facilitate communication Increase collaboration and integration among workers and different departments Motivate Support learning Help focusing attention on the right things Promote the achievement of goals

Customer

• • • • •

Improve customer relationships Speed up customer response time Improve understanding Increase customer satisfaction Increase customer value

Figure 13.1 The advantages of the digital twin and real-​time performance measurement to company’s stakeholder.

and control the maintenance services process and its performance in real time. This solution will give stakeholders better information visibility and give better accessibility to management, sales and customer service, process planning and scheduling, and operational workers. In the following paragraphs, the focus is on how a digital twin and real-​time performance measurement can improve the daily work of employees and management and promote better customer relationships. Figure 13.1 illustrates the benefits to the stakeholder. 13.4.1.1 Management In the subject case company, the digital twin and real-​time performance measurement were regarded as a daily management tool to support everyday management by providing real-​time information of the process and its performance. The digital twin and real-​time performance measurement of the process were also seen as part of Lean manufacturing, supporting the accomplishment of Lean principles, and sharing responsibility with lower levels: It helps with daily management, because the idea is not that management tells everybody to do something, but rather that we have a process, everyone has a role to play in it, and then with visualization and Lean, everybody can see what is really going on and react to it. In addition to the above, the digital twin and real-​time performance measurement was seen to support fact-​based management, facilitate the sharing of responsibility and control of the process, support decision-​making, facilitate

Real-time performance measurement  171 the management of operations, and give better understanding and knowledge of the subject company’s current situation. With help from the new visualization techniques, management enjoyed easy access to the real-​time information about the process and its performance regardless of time and place covering areas such as maintenance products information, their lead times and target times information, information about the process work load and resources, warehouse information, employee information, and so on. 13.4.1.2 Worker Interviews revealed that the digital twin and real-​time performance measurement of the process enables worker engagement in the lean process by giving transparent information of process performance. The benefits for the workers were classified into the following: • • • •

improving information retention, improving and harmonizing understanding, facilitating communication, increasing collaboration and integration among workers and different departments, • motivation, • supporting learning, • focusing attention on the right things, and • promoting the achievement of goals. One of the interviewees described the improvement in information retention as follows: “If I see a green or red ball, I still remember it in my dreams, but if I see black text on white background, I forget it easily.” In the subject case company, workers face information and communication challenges. The information, such as lead times, is monitored manually and on a case-​by-​case basis. In some cases, the correct information is hard to find, it is scattered across different locations, and the reliability of the information may be poor. Communication between departments regarding process operation was also perceived as challenging. With the digital twin and real-​time performance measurement, essential real-​time information is found in one system, solving many of the communication challenges. “With digital twin and information visualization, we would speak the same language about things. We don’t need to speculate about things, and it could help us to understand each other better.” The digital twin and real-​ time performance measurement also helped workers to focus their attention on the right things and better achieve goals such as maintenance lead times. “When you could see are you on the way achieving goals, you could personally lead your own work to make it.” In the subject case company, the achievement of lead time goals is linked to reward systems. With the digital twin and real-​time performance measurement, goals become more transparent to every worker, enabling workers to see the impact of their own

172  Mira Holopainen et al. contributions to achieving them. This also increases worker motivation and efficiency. 13.4.1.3 Customer Creating value for the customer is one of the key principles of Lean. In this paragraph, the discussion is about how the customer benefits from the implementation of a digital twin and real-​time performance measurement. The implementation potentially impacts customer relationships, understanding the process from the customer’s perspective, shortening customer response times, and increasing customer value and customer satisfaction. The digital twin and real-​time performance measurement helps to develop customer relationships. If customers come to visit us, they will see from our metrics that our delivery times have been met, how much goods we have and have left, and so on. Visualization would also make it easier to present our working spirit for visitors. In addition, they help to meet customer performance challenges in the company, such as slow customer response times. “Perhaps even the customer could be shown statistics on their maintenance service, such as what’s the benefit of their faster response time, so they can improve their own efficiency, when answering to our questions.” 13.4.2  Advantages by process perspective The benefits of the digital twin and real-​time performance measurement were also considered from the process and the company perspective. From the process development point of view, this distinction was particularly important. By enhancing the performance of the process, cause-​and-​effect relationships can be identified and marked for improvement to improve the overall performance of the company. Figure 13.2 illustrates the benefits of the digital twin with respect to the performance of the maintenance services process for the subject case company. According to the case study, many process benefits can be achieved through implementation of a digital twin and real-​time performance measurement.The digital twin and real-​time performance measurement of the process improves process monitoring and control, helps detect problems and enhance response to those problems, improves security of supply, facilitates anticipation, reduces errors, increases transparency, improves flow, saves time and resources, supports continuous improvement, helps detect causal relationships, reduces useless communication, improves access to information, and simplifies the process by making it more visible. One of the most important of these benefits is the improvement of process monitoring and control, which can be seen linked to other benefits. Enhancing process monitoring and control can also help in meeting the performance challenges of the maintenance services process, such as difficulties in finding the right information and achieving reliability targets,

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Process More effective process monitoring and control

Helps to detect problems and faults Enhances response to problems

Improves security of supply

Facilitates anticipation

Improves flow of the process

Reduces errors

Improves transparency of the process

Supports continuous improvement

Saves time and resources

Reduces useless communication

Helps detect causal relationships

Simplifies the process

Improves access to information

Increase efficiency

Increase productivity

Increase customer satisfaction

Increases profitability

Improves overall performance

Figure 13.2 The advantages of the digital twin and real-​time performance measurement to the performance of the process and the company.

Real-time performance measurement  173

Company

174  Mira Holopainen et al. traceability challenges of the different factors, such as lead times, communication and information sharing challenges between departments, and achievement of the performance goals. In the subject case company, process monitoring is currently done manually, which takes time and resources and leads to the introduction of errors.With the digital twin, monitoring and control is more automated, and resources can be freed up and allocated to core operations. The potential benefits for the subject case company are not mutually exclusive. For example, more efficient process monitoring can also lead to faster delivery times, which in turn can contribute to increased customer satisfaction and value. Improving the process with digital twin and real-​time performance measurement can also impact efficiency, productivity, and company profitability as well as its maintenance service, eventually leading to an overall performance improvement.

13.5  Conclusion This chapter reports on a qualitative case study that was conducted with the cooperation of a Finnish manufacturing company. The purpose of the study was to explore the advantages that a digital twin and real-​time performance measurement offers for the Lean manufacturing process. The study focused on these benefits from the perspectives of the subject case company’s maintenance services process and its stakeholders. The study’s main contributions are as follows. Firstly, the study shows that the digital twin and the real-​time performance measurement improved the maintenance services process. According to the results, the main benefits were improved process monitoring, control, and transparency.The results also reveal that the digital twin and real-​time performance measurement of the lean process positively impacted the quality of leadership and customer relationships. Secondly, the digital twin implementation facilitated operational level performance measurement by providing real-​time material and information management about the order-​delivery process. This was realized via the digital twins, because they enabled worker engagement in the lean process, increased collaboration and integration among different levels, and facilitated communication. These will potentially affect productivity, profitability, and finally overall performance. In terms of managerial implications, what has been learned suggests that to maximize benefits and support Lean principles, the digital twin and real-​ time performance measurement must be linked to continuous improvement projects. In the subject case study, only one process was considered, however, the digital twin and real-​time performance measurement can also be used to develop the company’s other processes. Also, the benefits of digital-​twin-​ based real-​time performance measurement can be enhanced by integrating the system with other company systems. Further research considering the implementation of a digital twin is recommended to determine the added value of digital twinning and real-​time performance measurement to the company’s

Real-time performance measurement  175 maintenance service. Further studies could also examine the characteristics and uses of digital twins with larger data sets.

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176  Mira Holopainen et al. Shingo, S. (1992). Production management system: improving process functions. Productivity Press, Cambridge, MA. Tannenbaum, A.S. (1968). Control in organizations. McGraw-​Hill, New York. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., Song, B., Guo, Z., Lu, S. C.-​Y. & Nee, A. Y. C. (2019). Digital twin-​driven product design framework. International Journal of Production Research, 57(12), 3935–​3953. Tezel, A., Koskela, L. & Tzortzopoulos, P. 2009. The Functions of Visual Management. Paper presented at the 6th International Research Symposium, Salford, January 27–​28. Van Assen, M. & De Mast, J. (2018).Visual performance management as a fitness factor for Lean. International Journal of Production Research, 57(1), 285–​297. Vartiainen, M. (2007). Analysis of multilocational and mobile knowledge workers’ work spaces. In International Conference on Engineering Psychology and Cognitive Ergonomics (pp. 194–​203). Springer, Berlin, Heidelberg. Wang, J., Huang,Y., Chang, Q., & Li, S. (2019). Event-​driven online machine state decision for energy-​efficient manufacturing system based on digital twin using max-​plus algebra. Sustainability, 11(18), 5036. Wang, Y., Wang, S., Yang, B., Zhu, L., & Liu, F. (2020). Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: architecture, control mechanism, application scenario and benefits. Journal of Cleaner Production, 248, 119299. Womack, J.P. and Jones, D.T. (1996). Lean thinking: Banish waste and create wealth in your corporation. Free Press, New York. Yin,Y. & Qin, S. F. (2019). A smart performance measurement approach for collaborative design in Industry 4.0. Advances in Mechanical Engineering, 11(1). Zhong, R. Y., Huang, G. Q., Lan, S., Dai, Q. Y., Chen, X. & Zhang, T. (2015). A big data approach for logistics trajectory discovery from RFID-​enabled production data. International Journal of Production Economics, 165, 260–​272. Zhou, G., Zhang, C., Li, Z., Ding, K. & Wang, C. (2019). Knowledge-​driven digital twin manufacturing towards intelligent manufacturing. International Journal of Production Research, 58(4), 1034–​1051.

14  Using real-​time simulation in company value chains and business models for value creation Maya Kristina Cheikh-​el-​Chabab, Olli Kuivalainen, Ulf R. Andersson, Roope Eskola and Aki Mikkola

14.1  Introduction In today’s rapidly changing global markets, industries are becoming not only interconnected, but also interdependent. Digitalization and globalization are increasing the pressure on companies to remain competitive to survive. Merely adapting technology is not enough. Companies must also understand business trends and the complexity of modern technology. Digitalization, in particular, has resulted in rapid development and a new complexity in electronics that is challenging upper and middle management to gain a better understanding of the new and emerging needs of their businesses.They must adapt by innovating and updating their business models to ensure their companies can fully benefit from the new technologies and offer their customers better value. Only a small percentage of companies believe existing business models will be sufficient to keep them profitable and economically viable as their industries continue digitalizing course and speed (Bughin et al., 2018). Revolutionary changes in business come about whenever growth of global innovation and competitiveness gives rise to advanced technologies that offer substantial benefit to industry. The introduction of water-​and steam-​powered mechanical manufacturing at the end of the 18th century (i.e., the “first wave” or industrial revolution), the division of labor in the beginning of the 20th century (i.e., the “second wave”), and the appearance of programmable logic controllers for production-​process automation in the 1970s (i.e., the “third wave”) were the main influencers of such revolutions (Brettel et al., 2014). The latest transformations have introduced the business world to a whole new possibility of interactions between humans and machines in a cyber-​physical world through a large network bringing upon a “fourth wave”. Industry 4.0 refers to the emergence and diffusion of a range of new digital industrial technologies, e.g., in relation to automation and data exchange in manufacturing technologies (Strange and Zucchella, 2017; Hannibal and Knight, 2018). In addition to technological innovations, companies have also had to undergo a huge shift in their organizational structure to cope with the new market complexity. Scholars have also recognized a shift from mass

178  Maya Kristina Cheikh-el-Chabab et al. production to customized production, moving toward the co-​ creation of products with customers and that transformation’s potential effects on value propositions and business models. This chapter examines real-​time simulation as a result of the fourth wave of industrial revolution that has been triggered by the internet. Real-​time simulation is an important result of this latest revolution. It is a technology that is evolving rapidly and finding its way into specific industrial applications, because real-​time simulation and its accurate physics-​based representations resolve real-​time problems by producing meaningful and timely information about product behaviors (Mevea, 2018a; Jaiswal et al., 2019). Real-​ time simulation can accurately predict –​in real time –​the dynamic behaviors of complex mechanical systems, such as mobile machines (Khamim et al., 2018). Real-​time simulation techniques are being applied to develop advanced operator training simulators (Mevea, 2018a). Furthermore, several manufacturing companies use real-​time simulation to improve their production processes, and real-​time simulations can serve as a vehicle to demonstrate to potential customers the technical features of a product. Consequently, new applications in product development and beyond are emerging that account for the needs and wants of both customers and operators throughout the whole value chain process, a critically important benefit. The possible next step is taking simulator-​driven design methodologies to a new level by developing real-​time simulator-​driven processes. This development will provide visibility and accessibility to multiple stakeholders in every part of the product lifecycle and therefore enhance the potential of new business models to drive increased competitiveness. However, this new field of technology is not fully mature and is currently only being applied in limited cases, so there are many benefits yet to be discovered and proven. This chapter describes how incorporating real-​time simulation in different value chain processes can affect business models and benefit various stakeholders. The primary objective is to explain how real-​time simulation tools can increasingly represent real-​world functionality in today’s businesses and emerging industries. This innovation has increased global competition to raise product quality and lower production costs and has ensured real-​time access to relevant product and production data for the involved parties.Therefore, companies are weakening the barriers to participation in their product development and support processes and giving multiple parties better access to data by exchanging it through autonomous systems embedded throughout the entire value chain (Brettel et al., 2014). To cope with these technological changes, industries have had to evolve and face the changing market. In this new environment, they are having to think more about creating value based on real-​time simulation.

14.2 The effect of digitalization on the market Impacting how companies do business, digitalization has launched a number of new trends. To better understand these trends, a better understanding of

Real-time simulation in value creation  179 digitalization is needed. Some managers view digitalization as an upgrade of what their IT functions do for the company. Others are interested in digital marketing and sales. Bughin et al. (2018) defined digitalization as “the nearly instant, free, and flawless ability to connect people, devices, and physical objects anywhere”. I-​SCOOP (2019) referred to digitalization in business as enabling, improving and/​or transforming business operations, business functions, business models and processes, and activities by leveraging digital technologies and making broader use of digitized data by turning it into actionable knowledge with a specific benefit in mind. This definition takes into consideration the importance of data gathered from different digitalization means. Other definitions have included the environmental aspect of digitalization and the adoption of digital technologies across all possible societal and human activities. However, in this chapter, the focus is on the definitions proposed by Bughin et al. (2018) and I-​SCOOP (2019). In 2018, CIMdata published an eBook entitled Digital Twins: Changing the Way We Engineer Validate, Market, and Operate our Products. The book introduced four different trends in business launched by digitalization (CIMdata, 2018). The first is increased complexity, whether in product or ecosystem.1 Product complexity does not only come from an increased number of assemblies, but also from current customer expectations when using electronics, software, and embedded systems, all of which are taken for granted. In addition to an increasing need to interconnect technologies, ordering and manufacturing are being challenged to meet sustainability conditions. As for ecosystem complexity, this phenomenon is shown in digitalization covering the entire product lifecycle process. Environmental responsibility is on one side and social responsibility is on the other as companies manufacture products that are increasingly interconnected to meet society’s needs. The second trend is giving the customer more choice. Today’s customer demands flexibility and is given a wide range of choices, if not a fully customized product or service. Since companies are gaining access to much better means, materials, and solutions; customers expect reliable products of superior quality that have been well tested before product launch. Customers are also participating more in the feedback process and codeveloping products by providing improvement ideas. And, more companies are expected to provide customers more involvement opportunities. The third trend is digitalization competitiveness, where companies not only focus on bringing improved and well-​tested products to market, but they also focus on doing it quickly, responding rapidly to market changes to stay competitive in their field. CIMdata explained that virtual capabilities must be applied at all stages of the engineering process, from inception through product development to manufacturing to service. This requires data and process management, visualization, collaboration, and predictive capabilities. Achieving digitalization does not mean that a company has everything in digital form, but rather that it is capable of capturing and analyzing data and then using it for decision making.

180  Maya Kristina Cheikh-el-Chabab et al. The fourth trend is sustainable innovation, which is realized through the virtual environment. Virtuality impacts company competitiveness and profitability, resulting in transformation of the value proposition and the business model. CIMdata noted some of the virtual engineering practices in businesses. These include system modeling and simulation (SMS), BigData, digital twins, IoT (Internet of Things), IIoT (Industrial Internet of Things), and more. The increased use of advanced technologies has accelerated these trends, and industry must work constantly to keep pace. These trends are driving the need for further research on each of these technologies to improve their implementation and better understand their impact. Real-​time simulation is already being used by some companies, but not all its benefits have been fully employed throughout the whole value chain. Indeed, real-​time simulation is mostly found as part of product development and not used in other value-​creating activities. This chapter therefore focuses on real-​ time simulation and its potential benefits.

14.3  Real-​time simulation models and How they Create Value for Customers A complete real-​time simulation model integrates the appropriate elements; including the models of environment, mechanics, control system, and user input; and predicts their interaction to simulate the dynamics of an entire system (Mevea, 2018b). The user’s main role is to provide input signals via the control console to direct the control system. The control system is where most of the input/​output data is processed and synchronized with other subsystems. Actuators produce the forces needed to drive the mechanical subsystem. For example, hydraulic actuators output the required forces to the mechanical system, which responds by moving within its motion constraints. Multibody system dynamics is the basis of the mechanical subsystem modeling, and it includes the description, e.g., of the bodies, joints, contacts, and tires. In a multibody approach, the set of position coordinates can be defined using generalized global or relative coordinates (Jalon and Eduardo, 2012). A selected set of coordinates is also used to define the velocities and accelerations of the system bodies. To express the equations of motion, the dynamic equilibrium of the system must be defined. This equilibrium can be determined using an approach such as the principle of Virtual Work. A multibody system is a constrained system, so the constraints must be considered when defining the equations of motion. There are several ways to express them including coordinate partitioning, the penalty method, the augmented Lagrangian method (Bayo and Ledesma, 1996), the collision response model (Korkealaakso et al., 2007), and the lumped LuGre friction model (Astrom and Canudas-​De-​Wit, 2008). Furthermore, the hydraulic system model that describes the actuators is often based on lumped fluid theory, where the hydraulic circuit is divided into

Real-time simulation in value creation  181 discrete volumes with the assumption that the pressure is distributed equally (Watton, 1989). Simulation tools such as these have helped decrease cost and improve simulation capabilities making it possible to model and predict real-​world behaviors. As a result, the capabilities of real-​time simulation and its ability to solve real-​ world problems have improved. In addition to the reduction in modeling cost, the new techniques have been made more available and accessible to a larger number of users for multiple applications (Bélanger et al., 2010). The importance of properly understanding the needs and wants of customers during the product design process, and therefore involving customers in the actual process through a virtual prototyping experience with real-​time simulation tools, has driven the need for improved real-​time simulation. In real-​ time simulation, the time required to perform computational functions and accurately compute equations must be synchronized and must be faster than the simulation time-​step for the simulation to acceptably represent its physical counterpart with equivalent performance (cf., Bélanger et al., 2010). For each time-​step, the simulator takes the following actions (Bélanger et al., 2010): 1. 2. 3. 4.

Reading inputs and generating outputs, Solving model equations, Exchanging results with other simulation crossing, and Waiting for the start of the next step.

As implied in the previous steps, all output data can be exchanged and shared. This capability enables a new form of communication between stakeholders, which could include current or potential customers, other dealers involved in the sales action such as sub-​retailers and wholesalers, partners and investors, or any other party that makes use of the simulator-​gathered real-​time data. Traditionally, product and service development decisions are made, for the most part, by the few experts tasked with directly addressing development issues and questions (Mohr et al., 2010. A certain approach could even be paternalistic. For example, see Baden-​Fuller and Haefliger, 2013). In this approach, customer needs and wants are often solicited via verbal or written interviews. For a completely new product, this approach is problematic, because describing a concept-​level product to customers is difficult, and it is equally difficult for customers to fully understand a new product’s potential advantages or disadvantages. Furthermore, if the product is the result of radical innovation, customers may not even be able to articulate what their specific needs might be (Mohr et al., 2010). This problem can be alleviated by developing real-​time simulation-​driven processes, which can be accomplished, in practice, by developing a toolset that gives multiple stakeholders access to machine research and development, production planning, and customer services via virtual worksites that can provide

182  Maya Kristina Cheikh-el-Chabab et al. fully configurable, real-​time, virtual prototyping. To this end, it is critical to employ server-​based virtual environments (Figure 14.1). With a server-​based virtual environment, any number of stakeholders can simultaneously work with the virtual machine. The environment also makes it possible to set up and modify included models. All in all, these processes can function as tools for open innovation and crowdsourcing (Füller et al. 2013).

14.4  Business model canvas as a tool to analyze the value chain As mentioned in the preceding paragraphs, it is challenging for upper and middle management to understand the new and emerging needs of their businesses and adjust to them by innovating and updating their business models. However, so the added value produced reaches customer, it is of great importance to innovate and update business models to ensure they can fully benefit from the new technologies introduced to industry. Studying the benefits of real-​time simulation helps companies to better capture its full potential value and use it more extensively. With this use comes the need to adjust the business model to provide customers and other players in the value chain technological value. This can be done by considering and applying real-​time simulation to different actions along the value chain. However, to identify exactly where this technology best functions, the business model canvas must be clarified. As Osterwalder and Pigneur (2010) described,“The business model describes the rationale of how an organization creates, delivers, and captures value”. His idea also identified nine building blocks that comprise the business model canvas. These include the following. • • •

• •

Value proposition represents what the company is offering and makes customers consider buying. It shows the bundle of services and products that create value. Channels are the way the company intends to reach its customers and deliver its products or services by different means of communication, distribution, and sales. Customer relationships comprise the connections that a company establishes with each of its customers. These relationships could be automated or personalized to build a customer base, retrieve customers, or increase sales. Customer segments are the various market segments for which the organization is creating value, that is, its’ most important customers. The company can serve one or more customer segments. Revenue streams are how the company makes money. Here, managers must determine what exactly customers value and what they are willing to pay for it.

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Real-time simulation in value creation  183

Figure 14.1 Virtual environment (Mevea, 2018a, 2018b, 2018c). Courtesy of Mevea.

184  Maya Kristina Cheikh-el-Chabab et al. • • •

•

Key resources are the most important assets held and owned by the company, rented from partners, or rented to other companies.These assets are important to offering value to customers, building relationships, and gaining revenue. Key activities serve as the most important activities for the company to deliver value, maintain customer relationships, and make revenue. They vary depending on the type of business model. Key partnerships constitute the partnership network of suppliers and partners. These partnerships take the shape of cooperation, joint ventures, alliances, and buyer–​ supplier relationships. The partnership network is especially important, because it results in and optimizes economies of scale, reducing the risk of uncertainty and granting the privilege of acquiring a particular resource or activity the company needs. Cost structure is understanding what the company must pay to create and deliver the value proposition. Cost structure has an effect over different blocks in the business model including value proposition, revenue streams, and long-​term customer relationships.

This business model canvas has challenged previous assumptions that there is one coherent and understandable business model. It has made it easier for companies to follow these steps and compare themselves to other company models (Osterwalder and Euchner, 2019). Innovating business models seems to be a method for companies to commercialize innovative ideas and technologies. However, business model innovation also complements process, product, and organizational innovation, which promotes more collaboration. Chesbrough and Rosenbloom (2000) stressed the importance of having a proper business model that works with a company’s new technology to create value and manage, not only technological uncertainties, but also economic and market uncertainties. They also explained that learning and searching for effective business models makes successfully adopting technology more likely. This search ensures that market needs are discovered and that customers receive best value. Zott et al. (2011) concluded that the business model plays an important role in unlocking the potential values of using technologies and converting them to potential outcomes. Companies can benefit from modifying their own business models to use real-​time simulation. This way, they can remain competitive and offer value to their customers.

14.5 Applying real-​time simulation to different alue chain activities 14.5.1  Applying real-​time simulators in R&D and product development Real-​time simulation is being applied in many areas including traffic control, movies, gamification, and HVAC (heating, ventilation, and air-​conditioning) systems (Pell et al., 2016; Jaiswal et al., 2018; Trcka and Hensen, 2010).

Real-time simulation in value creation  185 Companies are therefore becoming more aware of the advantages of using simulation to improve their businesses. More companies, such as Siemens and other German machine tool vendors, are developing simulation procedures that use data collected from machinery (Rubmann et al., 2015). According to Rubmann, this has enabled operators to virtually test and optimize machine settings for new products before real-​world introduction, thereby improving production quality and reducing setup times for the real machining processes by as much as 80%. Product development is essential because it can influence an organization’s competitive success, adaptation, and renewal (Brown and Eisenhardt, 1995). The vast amount of literature focusing on product development has described numerous reasons for successful new product introductions, as well as reasons for failure. A few of these reasons are addressed here. Ancona and Caldwell (1992) discovered that the most successful product development teams employed a wide-​ranging external communication strategy, combining so-​called ambassador and task coordination behaviors, that helped them secure resources, gain task-​related information, and enhance product development performance. This type of communication connects product development to various stakeholders. There is an analogy between Ancona and Caldwell’s (1992) ideas and the use of real-​time simulation in product development. Von Hippel (1982) showed that a real-​time simulation process makes it easier for a company to make customers an important resource for their product development efforts. Djelassi and Decoopman (2013) reported that crowdsourcing can be helpful in mobilizing selected customers. Crowdsourcing can be defined as “the act of a company or institution taking a job traditionally performed by employees and outsourcing it to an undefined, generally large group of people in the form of an open call” (Howe, 2006a, 2006b). Crowdsourcing is a form of user-​driven innovation and value co-​creation through which companies can apply innovation (Hopkins, 2011). Real-​time simulation can be used as a platform that fosters crowdsourcing-​based outsourcing. One example of using a “virtual machine” in product development might be its application in the development of new car models.Taking a simulator-​driven approach would enable the recruitment of a large number of test drivers who could then test-​drive several (virtual) prototypes or beta versions.This approach combines the classic idea of testing various product versions with customer-​ driven innovation. If this virtual machine approach is well executed, test users can experience virtual driving under assorted conditions and with different vehicle features over a substantially shortened schedule. Receiving customer feedback effectively during the early phases of product development is an important benefit of a simulator-​driven process, as it enables the involvement of a large group of potential users in the development process. To better involve potential users though, game-​like elements can be added to the testing platform. Indeed, gamification can boost the commitment of test users and even encourage the participation of yet a larger number of participants (Hamari et al., 2014).

186  Maya Kristina Cheikh-el-Chabab et al. The R&D real-​time simulator approach offers benefits for both the product development cycle and the product itself. For example, it can result in better concept design and material savings for the final design, optimized based on data from a larger pool of potential users than would be available using classic marketing research methods. This real-​time simulation benefit type could be common in companies that have a complex and expensive production process (cf., Bélanger et al., 2010; Jaiswal et al., 2019). Factories that are responsible for developing agricultural machinery, for instance, invest a lot of effort and resources during the model development process. A tool that can minimize risk and optimize test machine settings before building the actual machine would be welcome. This is how real-​time simulation can be utilized during the R&D process. It has proven to be an effective tool for machinery design, because the simulation model makes it possible to quickly understand how a machine’s dynamic behaviors are affected by changing design variables. It can replace experimentation and consequently accelerate product development. Mevea (2018b) discussed that virtual prototyping in real-​time simulation achieves significantly shorter lead times and decreases the cost of prototyping by reducing the need for numerous physical prototypes. It also tests the individual components of the product, how it performs in its environment, and how well it carries out the tasks it was designed for. Operator experience is also critical when considering the dynamic performance of a machine. Real-​time simulation gives a machine operator the opportunity to actively engage with a machine in operation, so the training simulator should feel and behave as realistically as possible.This is only possible if the real-​ time simulation model accurately accounts for the multiphysical behaviors of the mechanical components in response to actuations and correctly represents contact behaviors in accordance with control algorithm instructions. 14.5.2  Applying real-​time simulators in training Real-​time simulation techniques are also being used to develop advanced operator training simulators. Compared to traditional training methodologies, simulation-​based user training provides a number of advantages. Mevea (2018a) indicated that simulation could be used to generate training data, as well as to test solutions that can be used after training in various scenarios. For example, as operators are being trained, real-​time simulation gives them experience in likely operating environments subject to a variety of adverse environmental conditions such as wind, rain, or fog. In every case, the simulator can let the operator experience how the machine “feels”. Accidents or injuries to personnel or property that might otherwise occur when an inexperienced operator learns on an actual machine can also be avoided. Moreover, the simulator can be used to take operators through various accident scenarios and instruct them on the most appropriate responses. This application is most beneficial for high-​r isk jobs. For example, the Finnair Flight

Real-time simulation in value creation  187 Academy uses real-​life simulation to train its pilots and crew members, who are responsible for the safety of their passengers (Finnair Flying Academy, 2018). With this technology, they are able to simulate the physics, the flying process, the flying system, and the environment. Therefore, training the operators of hazardous systems with zero possibility of negative consequence is where real-​ time simulation is needed the most, because it provides actual experience with genuine real-​time data to prevent accidents. Further, traditional training processes require real machines that could otherwise be applied to productive service. Using real-​time simulation in the training process frees up real machinery to carry out its intended purpose. Simulation-​ based user training makes it possible to carry out productive revenue-​generating work while simultaneously saving costs and eliminating adverse environmental impact. 14.5.3  Applying real-​time simulators to predict faults In the extant literature, most real-​time simulation-​related articles focus on computational aspects or purposes, such as developing systems and software algorithms. Little attention is given to problem-​solving aspects (De Souza et al., 2014). Some researchers have even discussed the use of simulation to conduct real-​time tests. De Souza et al. (2014) explained that these tests would help develop new embedded algorithms and control techniques for dynamic systems such as motors, industrial processes, automobiles, and aircrafts. On the other hand, real-​time simulation can also be used subsequent to machine introduction to improve value-​chain processes including design, production, and even aftersales services. Trcka and Hensen (2010) mentioned that real-​time simulation can also be applied to make production processes more flexible. They were able to prove that real-​time simulation tools can be used during machine operation to predict and monitor performance and detect and identify abnormalities in system behavior. In their studies, real-​time simulation enabled the system to predict machine errors, a benefit in the R&D stage and a way to improve machine operation in service. For instance, machine faults can be monitored to predict when a machine might break down. To prevent such an event, maintenance could be carried out in advance.This preventative maintenance would save time and effort, and most importantly, enable the machine to continue performing productive revenue-​generating work for longer. The company would not be exposed to an unforeseen breakdown.This is why companies are moving toward predictive maintenance based on real-​time simulation (cf., CIMdata, 2018). Mattera et al. (2018) explained how simulation can be used to reduce energy consumption by predicting faults as well. The simulation in this case would be used to predict the optimal amount of used energy during different environmental conditions so that any deviation from the optimal case would be noted to maintain sustainable and environmentally friendly consumption.

188  Maya Kristina Cheikh-el-Chabab et al. 14.5.4  Applying real-​time simulators in services Outsourcing is a growing business trend that began with the outsourcing of elements of the manufacturing process (Hätönen and Eriksson, 2009). It has continued to include other business functions, such as human resource management and R&D. Many multinational enterprises, such as PC manufacturers, outsource all their major technology requirements, implying that technology is not seen as key to their success or a necessary core competency (Buckley, 2011). More value can be created by other activities such as building a brand. The market research agency Research & Markets (2015) actually forecasted an 8.1% annual growth in R&D outsourcing from 2015 to 2019. Another key reason for this outsourcing development trend is virtual R&D, which offers benefits such as cost efficiency and reduces heavy internal R&D investment risk. However, virtual R&D is also a specialized activity. As a result, there is room for R&D-​and innovation-​shrewd companies to provide virtual environments and simulator-​driven processes to their customers. An R&D company can collaborate with a machine manufacturer and provide the necessary user data collection and testing. Additionally, it may not be cost efficient for every company to develop expertise in market research or to integrate product development processes, because their new product releases may be infrequent. A company specializing in providing these services could thus benefit from economies of scale and more-​established knowledge bases, making this business model a tempting option. Real-​time simulation methods could be used for market research as several test users can try virtual models efficiently. One example of outsourced R&D is the pharmaceutical testing for Food and Drug Administration (FDA) approval in India. Due to lower costs and an abundant educated workforce, many Indian firms have begun to offer services to Western pharmaceutical companies (Manavalan and Sinfield, 2017). Information technology and protocol standardization allow drug testing to be performed in a way that is more cost-​optimized. This has given rise to a multitude of companies that specialize in a particular phase of the drug development R&D process. Similarly, by focusing on real-​time simulation technologies, companies can offer outsourcing services in a particular segment, such as heavy vehicles and machinery, that include events in which customers and users participate in the development process. From these events, the manufacturer can receive market data and valuable knowledge regarding potential customers’ preferences. 14.5.5  Applying real-​time simulators in sales and marketing Schneider and Hall (2011) reported that the “biggest problem” in a problematic new product launch is “lack of preparation”. They suggested that because companies are often so focused on designing and manufacturing new

Real-time simulation in value creation  189 products, they do not put enough early effort into marketing. An immediate and more realistic idea of different value drivers becomes available by introducing community-​based, real-​time tools that simulate real-​world functionality for potential customers. Simulation can substitute for real observation and can provide expansive realistic data. This information on how potential users may cope with different situations can then help marketeers and salespeople optimize products for their intended customer base. A product with a catalogue of value-​creating features serves as a practical example of using simulation to enhance marketing and sales. For example, a car dealer could use a simulator to give potential customers the opportunity to test the effects of a car’s various available options (e.g., a more effective engine). The ease with which customers could try out these extras in real-​life scenarios may result in more of them being sold, which results in a better bottom line for the dealer. Simulations can also provide information about intangible attributes, such as feel, which are less frequently addressed in customer surveys.This would also lead to cost savings for the car dealer, who would be able to stock fewer cars for test drive purposes. In general, the advantages for marketeers and salespeople can be found in customer value analysis, user training, and product demonstrations. Real-​time simulation lets more people participate in the testing phase, as well as in further phases of the product development lifecycle. This provides information for various marketing activities and market research. Simulation data can also be “topped up” with interviews after the simulation itself, where user behavior can be observed in real time by the marketeer. Furthermore, marketeers may be able to develop their market information capabilities, i.e., the processes by which firms can learn about markets and apply this market knowledge along the way (Vorhies and Morgan, 2005). 14.5.6 The effect of real-​time simulation on business models The aforementioned effects of real-​time simulation on the value chain will result in changes to the overall business model. If real-​time simulation is used during the development process, for example, it affects effort and time, because the simulation reduces the need for physical prototyping and therefore affects cost and resource demand.The business model presents a way for the business to capture value and deliver it to customers. In this way, the business model is similar to the value chain, which aims at pinpointing the actions that add value to products. Financial resources are required to begin applying real-​time simulation. In addition, to bring on any new technology, a company must add expertise for that technology to its workforce. So, to ensure continued competitiveness by introducing real-​time simulation, a company must not only commit financial resources, but it must upgrade its human resource as well. The business model would also face some changes regarding key company activities. When it starts using real-​time simulation, more data can be gathered from operating machines, leading to more possibilities to incorporate activities

190  Maya Kristina Cheikh-el-Chabab et al. that benefit from this data. For example, it becomes possible to provide accurate aftersales services by predicting faults using real-​time data. Another way simulation can affect business activities is providing training services for employees or customers. Combining the gains of real-​time data, some B2B companies could benefit from certain partnerships with other firms that provide services with these data, so real-​time simulation provides opportunities to network and cooperate with other partners, which will improve business processes and subsequently the value proposition for customers. The value proposition could be the most affected block of the business model canvas, because many real-​time simulation benefits transfer to the customer. One of these benefits is providing the customer the opportunity to give feedback and participate in developing products and services so that value can transfer to a wider audience. Predicting faults, training, and maintenance could be enhanced with real-​time simulation, thus building more value to pass on as part of aftersales services. Real-​time simulation makes it possible to track data throughout the product lifecycle, leading to greater customer benefit. The ability to share real-​time data enabled by real-​time simulation can enhance a company’s relationship with customers as they become more involved during different processes –​from R&D to when the product or service is in their hands and being consumed. Being able to track the data through the product lifecycle may increase customer trust and loyalty to the company and make them feel safer.2 As a company introduces real-​time simulation, the customer base could be divided into those who favor utilizing such a new innovative technology and those who do not.This technology could also add new segmentation according to the demand for the services and benefits presented by real-​time simulation. If the company targets one or more of the previously mentioned segments, the new technology will generate a new type of customer interested in the benefits that real-​time simulation has to offer before and after the buying action. For instance, customers may be interested in the data collected from their purchased machines and may want to use the data for different purposes to achieve their goals in predicting faults, reducing costs, or optimizing performance. Company stakeholders could communicate and share real-​time data as well. This will necessitate new and effective communication channels to distribute information between company headquarters and its dealers. As for physical channels, simulation could optimize distribution channels to cover as much area as possible with the right timing and portfolio of products introduced as needed. If real-​time storage and vehicle status data is available, the company could further optimize its distribution channels, because accurate input data will help the simulation to model the situation in more detail. All of these real-​time simulation benefits will eventually affect company revenue both directly, by adding more activities and services to the business model or more value to propositions and optimizing offerings (which could increase

Real-time simulation in value creation  191 sales), and indirectly by decreasing costs during R&D, marketing, and other value chain processes.

14.6  Discussion and conclusions Real-​time simulation is a complicated concept for non-​specialized managers, and they must properly understand its functions and applications to correctly use it throughout the business process model and reap its full benefits. This chapter presented some of these applications in different value chain activities such as R&D, product development, and marketing. Notably, advanced simulation technologies make it possible to describe increasingly complex mechanical systems, so the potential benefits and uses of these technologies should be considered. When the R&D and in-​house product development functions of manufacturing companies are already making use of real-​time simulation techniques, a starting point for many, it may also mean that it is time for them to proactively consider developing future real-​time simulation skills to enhance their market research and customer interface management competencies. Real-​ time simulation clearly offers advantages. Simulations can lead to higher success rates for new product launches, as well as cost savings. However, an R&D operation and product development model based on real-​time simulation requires manufacturers to upgrade their capabilities. For example, they must bring on new skills to manage various stakeholders. Required marketing capabilities may also relate to the product development processes, for instance, by which firms develop and manage product and service offerings and market information management (Vorhies and Morgan, 2005). Marketing and sales can also benefit from virtual environments. In a survey focusing on the success factors of Israeli high-​tech startups; product perceived utility, comprehensive market knowledge, reliable marketing plans, and the marketing and R&D relationship were considered to be important (Chorev and Anderson, 2006). Properly integrating real-​time simulation with marketing and sales activities can lead to improvements for all these factors. As customers are able to test a product in a virtual environment, they are able to experience its different utility benefits. At the same time, marketing and sales personnel are able to learn more about the marketplace, that is, their existing and potential customers. This learning should lead to more reliable marketing plans that match targeting, segmentation, and unique selling propositions. Training as a value-​added service may also become more integral in product offerings, which would increase sales. If this information, stemming both from simulation and face-​to-​face interactions with users, can be communicated to R&D via functioning market information management systems, the added value from marketing and sales activities can be used to enhance the entire R&D and product development process. Therefore, the marketing and sales tactics required for various customer segments must still be considered independently, because experience and usage needs may differ segment by segment.

192  Maya Kristina Cheikh-el-Chabab et al. Using real-​time simulations for various purposes can also be a challenge for companies. For example, the large pool of data stemming from simulation users may be difficult to digest. As a result, the outsourcing of simulation activities might be a viable option for many manufacturing firms, which provides opportunities for other companies to master this part of the product development process in the value chain. For the R&D firms that may get the contracts though, there may be a need to upgrade capabilities in relationship management. This chapter presented the possible uses of real-​time simulation for different aspects of the value chain, starting from product development and continuing through customer receipt. Mevea (2018c) discussed the possibility of analyzing machine usage data with digital twins to gain valuable insights into product behavior. This enables operators and consulting companies to find ways to improve machine use. Training as a part of customer service or internal training could both benefit from simulated scenarios in different environments. Predicting faults, in the R&D phase or during machine operation, is another benefit that real-​time simulation can provide that saves time and resources. Aiming to guide managers and increase awareness of this new technology and how it affects business models, this chapter also mentioned the use of real-​ time simulation during the R&D, sales, and marketing phases, as well as for after-​sales service. Ultimately, real-​time simulation was found to impact each of the nine blocks of the business model canvas. Still, changes in business models may differ depending on the industry and the market. This chapter accordingly displayed a general idea that can be adjusted to match a company’s situation at hand. As for future research, it would be beneficial to further explore the benefits of the data gathered by real-​time simulation, how it may affect machine learning, and how real-​time simulation in the value chain can affect different business model blocks. Artificial intelligence is being used on a larger scale, which is encouraging more research on the subject. According to Gartner (2018), 14.2 billion connected things will be in use in 2019. That total will reach 25 billion by 2021, producing an immense volume of data. Therefore, these data are driving the growth of artificial intelligence, leading to the greater possibility of its use in real-​time simulation to teach smart machines that are capable of learning. Another possibility for further research would be to specify the effect and use of real-​time simulation in certain industries to explore how it can increase variety or give stability to the industry employing this technology.

Notes 1 A business ecosystem can be defined as the “organisms of the business world –​ including stakeholders, organizations, and countries –​involved in exchanges, production, business functions, and … … trade through both marketplace competition and cooperation” (see Hult et al., 2020, p. 44).

Real-time simulation in value creation  193 2 Brettel et al. (2014) point out that one key obstacle to the establishment of close collaborations between companies is the absence of trust:This stems from the fact that many managers are not used to share critical information with other companies.

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15  Sustainable competitive advantage through the implementation of a digital twin Juhani Ukko,Tero Rantala, Mina Nasiri and Minna Saunila

15.1  Introduction Digital-​twin technology is revolutionizing industry. Everything in the physical world can be replicated in the digital space using a digital twin (Qi et al., 2020). Many companies and technical fields already use a digital twin to spot problems and increase efficiency using sophisticated models that can mirror almost every facet of a product lifecycle, a process, or a service (Tao et al., 2019; Qi et al., 2020). A digital twin refers to precise virtual copies of machines or systems that include the process of physical-​ to-​ virtual and virtual-​ to-​ physical twinning (Jones et al., 2020; Qi et al., 2020). The combination of both connections allows for continuous cycle optimization, because possible physical states are predicted in the virtual environment and optimized for a specific goal (Jones et al., 2020). Using physical modeling for the real-​time simulation of a vehicle is one example. So far, the discussion on digital twins has mainly focused on the physical modeling of products and production systems, where modeling refers to the process of representing a physical entity in digital forms that can be processed, analyzed, and managed by computers (Qi et al., 2020). In addition to physical modeling, Qi et al. (2020) present that together with AI and machine learning, a digital twin can be used for simulation, monitoring, diagnostics, prognostics and optimization, as well as for the training of users, operators, maintainers, and service providers. Vijayakumar (2020) showed that a digital twin could be effective, for example, in predicting customer needs and sales or in managing a supply chain. It seems that digital twins in all forms can provide comprehensive support for decision-​making covering a wide range of company operations. However, Qi et al. (2020) argue that implementing a digital twin is a complex and lengthy process that needs multiple technologies and tools to work together. They go on to say that many companies and researchers remain unfamiliar with the key technologies and tools of digital-​twin technology, mainly because of this complexity and the difficultly in integrating the requisite engineering disciplines.

Sustainable competitive advantage  197 Based on the notions above, digital twins can be utilized across a wide range of corporate operations. However, implementing a digital twin can be a major investment, and companies must know which operations to focus on in the long run and what type of competencies are needed to succeed in the implementation and use of a digital twin. For example, Jones et al. (2020) state that given the potential costs and challenges for the infrastructure and workflow changes needed to effectively implement digital twins in an industrial context, the lack of a clear understanding of the scale and nature of the benefits that will be gained remains a substantial obstacle. They continue by saying there are very few examples of validation and quantification of any perceived benefits against existing processes and systems with very few papers demonstrating tangible improvements over current norms. Jones et al. (2020) also expressed concern that without substantial effort to describe and quantify benefits, it is difficult to claim that the digital twin concept is the most appropriate solution to the challenges faced by any given industry. Therefore, it is worth first exploring and then justifying what possible strategic directions a company must face. It is equally important to study what kind of competencies the chosen strategic direction demands and what kind of benefits should be expected. In response to the above-​mentioned research gap, this chapter contributes to the understanding of how and under what conditions firms implementing digital twins can build a sustainable competitive advantage. This is achieved through the construction of a multidimensional model for the implementation of a digital twin. Furthermore, nine propositions are forwarded and justified to assist in positioning further research into the consequences of implementing digital twins. This chapter is structured as follows. First, the model and its theoretical grounds and descriptions of the key concepts are presented. In the third section (Section 15.3), the requisite competencies and research propositions are revealed. Next, conclusions for management research and practice are presented. Finally, the chapter presents some limitations and further research directions.

15.2 A multidimensional model for the implementation of a digital twin 15.2.1 Theoretical underpinnings Understanding how and under what conditions firms facing digital transformation, specifically implementing digital twins, can build a sustainable competitive advantage is an unanswered question. Because implementing a digital twin requires a clear understanding of the operation and a collaborative establishment of clear objectives (for example, in terms of promoting continuous change and introducing new methodologies) new competencies and knowledge are needed to stay ahead of competitors. Therefore, the competencies needed to successfully implement a digital twin can be considered an important source of sustained competitive advantage.The dynamic-​capabilities view offers

198  Juhani Ukko et al. great potential for studying this type of change in firms. According to Teece et al. (1997, p. 516), dynamic capabilities are “the firm’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments”. Therefore, the dynamic-​capabilities view deals with the competencies that enable firms to adapt to their operating environment (Teece et al., 1997). A dynamic-​capabilities view is likely to bring value to the digital transformation study, because functioning in a turbulent operations environment requires the firm to focus beyond its traditional core competencies. Teece (2014) uses the term “ordinary capabilities” to refer to the competencies that enable executing administrative and operational assignments necessary to keep the firm in operation. These include accounting and sales, for example, which are easily replicable and by themselves can no longer sustain competitiveness. To achieve sustainable competitive advantage, unique capabilities are needed that are hard to replicate and that govern the rate of change in a firm’s ordinary capabilities (Teece, 2014). These difficult-​to-​replicate capabilities are referred to as “dynamic capabilities” that enable a firm to enhance and direct its ordinary capabilities toward sustained competitive advantage (Teece, 2018). As a result, a firm’s resources must be developed and coordinated to address digital transformation in the operating environment. Dynamic capabilities, therefore, are used in the implementation of a digital twin as an appropriate theoretical foundation to reveal the competencies that shape and direct traditional competencies such as product development, marketing, and production. 15.2.2  Defining a digital twin Along with using the most recent information technologies related to, for example, the internet of things or different types of cloud technologies for data management (Jiang et al., 2014; Xu et al., 2014), the implementation of digital twins is increasing in contemporary operations environments (Tao et al., 2018). At the general level, a digital twin can be considered a digital replication of, for example, a product or physical space. A digital twin is not just a 3D-​level visualization of the physical product, machine, or factory.Typically, it also includes the same functionalities as its physical twin. One of the most commonly recognized definitions of the digital twin was offered by Glaessegen and Stargel (2012). They contended that a digital twin is an integrated multiphysics, multiscale, probabilistic simulation of a complex product that closely replicates its geometry and functionality. Tao et al. (2018) have further argued that a digital twin can be considered an integrated multiphysics and probabilistic demonstration of a complex physical product that utilizes advanced models and sensors to reflect its physical twin. According to Glaessegen and Stargel (2012), digital-​twin technology comprises three parts: the physical product, the virtual reflection of that product, and the connection between physical and virtual life. Further, according to Tao et al. (2019), a digital twin can be considered a bridge or linkage between physical and digital worlds. As such, digital twins give companies the ability

Sustainable competitive advantage  199 to visualize and status their operations from thousands of miles away (Porter et al., 2015). Previously, different types of digital twins have usually been used in error diagnosis, predictive maintenance for factories and machines, and in performance analysis (Tao et al., 2019). Even though these activities are still important and valid uses of digital twins, more recent implementations also consider other important activities. For example, current utilizations include product design and development and sales and marketing activities. Today, companies are more frequently using the rich data provided by digital twins to better understand and operate their connected products (Porter et al., 2015). 15.2.3  Description of the model This chapter examines the pathways for implementing a digital twin. Digital twins in product development, production, marketing, sales, and delivery allow firms to operate sustainably. These physical entities, virtual counterparts, and the in-​between data connections (Jones et al., 2020) enable firms to connect with products and services (Cenamor et al., 2017) with the aim of building a sustained competitive advantage.Therefore, the digital twin concept is dynamic in that a firm’s competencies need to be constantly evolving. In addition, it may take years before digital transformation results in added value for the firm (Kohtamäki et al., 2020). The term “sustainability performance” is used here to describe the comprehensive and long-​term effects that digital-​twin implementation can bring. Digital transformation-​driven digital-​twin implementation shapes and directs a firm’s competencies, which then results in strategically directed outcomes and a further sustained competitive advantage. The various elements of digital-​twin implementation is presented in Figure 15.1.These will be described further in the following paragraphs. 15.2.3.1  Firm competencies In today’s digital transformation environment, where digitalization has changed patterns of work and business, it is necessary for firms to be equipped with different competencies that are compatible with the new ecosystems (Kohtamäki et al., 2020; Longo et al., 2017). Competencies will integrate knowledge and skills to help companies adapt to the new business environment and build successful businesses (Fowler et al., 2000). Firm competencies based on strategy and strategic decision-​making concentrate on the integration of “corporate-​wide technologies and production skills into competencies that empower individual businesses to adapt quickly to changing opportunities” (Hamel and Prahalad, 1990, p. 81). Therefore, to be successful in the current dynamic environment, firms must have strategic competencies that work together to apply all actions and operations to reach the final goal (Matt et al., 2015; Sia et al., 2016). Companies should prioritize their strategic initiatives to best impact their markets (Kohtamäki et al., 2020).

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Firm competencies

Strategic direction

Strategic – the application of all actions and operations to reach firm goals

Product development

Tactical – the utilization of the digital

Production

twin in the operating environment

Marketing and sales

Operational – interaction of the digital twin within firm actions and operations

Supply chain management

Technical – the properties and the

Economic dimension

(sales, market share, profitability)

Social dimension

(renewal, productivity, customer satisfaction, social responsibility)

Environmental dimension

(capacity utilization, resource efficiency)

abilities of the digital twin

External environment

Figure 15.1 Model of a digital-​twin implementation.

Sustainability performance

200  Juhani Ukko et al.

The implementation of a digital twin

Sustainable competitive advantage  201 Strategic competencies encompass the results of a firm’s capabilities to distinctively integrate its resources with strategic business processes (Huikkola and Kohtamäki, 2017). Access to large amounts of data provides opportunities in a digital twin, but without a coherent use strategy, these opportunities can become data-​overload problems (Lenka et al., 2017). Therefore, to support strategic competencies, a strategy is needed to effectively employ digital twinning in the operating environment (Lenka et al., 2017; Kohtamäki et al., 2020). Firms operating in digital transformation without tactic or business model fail to successfully collect, analyze, and exploit the real-​time data (Huikkola and Kohtamäki, 2017). Additionally, when competing in an ecosystem characterized by swift technological growth, operational competencies and routines to support both strategic and tactical competencies are a necessity (Matt et al., 2015; Kohtamäki et al., 2020; Ukko et al., 2019). According to Matt et al., 2015, the cross-​functional characteristic of digital transformation has obliged firms to be aligned with other functional and operational strategies (Matt et al., 2015). Therefore, operational competencies concentrate on the interaction of digital twins within a company’s actions and operations (Longo et al., 2017). Finally, in addition to strategic, tactical, operational competencies, firms need technical competencies to be able to propose effective solutions, which can guarantee the firms position as a technology leader (Huikkola and Kohtamäki, 2017). Firms equipped with digital twins are characterized by the seamless integration of sophisticated manufacturing capabilities with digital infrastructures and with the ability to capture, produce, and distribute smartness through enhanced monitoring, analytics, modeling, and simulation (Longo et al., 2017). Therefore, their technical competencies focus on the properties of the abilities of the digital twin (Longo et al., 2017; Qi et al., 2020). 15.2.3.2  Strategic direction Due to the rapid growth of digital technologies in various areas of business operations, many researchers argue that digitality should be integrated in the business strategy, and moreover, that digitality should be considered the main part of the business (El Sawy et al., 2016; Sia et al., 2016; Ukko et al., 2019). According to Matt et al. (2015) considering digital transformation as a strategic issue, digital technologies often affect large parts of companies and even go beyond their borders by impacting products, business processes, sales channels, and supply chains. They go on to say that with digital transformation strategies, the potential benefits of digitization are manifold and include increases in sales or productivity, innovations in value creation, and novel forms of interaction with customers. Consequently, the connection of digital twins to the strategy can be seen as equally important, at least in regard to the presented benefits.

202  Juhani Ukko et al. For example, Jones et al. (2020) present many potential and perceived benefits that have been highlighted in literature and industry relating to the digital twin concept such as: • reducing costs, risk, and complexity, minimizing design and reconfiguration time; • improving after-​ sales service, efficiency, maintenance decision making, security, safety and reliability, manufacturing management, processes and tools; • enhancing flexibility and competitiveness of manufacturing system; and • fostering innovation. However, to gain a more structured description of the benefits of various operations, the use of digital twins must be examined through the different functions of the company. So far, the strategic directions for and research into digital-​twin technology has been largely concerned with data management and data usage techniques of physical modeling, simulation, and optimization (Jones et al., 2020). Qi et al. (2020) state that modeling is arguably the cornerstone of digital twinning, and that it provides an information representation methodology for product design, analysis, computer numerical control (CNC) machining, quality inspection, and production management. As a result, strategic directions with regard to digital-​ twin implementation have so far focused on physical modeling and simulating the company’s internal functions such as product development, production, and product lifecycle management. A more novel strategic direction, in addition to using a digital twin for physical assets, is to apply digital-​twin technology to non-​physical modeling such as predicting customer needs (Vijayakumar, 2020).This can be made more effective by developing the digital twin to track customer behavior dynamically, logging the products they purchase and recording their level of satisfaction, etc. Similarly, the digital twin of a supply chain could track transportation, inventory, demand, and the capacity of the physical supply chain to make real-​time decisions on the product (Vijayakumar, 2020). This adoption of a supply chain digital twin can be considered a balancing act between logistical variations and customer requirements, enforcing the production of the right products at the right time, and promoting and achieving higher future revenue and sales (Vijayakumar, 2020). Therefore, the strategic directions regarding the implementation of non-​physically modeled digital twins can relate to the company’s external functions such as supply chain management, customer relationship management, and sales promotion. 15.2.3.3  Sustainability performance Rapid advancements in technological issues have enhanced organizational performance in the last couple of decades, but at the same time, the advancements

Sustainable competitive advantage  203 have led to growing expectations for sustainable business operations. With the emergence of industry 4.0, organizations expand their knowledge to transform their focus from performing economically to further excelling socially and environmentally. Therefore, organizations should operate in a competitive environment to produce sustainable competitive advantage and pursue the triple bottom line that results in a proper balance between economic, social, and environmental dimensions (Gupta et al., 2020).The sustainable performance of a practice is the integration of economic, social, and environmental performances in business process operations (Chardine-​Baumann & Botta-​Genoulaz, 2014). Economic sustainability performance focuses on the financial business value including increases in sales, market share, and profitability (Nasiri et al., 2018). Digital transformation and drastic changes in the business ecosystem bring about the demand for procedural changes through the emergence of industry 4.0, which leads to business profitability. Economic sustainability performance supports the development of an ecosystem for financial growth in business process operations and promotes opportunities to oblige stakeholder preferences in terms of social and environmental aspects (Gupta et al., 2020). Social sustainability performance addresses issues regarding social and personal needs in the places people live and work. These issues include wellbeing, social responsibility, renewal, productivity, and customer satisfaction (Nasiri et al., 2018). Advanced companies who utilize real-​time data are struggling to reach a high level of social performance by engaging moral standards, trust, and free discussion (Gupta et al., 2020). Environmental sustainability performance refers to the creation of business profit while conforming to environmental policies and standards as well as resource efficiency and capacity. As one of the outstanding tools in industry 4.0, a digital twin can enhance environmental sustainability performance through real-​ time monitoring to optimize resource consumption and reduce toxic emission (Nasiri et al., 2018). 15.2.3.4  External environment A company’s external environment can be defined and understood as the different types of stable rules, social level standards, and cognitive structures (Scott, 1995) as well as the environmental aspects such as weather that are either guiding, favoring, or restricting its businesses and operations. As such, the external environment of companies comprises different aspects and continuous changes to which companies must adapt. To be able to operate in these continuously changing environments, companies are trying to gather and analyze important data from their external environment. Digital twinning can play an important role while gathering and analyzing data from external operations and business environments. According to Tao et al. (2018), digital twin-​driven virtual verification can be utilized to better follow and understand a company’s environment, its materials, and the physical characteristics of its customers. As such, the utilization of a digital twin

204  Juhani Ukko et al. can, for example, be targeted to follow the changes in the prices of raw materials or how the changes in weather conditions affect operations. External environment effects can also be estimated and analyzed in product development via digital twinning. For example, Tao et al. (2018) suggested that designers could install assembly sensors in the conceptual design phase to provide various data from the environment to be used in the design process.

15.3  Requisite competencies and research propositions 15.3.1  Linking competencies to the strategic direction of a digital twin Early phases of product development are sometimes “fuzzy”, because there is a lack of information about the initial solution and final application of the product. As a result, companies utilize digital solutions (e.g. a digital twin) to virtually visualize multiple product solutions and their application. Managers and the people who engage in decision making should commit to this virtual working environment and consider strategic sustainable initiatives in the initial product development phase (Hallstedt and Isaksson, 2017). According to Tao and his colleague (2018), the successful development of a digital twin for product development necessitates having advanced technical knowledge and digital technologies for data acquisition and exploitation in every stage of product lifecycle. Additionally, the exploitation of real-​time information is challenging without also having the ability to manage, process, and analyze the large amount of data (Tao et al., 2018). Based on the preceding discussion, the first proposition can be shaped as follows. P1: Firms that are strong in the necessary competencies are more successful implementing a digital twin to improve product development. One key aspect of leveraging digital twins is the facilitation of production and manufacturing processes. Digital twins could enable designers to simulate the entire factory design process with respect to factory layout, equipment configuration, material handling, buffer capacity, etc. (Zhang et al., 2017; Guo et al., 2019; Qi et al., 2020). This can be achieved by focusing on the development of a simulation-​based approach for plant design and production planning where modeling and simulation approaches can be applied to develop digital-​twin models of the plant (Zhang et al., 2017; Qi et al., 2020). However, to successfully deploy digital twinning on such a scale, companies must have the necessary competencies, for example, the management and operational competencies. Ukko et al. (2019) argue that in addition to understanding their business, managers must be familiar with existing digital tools, applications, and solutions. They need to have a clear vision for utilizing digitality in the company now and in the future and need to build a management culture that supports the utilization of digitality in the company.

Sustainable competitive advantage  205 Similarly, operational competencies include proficiency in adopting and implementing digital tools and solutions and the ability to make use of them as a natural part of business processes (Peng et al., 2008; Benitez et al., 2018; Ukko et al., 2019). Continuing this line of thought, the second proposition is as follows. P2: Firms that are strong in the necessary competencies are more successful implementing a digital twin to improve production. Through the contemporary possibilities of integrating with mobile internet, cloud computing, big data, and other recent technologies (Qi et al., 2020), companies now have the opportunity to adopt and utilize digital twins in their marketing and sales activities. Currently, digital twins can be found in smart cities, manufacturing, cargo shipping, and automotive industries (Qi et al., 2020). Within these industries, the utilization of digital twins can support, for example, product launches and marketing based on relevant customer data (Tao et al., 2018). Tao et al. (2018) further argued that as part of company sales processes, digital twins can make customer preferences and the location distribution of orders available and also support after-​sales services. As the implementation of digital twins in sales and marketing operations provides companies with new types of possibilities and options, it becomes necessary to find the most suitable ways to support those operations. Digital twinning results in an abundance of data from many different sources, but not all are valuable from the sales/​marketing perspective. As such, a third proposition can be presented. P3: Firms that are strong in the necessary competencies are more successful implementing a digital twin to improve sales and marketing. Digital twins provide great value beyond the boundaries of individual firms, but at the same time, their implementation in the supply chain context necessitates high-​level competencies. The required competencies are not purely technological. Firms also must deal with the challenges associated with interpreting the data and taking action based on the insights gained (Srai et al., 2019). Specific knowledge regarding the behavior of the physical object (of the digital twin), such as its quality evolution, is required to optimize the supply chain from transport to storage and beyond (Defraeye et al., 2019). Liotine (2020) uses the term digital innovation to describe the linked supply chain competencies needed to sense the variability in demand and adjust production capacity to optimize the supply chain in terms of timely deliveries and reasonable inventories. A fourth proposition follows based on these considerations. P4: Firms that are strong in the necessary competencies are more successful implementing a digital twin to improve supply chain management.

206  Juhani Ukko et al. 15.3.2  Linking strategic direction of a digital twin to sustainability performance Digital twinning provides many opportunities in every stage of product development through advanced computing and by providing novel tools for simulation and data analysis and digitalized data (Qi et al., 2020). Digital-​twin technology in product development makes it possible to estimate performance early, which results in savings of time and cost (Wagner et al., 2019) and gains in energy efficiency (Horváthová et al., 2019). Designers believe that the development of efficient physical artifacts is more challenging without the comprehensive availability of manufacturing knowledge in the early stages of product development. The digital twin can help via knowledge discovery in the database and the optimization of each process based on prior product data. These benefits result in sustainable performance (Wagner et al., 2019). As a result, a fifth proposition can be stated as follows: P5: The more a digital twin is used to improve product development, the higher the level of sustainability performance. It has recently been demonstrated that in production management, digital twins enable the optimal (re)configuration of on-​site resources, equipment, work-​in-​progress, and workers through the simulation, verification, and confirmation of process planning and production scheduling (Zhang et al., 2018; Qi et al., 2020). With regard to control and execution, digital twins can be used to track everything that occurs in the physical world to subsequently perform operational forecasting, optimize control strategy, and align actual processes with planning (Sun et al., 2017;Vachálek et al., 2017; Senington et al., 2018; Qi et al., 2020). Qi et al. (2020) present that by using twin data such as sensor data, energy costs, or performance factors, the optimization service tools are triggered to run hundreds or thousands of what-​if simulations to evaluate readiness or to make necessary adjustments to current system set-​points. From a production management perspective, this makes possible a controlled and optimized system designed to achieve various sustainability functionalities such as providing higher security and safety (social aspect), decreasing energy and material consumption (environmental aspect), and decreasing costs (economic aspect) (Chen et al., 2015; Jones et al., 2020; Qi et al., 2020). Therefore, based on the notions above, a sixth proposition is presented. P6: Making greater use of a digital twin to improve production results in a higher level of sustainability performance. The utilization of digital twins in marketing and sales processes provides companies possibilities to improve their sustainability. For example, the use of digital twins, for example, in exhibition marketing and individual sales processes

Sustainable competitive advantage  207 can help companies to reduce the amount of physical material needed to demonstrate their products and services. In addition, the real-​time tracking of logistics (Tao et al., 2018) and warehouse inventories make it possible to minimize unnecessary product movement.Tao et al. (2018) further present that in the area of after-​sales services, digital twinning can provide maintenance data which help companies to predict and improve product lifetime and help to minimize product failures. Qi et al. (2019) further argued that digital twins can be utilized widely among different industries to optimize product lifecycles and services making them more sustainable. The utilization of digital twins as a part of sales and marketing operations, therefore, can help to minimize physical demonstration and decrease unnecessary people and product logistics, both of which help companies to operate in a more sustainable manner. Based on this discussion, a seventh proposition is offered. P7:  Making greater use of a digital twin to improve sales and marketing results in a higher level of sustainability performance. Digital twins have several positive implications for the supply chain as well. According to Marmolejo-​Saucedo et al. (2019), digital twins can help supply chain members to decrease costs. Because the supply chain is more connected and more real-​time information is available, decisions can be made in real time, and potential flaws can be more rapidly fixed. Defraeye et al. (2019) have applied a digital twin to predict quality loss in complex-​shaped fruits. They concluded that the digital twin helped to enhance the logistics process by reducing food losses, thereby making the supply chain more sustainable. Further, digital twins can provide greater sustainability performance for the supply chain by reducing inventory, enhancing product/​service quality and environmental compliance, and improving livelihood and resource efficiency (Srai et al., 2019). Based on the evidence presented above, the following eighth proposition is established. P8: Making greater use of a digital twin to improve supply chain management results in a higher level of sustainability performance. 15.3.3  External environment as moderator The concept of a digital twin includes real-​time interactions with the external environment. The external environment constitutes various uncertainties and constraints that are monitored by sensors and manipulated by actuators that form the backbone of a digital twin (Tao et al., 2019). Digital twins can assist companies in responding to environmental changes to maintain optimized production in the pressure of everchanging external constraints (Min et al., 2019). Qi et al. (2019) has characterized these types of behavioral models in terms of responding mechanisms that assist in coping with changes in the external

208  Juhani Ukko et al. environment. These responses to the external environment help these models to enhance the simulation service performance of a digital twin (Qi et al., 2019). Based on the research into the external environment facilitating digital-​twin implementation, the following ninth proposition is provided. P9: The external environment will moderate the relationships among organizational competencies, the strategic direction of digital twin use, and sustainability performance.

15.4  Conclusions 15.4.1 Theoretical implications This chapter has contributed to the understanding of how and under what conditions firms implementing digital twins can build a sustainable competitive advantage. The chapter constructed a multidimensional model capable of managing a digital twin-​driven operation based on insights from multiple disciplines and a dynamic-​capabilities view of the firm. It has highlighted the benefits the model brings to understanding the interplay between firm competencies, strategic direction, and sustainability performance in the implementation of a digital twin. Further, nine propositions are offered related to the requisite competencies and strategic direction of digital-​twin implementation. The model provides a robust theoretical basis for future research into the consequences of digital-​twin implementation. Therefore, this multidimensional view of digital-​ twin implementation is important for the following reasons. First, a focus of digital twin research has been in product and production development (Vijayakumar, 2020). However, digital transformation in products or in production alone is inadequate to achieve long-​term competitiveness (Rantala et al., 2019). Therefore, this type of focus on explicit areas of digital-​ twin implementation may limit their effectiveness; whereas a wider perspective on the strategic direction of digital-​twin implementation facilitates expanding its interaction into all areas of the firm to achieve sustainable competitive advantage. Second, the model will help companies implementing digital-​twin technology to identify and develop organizational conditions and competencies needed to fulfill their strategic objectives. Third, the model establishes clear connections between firm competencies, strategic direction, and sustainability performance in the implementation of a digital twin. The study suggests that firm competencies at different levels affect the strategic direction of a digital-​twin implementation. Strategic direction refers to using a digital twin to improve product development, production, sales and marketing, or supply chain management. Further, greater use of a digital twin makes possible a higher level of sustainability performance. In this regard, the model identifies technical, operational, tactical, and strategic competencies as four separate but related competencies that are likely to influence the implementation of a digital twin and, consequently, sustainability performance.

Sustainable competitive advantage  209 15.4.2  Managerial implications From the perspective of management, this chapter provides an important contribution that can increase and support the practical level utilization and adoption of digital twins for companies. Firstly, the chapter increases the practical level understanding of the multidimensional nature of digital twins by providing a comprehensive view of the different possibilities of how and under which conditions digital twins can be adopted. Further, and more importantly, this chapter gives practitioners an understanding of how and under which conditions digital-​twin technology can be realized to build a sustainable competitive advantage. In addition to discussing multiple different uses for digital twins and their connections, the chapter provides a multidimensional model that can be utilized to implement a digital twin in practice. The presented model can be utilized as a practical level tool that provides different implementation pathways. These different pathways can increase the practical level understanding of the role of competencies and the strategic directions in implementation of a digital twin. 15.4.3  Limitations and further research directions Although this chapter provides a comprehensive understanding of the different possibilities for how and under which conditions digital twins can be implemented and utilized in companies, some limitations remain concerning the results of the chapter. The arguments made concerning the interplay between firm competencies, the strategic direction of digital-​twin use, and their connection to sustainability performance, as well as the presented model for implementation of a digital twin are mainly theoretical and based on the prior literature. As such, more empirical evidence to further explore the presented model is recommended. While the theoretical nature of the chapter can be considered a limitation, it also provides important and interesting avenues for further research. This chapter offers nine propositions that link competencies to the strategic direction for digital twinning and link strategic direction to sustainability performance. These propositions provide important and interesting avenues for further research in the field of implementation and the use of digital twins, either individually or in different combinations.

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16  Managing digital-​twin lifecycle –​recognition and handling of business risks Tero Rantala, Minna Saunila, Juhani Ukko, Aki Mikkola, Juha Kortelainen and Akhtar Zeb

16.1  Introduction The utilization of different types of digital twins among industrial organizations is becoming increasingly common as the possibilities and solutions developed by service providers continue to grow (Qi et al., 2020; Tao et al., 2019; Tao et al., 2018). Currently, digital twins are increasingly being adopted and utilized by different types of manufacturing companies to support, for example, their daily engineering, production, management, and decision making. The digital twins provide these companies with a large amount of data from various processes. For that reason, company leaders from many different industries are becoming increasingly interested in supporting their operations management and business activities with effective utilization of lifecycle big data (Zhang et al., 2017). From the business point of view, optimization of the process of digital-​twin lifecycle management is an increasingly important objective for companies among different industries to improve their sustainable competitive advantage. From the business point of view, the management and handling of risks arising during different digital-​twin lifecycle phases generates new types of business-​related managerial challenges. Because the use of digital twins as a part of company operations and management is relatively new and because of the small number of example cases, the long-​term functionality of the digital-​twin lifecycle is not well understood. More precisely, less attention has been paid to the recognition and understanding of managerial and business-​related risks during different phases of the digital twin lifecycle. Because of a growing interest in the implementation and use of digital twins, the potential for business-​related risk is growing, and there isn’t much real information available to help mitigate this risk over the long term. Digital twins are being implemented as integral parts of overall systems, for example in power plants. Because their expected lifecycle might be decades instead of few years, business-​and management-​related risks related to this type of digital twin use cannot be ignored. This chapter provides insights to help improve the understanding of risks related to the lifecycle management of digital twins from the business

214  Tero Rantala et al. perspective. The aim of the chapter is to recognize risks related to different phases of the digital-​twin lifecycle and provide information that will help businesses to prepare and deal with these risks. In addition to the recognition of the risks from the current literature related to digital twins, the chapter provides empirical insights from two large multinational Finnish industrial companies. These companies represent digital twin users that are planning to develop new service businesses based on their digital twin implementations. The company insights were gathered via semi-​structured interviews with people representing different organizational backgrounds and levels. The rest of the chapter is structured as follows. First, the theoretical background of the chapter is presented, including the definition of the digital twin as it is understood here. Next, the chapter gives a theoretical definition for the digital-​twin lifecycle and describes associated risks and challenges related to the different phases of that lifecycle. The methodology for the empirical part of the study is then detailed followed by the presentation of results. The chapter ends with concluding remarks and discussion.

16.2 Theoretical background 16.2.1  Digital twins The first mentions of the digital-​twin concept are from 2011 (Tuegel et al., 2011). A hypothetical concept of a digital model of an aircraft was introduced that would be capable of representing its real-​ world counterpart in an ultrarealistic manner.This digital twin would include structural and aerodynamic details as well as operational loads and factors. Although a large amount of computational resources would be needed, the proposed digital model would be able to compute, in real time, the physical phenomena involved. Since then, the concept of a digital twin has been under active research and development and has been defined in many ways. According to Grieves and Vickers (2017), the concept of having a representative digital model of a real physical system has a long history, and it has been referred to as a “conceptual ideal for PLM”, a “mirrored spaces model”, and an “information mirroring model”. Grieves and Vickers (2017) define the concept as follows: “The digital twin is a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level”. In their definition, the digital-​twin concept is further defined as “a digital-​ twin prototype” and “a digital twin instance”, the former being a digital representation of a prototypical physical artefact and the latter representing an individual instance of a product throughout its lifecycle. Similarly, Hartmann and Van Der Auweraer (2020) introduced the concept of “executable digital twin”, which is based on existing simulation models developed during the product design and development phases. Such digital twins would be built according to specific applications and would have the required accuracy and

Recognition and handling of business risks  215 performance. Together with their execution engines or simulation tools, these digital twins could be deployed on the edge (edge computing) or in the cloud. They could be used by autonomous machines or users with little experience. In both definitions of a digital twin, the authors are referring to physical products. The definition of a digital twin can be extended from those referring to physical products or systems, such as aircrafts, to any kind of system such as industrial processes (Vachálek et al., 2017) and even non-​physical systems such as operational and organizational processes. This wider interpretation of the digital-​twin concept makes it difficult to categorize what a digital twin is or what it is not (Rasheed, San, and Kvamsdal, 2020). Nevertheless, the concept contains three main elements: the target system or product, its digital representation (the digital twin), and communication and data exchange between the target system or product and the digital representation. The level of detail and the form of representation the digital twin has about the target system depends on the purpose of the digital twin. For example, if the purpose of the digital twin is to monitor and collect information about the state and condition of the machine system, it may represent a detailed structure of the target machine assembly that includes details of the individual components and their condition and history. On the other hand, if the purpose of a digital twin is to optimize the operation of the target system, e.g., by minimizing structural loading and maximizing system performance, details of the dynamics of the target system are needed by the digital twin together with some optimization capabilities. 16.2.2  Digital twin lifecycle The lifecycle of a digital twin refers to the existence of the digital twin itself, i.e., its conception (beginning of life), its use, any possible modifications and upgrades (middle of life), and retirement (end of life). The evolvement of the concept of a digital twin builds on top of other concepts relating to digital models, such as “virtual prototypes” (Wang, 2003) (Madni et al., 2019) and “cyber-​ physical systems” (Chen, 2017) (Nazarenko and Camarinha-​ Matos, 2020). In these concepts, the digital models of the physical systems contain features that are present in the design and engineering phase of the targets, i.e., at the beginning of life of the physical system and the digital twin. How the lifecycle of a digital twin refers to the lifecycle of the target system may differ. At the design and development phases of the product (beginning of life), the digital twin contains most of the information and possibly different models constructed to help design the product. Digital twin models are designed in virtual space according to the product operational needs and requirements. At this stage, the digital twin models may represent a generation of the product having similar characteristics. These simulation models are used as digital-​twin prototypes in the virtual space for experimentation and the analysis of product behaviors. Once

216  Tero Rantala et al. the product is tested and the simulation results are considered satisfactory, the product is ready for production and instantiated from the digital twin models considering the unique requirements of the end-​user. The product is then manufactured considering its potential use and operating conditions. At this stage, therefore, the digital twin models are modified and instantiated so that each digital twin corresponds to an instance of the physical product. Information from the digital twin can be directed back to the virtual space and used in the design of new generations of the product. This means that a digital twin can also be used for product lifecycles other than the one it explicitly represents (Tao et al., 2019). According to Haag and Anderl (2018), the current computer-​aided design, manufacturing, and engineering models (CAD, CAM and CAE models, respectively, or CAx models) are not designed to be utilized beyond the product development phase, which hinders the automatic generation of digital twin models that can be used during the subsequent product operational phase. There is a need for development and maintenance of a “digital-​twin template” in conjunction with the product at every phase of the product development process. Therefore, as the physical product is instantiated from the product models, the corresponding digital twin will be readily available from the digital twin template. At the product operation and maintenance phase (middle of life), the digital twin evolves along the product lifecycle and captures all the information about its physical counterpart. The digital twin and the physical twin are linked through data that flows both ways, i.e., from the physical product to the digital twin (e.g., data about product performance, behavior, state, and configuration) and from the digital twin to the physical product (e.g., predictions about product’s performance, failure, and life span). A digital twin is a digital replica of a real product system. In the industrial or business context, this means that if the target system is a series product, there will be a series of digital twins, one for each product produced. In a successful business based on series products, the number of digital twins will increase over time, and the maintenance required for each will cover the lifecycles of all the products sold. In addition, when a digital twin is used in an industrial or business context, the resource aspects must be considered. As a result, the digital twin should be constructed automatically from the design and engineering data as a side product of the design work. During the operational phase of the product, the maintenance and possible modifications and upgrades of the digital twins should also be automated to cover the overall fleet of digital twins. A digital twin should bring benefits to both the vendor and owner of the real asset. A digital twin will help the owner to monitor and optimize the performance of the physical product, and the vendor will be able to better design the next product generation using the information and knowledge gained from the digital twin. For a digital twin to accurately represent the actual state of its counterpart physical twin, timely modifications and upgrades must be

Recognition and handling of business risks  217 carried out. The digital twin may come from the original vendor, or it may be provided as a service from a third party. In either case, strong collaboration among all the participants should continue to appropriately allocate responsibilities, ensure secure access, protect IPR, and receive fair rewards (Cameron et al., 2018). At the product disposal phase (end of life), the digital twin may be used for safe product disposal. Since the digital twin is a digital representation of the physical product, it contains information about, e.g., product configuration and materials, which is useful for proper product disposal. Similarly, for a large and complex system, such as a process plant, a digital twin can help in demolition planning for the production process lines and structures by providing information about the available space around equipment and in the buildings and possible hazardous structures. The use of up-​to-​date digital models simplify planning and reduce the risk of accidents. 16.2.3  Lifecycle risks of digital twins The digital twin contains all the information about the physical system, the data linking the real to the virtual, and the information linking the virtual to the real system throughout the digital-​twin lifecycle (Schützer et al., 2019). From the perspective of doing business with the digital twin, this reciprocal data flow introduces several potential risks along the entire lifecycle. These risks have been previously studied from the perspective of data lifecycle management. The process of gathering, utilizing, and interpreting data introduces a number of risks. These include the loss of relevant data during data overloads (Tao et al., 2019; Ofner et al., 2013) as well as the possibility of duplicating data or receiving conflicting data from different sources (Tao et al., 2019; Zhang et al., 2017) and phases of the lifecycle (Tao et al., 2018). There is also a risk of losing information across the boundaries of business units, departments, business processes, and systems (Ofner et al., 2013). This information should be compressed as a small selection of useful information to support decision-​ making (Tao et al., 2019). Tao et al. (2019) have highlighted the necessity of combining data from different sources such as products, customers, and the operating environment. The unifying management and storage of the data (Zhang et al., 2017) can decrease the risk of duplicating data from diversified sources.There is also a risk that the data collected in distinct phases of the lifecycle may become information islands that waste resources and result in data-​sharing problems (Tao et al., 2018). Furthermore, a lack of regulation or standards at the individual, organization, national, and international levels hinders the implementation of digital twins (Tao et al., 2018). Dealing with the technology leads to other risk types. If the technology cannot sense or completely capture the lifecycle data on time (Zhang et al., 2017), it is a risk for the business. In situations where there is a need to respond rapidly

218  Tero Rantala et al. to real-​life events based on real-​time data or to predict a future event based on historical data (Tao et al., 2019), the lack of reliable technology is a risk. Also, ensuring efficient retention and destruction of collected data has been highlighted. According to Tao et al. (2019), improper data storage and transfer in a digital twin can result in severe mistakes. It is also crucial to assure validity and security in data transmission (Tao et al., 2019). Finally, human-​related risks deal with a lack of skills in mining and using the information coming from lifecycle data (Zhang et al., 2017). As presented above, there are variety of risks related to data lifecycle management. As the requirements for different phases of digital twin lifecycle differ (from early stages of systems development to detailed systems design, maintenance support, and process data feedback) (Garrido & Sáez, 2019), the same applies to risks. Therefore, being a novel and prominent form of technology, the risks related to different phases of the digital-​twin lifecycle require further understanding.

16.3  Research methodology The case study research methodology was used to collect the empirical data considered in this study. The case study method was chosen, because it is considered to be an appropriate research method to explore real-​life cases under circumstances where researchers have the opportunity to observe and gather data in a realistic context (Yin, 2009; Voss et al., 2002). The motivation for selecting the two case companies under investigation was to keep the number of cases low, which according to Voss et al. (2002) makes possible deeper observation. The selected case companies are large multinational Finnish industrial companies that are planning to exploit possibilities provided by modern digital twinning to develop their service and product offerings. Since both companies are currently and actively exploring the potential of digital twins, these cases provide interesting and valuable real-​life settings to observe possible risks related to different phases of the digital-​twin lifecycle. Table 16.1 summarizes the interview process that was used for data gathering.

16.4  Results The results of the interviews indicate that there are currently many open questions about the business risks associated with the lifecycle stages of digital twins and their identification. The results of the study also show that new business models based on digital twins, especially service-​ business-​ related business models, are a relatively new phenomenon whose potential is still being identified. Since there is a shortage of existing real-​life cases, more evaluation of and more experience with longer-​term business risks is needed. In the first phase of the digital-​twin lifecycle, one of the biggest business challenges relates to digital twin-​related business logic and its design. When a new service business is designed based on the digital twin, there are costs

Recognition and handling of business risks  219 Table 16.1 Process of empirical data gathering Case company

Company type

Interviews

Company A

Large multinational machine and solution supplier in wood industry -​ Its customers are companies operating in the wood products industry Manufactures and services power sources and other equipment in the marine and energy markets -​ At the marine market customers comprise both shipyards and ship owners

Group Vice President, Technology Business Line Manager Development Manager R&D Manager

Company B

Technology Manager Development Manager Product Development Manager

associated with launching and developing it that should be recouped over the digital twin lifecycle. Based on the results from interviews, the business model related to digital twins that is currently more clearly conceivable and feasible seems to be based on a fixed monthly fee paid by the end customer, where the customer is willing to pay for the identified value-​added features and elements. In this case, the business risk arising from the costs of setting up a digital twin remains with the product/​plant supplier. During the lifecycle of the digital twin, the revenue stream becomes positive after the calculated time span. Another, alternative model to deal with the digital twin’s business risk is to outsource the operation of machines, production lines, or entire factories with profit responsibilities to their supplier. In such a digital-​twin-​related business model, the supplier operates the products it supplies using a digital twin, and the business benefit obtained is based on, for example, cost savings or improved efficiency. Such a business model is seen as a potential option in the near future, but at present there are still too many risk factors associated with uncertainties regarding digital twin accuracy and functionality, so business risk from operating products or facilities could be handled solely by suppliers. In the “beginning of life” phase of the digital twin lifecycle; along with the delivery of new machines, production lines, or entire plants; end customers expect advanced and sophisticated visualizations of the products to be delivered. Providing these have gradually become common practice for sales and design operations. Digital visualizations are becoming increasingly sophisticated and complex with no additional cost being billed to customers. As a result, advanced visualizations to support sales and design work may also result in difficulties for the sale of digital twins at the “beginning of life” stage, because it may be difficult to identify additional features that a customer would be willing to pay for. Therefore, customers are accustomed to constantly receiving and utilizing more sophisticated digital visualizations without viewing the practice as a billable service.

220  Tero Rantala et al. In the “middle of life” phase of the digital twin lifecycle, from a business perspective, the main percentage of currently recognized risks seems to be related to the additional costs of the necessary changes, upgrades, and maintenance of digital twins over time. When designing the digital twin in the first phase of its lifecycle, it is assumed that the development and change of the operating environment will be anticipated and considered as accurately and comprehensively as possible. Nevertheless, changes due to the development of the operating environment pose business risks. For example, if the operational environment modeled by the digital twin changes faster or more than expected, the digital twins may need to face some major upgrades that may take too long to pay back. Another risk associated with the change and development of the business environment in the second phase of the digital twin lifecycle is related to the development of the business model and earnings logic. As the business and operating environments of industrial companies continue to evolve, so do the digital twins, which are constantly and more cost-​effectively offering new features and opportunities. This development will also allow business logic to be updated and diversified during the second phase of the lifecycle, but there is a risk that such features that will enable additional sales will not be identified or customers will be reluctant to pay more for them. Customers may assume that the features enabled by the development are part of the service to be provided in accordance with a previously agreed pricing principle. From a business perspective, the second phase of the digital twin lifecycle also involves the most significant human-​related risks and challenges. Based on the results, it is assumed that artificial intelligence and machine learning will soon become a growing part of the digital twin operating environment. In this case, digital twin implementation will partially or completely replace people in operational and business decision making. Thus, the digital twin will replace and displace existing skills and simultaneously demand that new skills be developed to replace them. Such machine learning and artificial intelligence expertise can be a significant business investment for companies with risks such as labor availability and price. One of the major business risks during the second phase of the digital twin lifecycle is associated with the company replacing existing equipment with new and different equipment. Digital twins may form factory-​level entities in which all equipment and functions relevant to the operation of the factory are visualized under the same digital model. It is assumed that during the lifecycle of a digital twin, not all devices will come from the same equipment supplier. The introduction of a new supplier may lead to significant changes and additional costs for the operation of the digital twin. These may include, for example, the reluctance of the new entrant to share in the costs of maintaining the digital twin, an unwillingness to share information, or the inadvertent transfer of proprietary information to competitors. From a business perspective, the “end-​of-​life” phase of the digital twin currently appears to be the least considered and recognized. Digital twins are planned to last a long time. Little attention is currently being paid to the end

Recognition and handling of business risks  221 of their lifecycle. End-​of-​life risks include a digital twin reaching the end of its lifecycle before it has paid for itself or a digital twin reaching the end of its lifecycle prematurely. The results of the interviews at different stages of the digital-​twin lifecycle are summarized and presented in Table 16.2. Table 16.2 Business risks related to digital twin lifecycle Phase of lifecycle

Human related risks Process related risks

Beginning of life

Finding the Identification of Design of long term necessary the elements that updating and human-​related the customer is maintenance of skills in the willing to pay for is digital twin beginning of life inadequate. Digital twin is designed Customer expects as a closed system. certain elements and visualizations with no extra charge. Business model and earning logic –​who carries the risk of the payback and ROI? AI and Machine Emergence of The computing learning are competitors infrastructure replacing on digital twin (computer hardware, humans. platforms operating system, Finding the Sales of additional simulation software) necessary services/​ additional may not be accessible human-​related features as or available for the skills during the functionalities long term, especially middle of life evolve (customer for digital twins with may not be willing lifecycles in decades. to pay for new services because they are thought to be part of the product features). Finding the balance between the incomes and costs of operating and updating of digital twins The digital twin does The end of lifecycle not return the happens in an investment during unpredicted and its lifecycle. uncontrolled manner because of technology loss.

Middle of life

End of life

Technology related risks

222  Tero Rantala et al.

16.5  Concluding remarks From a business perspective, in the first phase of the digital twin lifecycle, the most significant risks currently identified seem to be related to the business logic and balancing revenue streams and costs over the digital-​twin lifecycle. Even though the identification of human-​and process-​related risks appears to be increasing in the second phase of the digital twin lifecycle, the technological perspective will nevertheless be emphasized in the consideration and identification of risks. To summarize the business risks associated with the digital twin lifecycle, the contemporary identification of risks emphasizes a technological perspective. First, the risk may realize, if the digital twin is designed as a closed system that can only be accessed by the service provider. Second, the risk may materialize, if the entire computing infrastructure (computer hardware, operating system, simulation software) is not be accessible or available for the long term. Third, the risk can realize, if the end of lifecycle happens in an unpredicted and uncontrolled manner. This can be, for example, a loss of technology if a service provider goes bankrupt. While technology-​related business risks are an integral part of the various stages of the digital-​twin lifecycle, human-​related risks also pose significant business risks for companies. For this reason, it would be important in future research to provide more information and an understanding of the human risks associated with the different stages of the lifecycle of digital twins and how companies could better prepare for them. As the results of this study also show that the identification of business risks related to the “end of life” stage of the digital-​twin lifecycle is less compared to the beginning of the lifecycle, future research would also be essential to provide more information on and understanding to better prepare for the “end-​of-​life” phase risks.

Acknowledgments The authors would like to thank all the stakeholders participating in the DIGIBUZZ (Toward Commercial Exploitation of the Digital Twins) project, as well as Business Finland –​the Finnish innovation funding, trade, investment, and travel promotion organization –​for the provided support to write this chapter.

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Index

Note: Page references in italics indicate figures and in bold indicate tables on the corresponding pages. agricultural machine: farm tractor model case example 83–​86, 84; modeling 79–​81, 80 analytical methods, virtual sensors 94 Artificial Intelligence (AI) 125, 192 augmented reality (AR) 147–​148, 150–​151, 153 Baines, T. S. 15, 122 beginning of life (BOL) 15–​16 business model canvas 182–​184, 183 business models, effect of real-​time simulation on 189–​191 “bystander” strategy 47 channels 182 Chen, D. 42, 52 cluster analysis results 45–​46, 46 company capabilities: conclusions on 37–​39; digital business strategy and 29–​30; digital business strategy and real-​time activities and 31; digital capabilities and 30; digital capabilities and real-​time activities and 31–​32; empirical examination of real-​time simulation and 33–​37, 34–​35, 36, 37; introduction to 28–​29; research model 33, 33; theoretical framework and research model 29–​33, 33 competitive advantage, sustainable: conclusions on 208–​209; external environment and 203–​204; firm competencies and 199–​201; introduction to 196–​197; multidimensional model for implementation of digital twin for

197–​204, 200; requisite competencies and research propositions 204–​208; strategic direction and 201–​202; sustainable performance and 202–​203 cost structure in business model canvas 184 crowdsourcing 185 customer choice and digitalization 179 customer relationships in business model canvas 182 customer segments 182 cyber-​physical systems (CPS) 91, 151 data-​based solutions in B2B markets 53–​54 data collection 44–​45 data-​driven modeling: applications of 72, 72–​74; concept of 67–​72; conclusions on 75; enhanced operation 73; introduction to 65–​66; maintenance and service 74; in multibody framework 71–​72; neural-​networks-​ based data models and other methods 70–​71; new business opportunities 74; research and product development 73; supporting sales and purchase 74; system modeling 68–​70 “data wisdom” strategy 47 deep digital-​twin solutions 56, 56–​57, 56–​57, 59 deep neural network (DNN) models 68, 70 Delphi method see expected benefits from simulation modeling digital business strategy 29–​30; real-​time activities and 31

Index  225 digital capabilities 30; real-​time activities and 31–​32 digitalization 2, 3–​5; firm competencies and 199–​201; introduction to 13–​14, 177–​178; market effects of 178–​180; real-​time simulation models and value creation 180–​182; strategic direction and 201–​202 digitalization competitiveness 179 digital twins: in business-​to-​business markets (see digital twins in business-​to-​ business markets); conclusions on 24–​25; in cyber-​physical systems (CPS) 91; defining 198–​199; description of model 199–​204, 200; identified industrial needs for 19–​23, 20–​22; introduction to 13–​14; in lean production (see lean production); lifecycle of 215–​217; lifecycle risks of 217–​218; linking sustainability performance to strategic direction of 206–​207; multibody definition of 148–​149; multidimensional model for implementation of 197–​204, 200; performance measurement and 167–​168; real-​time simulation and 14–​17, 16; recognition and handling of business risks and (see risks, recognition and handling of business); research methodology and data collection on 17–​19, 18; sustainable competitive advantage and (see competitive advantage, sustainable); theoretical background 214–​215; user experience and (see user experience) Digital Twins: Changing the Way We Engineer Validate, Market, and Operate our Products 179 digital twins in business-​to-​business markets: challenges in selling 57–​59; conclusions on 59–​61; introduction to 51–​52; level of customer and digital-​twin understanding needed from sales perspective and 55–​57, 56–​57; in manufacturing industry 52–​53; research methodology 54, 55; research results 55–​59, 56–​57; selling data-​based solutions 53–​54; theoretical background 52–​54 Donoghue, I. D. M. 16, 123–​124, 128, 150 end of life (EOL) 16, 150 environment modeling 81

expected benefits from simulation modeling: benefits of using simulation in the long term for the customer and other stakeholders 139–​141; business-​ activity related effects of simulation 141–​142, 141–​143; conclusions on 143–​144; expert panel insights 136–​143, 137–​139, 141–​142; introduction to 132; motives for 133–​134; research method and data gathering process 134–​135, 135–​136 external environment 203–​204; as moderator 207–​208 faults, applying real-​time simulators to predict 187 finite element method (FEM) 95 firm competencies 199–​201 Gallois, P. M. 133, 134 gamification: conclusions on 86–​87; defined 81; design process for parameterization in 79–​80, 80; elements of 82, 83; environment modeling 81; farm tractor model 83–​86, 84; introduction to 77–​79; marketing opportunity 85–​86; methods of data extraction for 82–​83; modeling an agricultural machine and 79–​81, 80; in product development and R&D 185; product development opportunity 85; steps in 81–​82 Google Maps 44 Grieves, M. 15, 17, 123–​124, 127–​128, 149–​150, 214 haptics 153 Hess, T. 31, 42 industrial mobile machines, joint simulation of 107–​115; technical-​ business challenges of 108–​109 Industry 4.0 177–​178 Information Mirroring Model (IM Model) 17 Internet of Things (IoT) 1, 122, 124, 149 “in the game” strategy 47 I-​SCOOP 179 Jaiswal, S. 81, 85, 92 joint simulation modeling: assembling 104–​107, 105–​107; collaboration benefits and issues on the alliance

226 Index 112–​115, 113–​114; combining parameterized models with optimized model 106–​107, 107; conclusions on 115, 116; developing customer-​ oriented B2B products 104–​105, 105; of industrial mobile machines 107–​115, 110, 111, 113–​114; introduction to 102–​103; preparation of B2B parameterized real-​time 105, 106; real-​time solutions 109–​112, 110, 111; related research on 103–​104 Jones 197, 202 Jones, D. 14, 17 key activities in business model canvas 184 key resources in business model canvas 184 K-​means clustering method 46 Kurvinen, E. 87 lean production: advantages of digital twin and real-​time performance measurement for 169–​174, 170, 173; conclusions on 174–​175; context and background 166–​168; introduction to 165–​166; performance measurement and 166–​167; research methodology 168–​169 leap controllers 153 lifecycle of digital twins 215–​217; risks 217–​218 light digital-​twin solutions 55–​56, 56, 56–​57, 58 LuGre friction model 180 machine learning (ML) methods 67, 68, 70 Matt, C. 31, 201 Mevea 186, 192 middle of life (MOL) 15–​16, 150 Mikkola, A. 134 mixed reality glasses 153 moderate digital-​twin solutions 56, 56, 56–​57, 58–​59 Mont, O. 122–​123 multibody system dynamics 71–​72, 104; definition of digital twin and 148–​149; user experiences in 151–​159, 152, 156, 158 numerical methods, virtual sensors 94–​96

offline virtual measurements 96 online virtual measurement 97 operator experience and product development 186 optimized model combined with parameterized model 106–​107, 107 outsourcing 188 parameterization: combined with optimized model 106–​107, 107; design process for 79–​80, 80; joint simulation modeling 105, 106; user testing and 157 performance measurement: advantages of digital twin and real-​time 169–​174, 170, 173; digital twins and 167–​168; lean approach and 166–​167 product complexity and digitalization 179 product development and R&D 184–​186; outsourcing 188; sustainability performance and 206; sustainable competitive advantage and 204–​205 product lifecycle management (PLM) 15–​17, 16, 24–​25; identified industrial needs for real-​time simulation and digital twins in 19–​23, 20–​22; phases of 149–​150; user experience and (see user experience) product-​service system (PSS) 15–​17; discussion and conclusions on 128–​129, 129; introduction to 121–​122; related research 122–​125; research methodology 125; research results 125–​127, 126, 128 Qi, Q. 196, 202, 206–​208 Rantala, T. 53–​54, 55, 61 real-​time simulation: applied to different value chain activities 184–​191; applied to predict faults 187; applied to sales and marketing 188–​189; applied to services 188; applied to training 186–​187; characteristics of 46–​47, 47–​48; cluster analysis results 45–​46, 46; company capabilities and (See company capabilities); conclusions on strategies for 48–​49; data collection in 44–​45; digital twins and (see digital twins); effect on business models 189–​191; empirical examination of

Index  227 5–​6, 13–​14, 33–​37, 34–​35, 36, 37, 44–​47, 46–​48; expected benefits from (see expected benefits from simulation modeling); future research directions on 6, 6–​7, 8, 49; identified industrial needs for 19–​23, 20–​22; introduction to 42–​43; joint simulation 109–​112, 110, 111; in lean production (see lean production); limitations of 49; managerial implications 49; reflections on sustainable production based on 3–​5; for sustainability 43–​44; theoretical implications 48–​49; user experience and (see user experience); value creation with 180–​182 revenue streams 182 risks, recognition and handling of business: concluding remarks 222; digital twin lifecycle and 215–​217; introduction to 213–​214; lifecycle risks of digital twins and 217–​218; research methodology 218; research results 218–​221, 219, 221; theoretical background 214–​218 Sääksvuori, A. 125, 128 sales and marketing: applying real-​time simulators in 188–​189; sustainability performance and 207; sustainable competitive advantage and 204 Smart Connected Product System (SCPS): discussion and conclusions on 128–​129, 129; related research 123–​124; research methodology 125; research results 125–​127, 126, 128 Stark, J. 14, 16, 128 supply chain management 205, 207 sustainability performance 202–​203; linking strategic direction of a digital twin to 206–​207 sustainable innovation and digitalization 180 sustainable performance 44 sustainable production 3–​5; real-​time simulation for 43–​44 system modeling 68–​70 Tao, F. 17, 149, 168, 198, 203–​205, 207, 217–​218 technical methods enabling virtual sensing 93–​94 Terzi, S. 16, 150

training, applying real-​time simulators in 186–​187 Ukko, J. 31, 204 Unmanned Aerial Systems (UAS) 124 user experience (UX): co-​creating product value with 150–​151; co-​creation of new forklift mast system in virtual space 155–​157, 156; conclusions on 159–​160; contributing to product value and competitive advantage 150; developing a user-​ centered virtual space of a physical model 151–​152, 152; enabled in the product lifecycle with immersive multibody-​based digital-​twin approach 151–​155, 152; end of product life 159; enhancement of measured data 154–​155; immersive methods for generating user input 153; industrial case study 155–​159, 156, 158; introduction to 147–​148; manufacturing of the physical product 153–​154; product life management data 154; real-​time communication between the physical and virtual spaces of the digital twin 154; related research 148–​151; simulator or motion feedback platform 153; user-​related product services in the operation phase 158–​159; user selection of component design data 152; user testing of parameterized model in different environments 157, 158; utilizing user-​ based multibody model in production 157–​158;VR, AR, mixed reality glasses, leap controllers and haptics 153 value chain processes: applying real-​time simulation to different 184–​191; business model canvas as tool to analyze 182–​184, 183; discussion and conclusions on 191–​192 value creation with real-​time simulation models 180–​182 value proposition 182 virtual machine 185 virtual measurements 96–​97 virtual models see digital twins virtual prototypes 215–​216 virtual reality (VR) 147–​148, 151, 153 virtual sensors: analytical methods 94; business opportunities introduced

228 Index by 97–​98; conclusions on 98–​100; context and background of 91–​93; introduction to 90–​91; numerical methods 94–​96; opportunities/​benefits and challenges of virtual measurements

96–​97; as part of product offering 93–​98; technical methods enabling 93–​94 Yin, R. K. 17, 54