Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry 3658405937, 9783658405939

Table of contents :
Disclaimer
Abstract
Contents
Abbreviations
List of Figures
List of Tables
1 Introduction
1.1 Present Situation and Problem Statement
1.2 Research Objective and Structure of Thesis
2 Theoretical Background and Technical Overview
2.1 Modularity of Product Systems and Product System Fleets
2.1.1 Modular Product System Architectures
2.1.2 Multiple Product Systems and Population Fleets
2.2 Life Cycle Thinking
2.2.1 Environmental Life Cycle Assessment
2.2.2 Life Cycle Costing
2.3 Sustainable Development and Corresponding Legislation
2.3.1 Environmental Impacts and Impact Assessment
2.3.2 Sustainability and Sustainable Development
2.3.3 General Environmental Legislation
2.3.4 Environmental Regulations for the Automotive Industry
2.4 Life Cycle Engineering and Mathematical Optimization
2.4.1 Life Cycle Engineering
2.4.2 Optimization Approaches for Decision Support
2.5 Conclusions Regarding the Theoretical Background
3 State of Research and Identification of the Research Gap
3.1 Criteria and Requirements for Optimizing the Eco-effectiveness of Product Systems
3.1.1 Criteria to Handle Product System Modularity
3.1.2 Criteria to Handle Use Case Specific Requirements
3.1.3 Criteria for Optimization Approach
3.1.4 Additional Requirements to Obtain Useful Results
3.2 Current Approaches and State of Research
3.2.1 Product Modularity
3.2.2 Modular Life Cycle Assessments
3.2.3 Optimization of LCA and LCC
3.3 Identification of the Research Gap
4 Concept for the Optimization of Eco-effectiveness of Product Systems
4.1 Concept Requirements
4.2 Framework for the Optimization Concept
4.2.1 General Framework
4.2.2 Selection of Graph Theory as Optimization Approach
4.3 Modelling the Product System’s Structure in a Network
4.3.1 Transformation of Modular Product Systems into Networks
4.3.2 Product System Networks Including Interdependencies
4.3.3 Network Reduction Strategies for Interdependency Modelling
4.3.4 Strategy Adaption to Reduce the Data Demand of LCA Values and LCC Values
4.4 Data Management of the Input Data
4.5 Adaption of Shortest Path Algorithms to the Problem Statement
4.6 Visualization and Interpretation of Results
5 Prototypical Implementation and Application of the Methodology
5.1 Prototypical Implementation of the Optimization Approach
5.2 Exemplary Application Cycle of the Optimization Approach
6 Application of the Optimization Approach to a Case Study of the Automotive Industry
6.1 Life Cycle Perspectives of a Vehicle
6.1.1 Product Life Cycle of a Vehicle
6.1.2 Environmental Assessment of a Vehicle’s Life Cycle
6.1.3 Total Cost of Ownership along a Vehicle’s Life Cycle
6.2 Selection of Measures for the Reduction of Greenhouse Gas Emissions
6.2.1 Selection of Measures and Module Alternatives
6.2.2 Measure Analysis Regarding LCA, LCC and Availability
6.3 Data Input for the Vehicle and Fleet Optimization
6.4 Results of Optimization for Different Scenarios
6.4.1 Analysis of the Results for a Single Vehicle Optimization
6.4.2 Analysis of Results for Vehicle Fleet Optimization
6.4.3 Sensitivity Analysis of Results
6.5 Findings for Further Vehicle Development and Fleet Planning
7 Summary, Critical Appraisal and Outlook
7.1 Summary
7.2 Critical Appraisal
7.3 Outlook
Appendix
Literature

Citation preview

AutoUni – Schriftenreihe

Chris David Gabrisch

Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry

AutoUni – Schriftenreihe Volume 164 Reihe herausgegeben von Volkswagen Aktiengesellschaft, Volkswagen Group Academy, Volkswagen Aktiengesellschaft, Wolfsburg, Germany

Chris David Gabrisch

Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry

Chris David Gabrisch Salzgitter, Germany Admitted dissertation of the Technical University Carolo-Wilhelmina zu Braunschweig, 2022. The results, opinions and conclusions of the AutoUni publication series published doctoral theses are only those of the doctoral candidate.

ISSN 1867-3635 ISSN 2512-1154 (electronic) AutoUni – Schriftenreihe ISBN 978-3-658-40593-9 ISBN 978-3-658-40594-6 (eBook) https://doi.org/10.1007/978-3-658-40594-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer Vieweg imprint is published by the registered company Springer Fachmedien Wiesbaden GmbH, part of Springer Nature. The registered company address is: Abraham-Lincoln-Str. 46, 65189 Wiesbaden, Germany

Disclaimer

Ergebnisse, Meinungen und Schlüsse dieser Dissertation/Veröffentlichung sind nicht notwendigerweise die der Volkswagen Aktiengesellschaft. The results, opinions and conclusions expressed in this thesis are not necessarily those of Volkswagen Aktiengesellschaft.

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Abstract

The automotive industry is facing the challenge of reducing its environmental impact to comply with stricter fleet emission regulations across all relevant markets. Thus, new technologies, e.g. lightweight materials, or an electrified powertrain need to be integrated into the new products to reach the reduced emission limits for the products. Many innovations that reduce emissions during the use stage of a vehicle often come with higher burdens during the production stage or at the end of life stage. However, both these stages are currently not regulated. The current emission targets are set for fleet averages in the use stage only. Thus, the OEMs need to find the ideal product configuration based on new technologies and design the ideal fleet composition to reach this target with the least cost effort. Still, an OEM’s contribution to the targets of the Paris Agreement must consider the entire life cycle of a vehicle, surpassing the targets of the current legislations which focusses on the use stage only. This work presents a concept that identifies the ideal configuration of a modular product system like a vehicle to meet a limited environmental impact at the lowest life cycle costs along the entire life cycle. This optimization is based on the ideal combination of modular product components which are selected by an algorithm based on graph theory. A modified shortest path algorithm is used to define the flow through a network of available components with individual weights for costs and emission on every arc. Expanded to a minimum-cost flow problem, multiple agents, e.g. representing a fleet of vehicles, are sent through the network with the target to identify the ideal fleet configuration. Herein lies the challenge of handling limited availabilities of certain options (e.g. scarce green electricity for the use stage of electric vehicles). The presented methodology includes a mechanism that assigns such limited options to those individual vehicles within a fleet that profit the most from that specific option.

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Abstract

The application to a case study based on a Volkswagen Golf VII model and an exemplary fleet of 100,000 vehicles of this type of vehicle shows the advantages of the developed concept. Four different emission limitation scenarios, ranging from 35t CO2 eq. to 21t CO2 eq. over the entire life cycle, have been assessed for both the single vehicle and the vehicle fleet. The results show that for fleet targets within the defined assumptions and scenarios, a fleet-based optimization for 100,000 vehicles is more efficient compared to a single vehicle optimization which is then rolled out to 100,000 vehicles. For relatively simple emission standards, the fleet is ideally based on combustion engines with optimized driving resistances, e.g. by using lightweight materials. For stricter standards, electrification is indispensable. Given the defined assumptions and scenarios, a tipping point is reached at an emission limitation of 28t CO2 eq. per vehicles life time. Starting from this emission limit, the algorithm stops using gasoline powered vehicles and starts to incorporate electric vehicles into the fleet composition. Keywords Life cycle assessment · Life cycle costing · Graph theory · Ecoeffectiveness · Fleet emission reduction

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Present Situation and Problem Statement . . . . . . . . . . . . . . . . . . . . 1.2 Research Objective and Structure of Thesis . . . . . . . . . . . . . . . . . .

1 1 5

2 Theoretical Background and Technical Overview . . . . . . . . . . . . . . . . 2.1 Modularity of Product Systems and Product System Fleets . . . . . 2.1.1 Modular Product System Architectures . . . . . . . . . . . . . . . . 2.1.2 Multiple Product Systems and Population Fleets . . . . . . . . 2.2 Life Cycle Thinking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Environmental Life Cycle Assessment . . . . . . . . . . . . . . . . 2.2.2 Life Cycle Costing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Sustainable Development and Corresponding Legislation . . . . . . . 2.3.1 Environmental Impacts and Impact Assessment . . . . . . . . 2.3.2 Sustainability and Sustainable Development . . . . . . . . . . . 2.3.3 General Environmental Legislation . . . . . . . . . . . . . . . . . . . 2.3.4 Environmental Regulations for the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Life Cycle Engineering and Mathematical Optimization . . . . . . . 2.4.1 Life Cycle Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Optimization Approaches for Decision Support . . . . . . . . 2.5 Conclusions Regarding the Theoretical Background . . . . . . . . . . .

9 9 9 18 20 22 28 33 33 36 41

3 State of Research and Identification of the Research Gap . . . . . . . . . 3.1 Criteria and Requirements for Optimizing the Eco-effectiveness of Product Systems . . . . . . . . . . . . . . . . . . . . 3.1.1 Criteria to Handle Product System Modularity . . . . . . . . . 3.1.2 Criteria to Handle Use Case Specific Requirements . . . . .

43 51 51 57 60 63 63 64 65

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Contents

3.1.3 Criteria for Optimization Approach . . . . . . . . . . . . . . . . . . . 3.1.4 Additional Requirements to Obtain Useful Results . . . . . . 3.2 Current Approaches and State of Research . . . . . . . . . . . . . . . . . . . 3.2.1 Product Modularity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Modular Life Cycle Assessments . . . . . . . . . . . . . . . . . . . . . 3.2.3 Optimization of LCA and LCC . . . . . . . . . . . . . . . . . . . . . . 3.3 Identification of the Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Concept for the Optimization of Eco-effectiveness of Product Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Concept Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Framework for the Optimization Concept . . . . . . . . . . . . . . . . . . . . 4.2.1 General Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Selection of Graph Theory as Optimization Approach . . . 4.3 Modelling the Product System’s Structure in a Network . . . . . . . 4.3.1 Transformation of Modular Product Systems into Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Product System Networks Including Interdependencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Network Reduction Strategies for Interdependency Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Strategy Adaption to Reduce the Data Demand of LCA Values and LCC Values . . . . . . . . . . . . . . . . . . . . . . 4.4 Data Management of the Input Data . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Adaption of Shortest Path Algorithms to the Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Visualization and Interpretation of Results . . . . . . . . . . . . . . . . . . . 5 Prototypical Implementation and Application of the Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Prototypical Implementation of the Optimization Approach . . . . 5.2 Exemplary Application Cycle of the Optimization Approach . . . 6 Application of the Optimization Approach to a Case Study of the Automotive Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Life Cycle Perspectives of a Vehicle . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Product Life Cycle of a Vehicle . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Environmental Assessment of a Vehicle’s Life Cycle . . . . 6.1.3 Total Cost of Ownership along a Vehicle’s Life Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

66 67 68 70 72 75 83 91 91 93 93 96 103 103 106 110 115 122 124 132 137 137 141 145 146 146 148 151

Contents

6.2 Selection of Measures for the Reduction of Greenhouse Gas Emissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Selection of Measures and Module Alternatives . . . . . . . . 6.2.2 Measure Analysis Regarding LCA, LCC and Availability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Data Input for the Vehicle and Fleet Optimization . . . . . . . . . . . . 6.4 Results of Optimization for Different Scenarios . . . . . . . . . . . . . . . 6.4.1 Analysis of the Results for a Single Vehicle Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Analysis of Results for Vehicle Fleet Optimization . . . . . 6.4.3 Sensitivity Analysis of Results . . . . . . . . . . . . . . . . . . . . . . . 6.5 Findings for Further Vehicle Development and Fleet Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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155 156 161 168 171 174 177 184 187

7 Summary, Critical Appraisal and Outlook . . . . . . . . . . . . . . . . . . . . . . . 7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Critical Appraisal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193 193 195 196

Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

199

Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Abbreviations

AP AbwAG BEV BimSchG CAFC CARB CCT ChemG CNG CO CO2 CO2 eq. eGas EPA EU EU ETS FRV FTP-75 GDP GHG GWP HC IEA LCA LCC LCE

Acidification potential Abwasserabgabengesetz Battery electric vehicle Bundes-Immissionsschutzgesetz Corporate average fuel consumption California air resources board Computational complexity theory Chemikaliengesetz Compressed natural gas Carbon monoxide Carbon dioxide Carbon dioxide equivalents Regenerative compressed natural gas Environmental protection agency European Union European Union emissions trading system Fuel reduction value Federal test procedure 75 Gross domestic product Greenhouse gases Global warming potential Hydrocarbons International energy agency Life cycle assessment Life cycle costing Life cycle engineering

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LCI LCIA LCM MCFP NEDC NMHC NOX NP ODP OEM PCOF PHEV PM PN TCO UN UNEP USA VDI WBCSD WCED WLTP ZLEV

Abbreviations

Life cycle inventory Life cycle impact assessment Life cycle management Minimum-cost flow problem New European driving cycle Non-methane hydrocarbons Oxides of nitrogen Nondeterministic polynomial time Ozone depletion potential Original equipment manufacturer Photochemical ozone formation Plug-in hybrid electric vehicle Particular matter Number of particular matter Total cost of ownership United Nations United Nations environment programme United States of America Verein Deutscher Ingenieure World business council for sustainable development World commission on environment and development World harmonized light duty test procedure Zero and low emission vehicles

List of Figures

Figure Figure Figure Figure Figure Figure Figure

1.1 1.2 2.1 2.2 2.3 2.4 2.5

Figure 2.6 Figure 2.7 Figure 2.8 Figure 2.9 Figure 2.10 Figure 2.11 Figure 2.12 Figure 2.13 Figure Figure Figure Figure Figure Figure

2.14 2.15 2.16 2.17 2.18 2.19

Perspectives on regulation of product fleets . . . . . . . . . . . . . General structure of the dissertation . . . . . . . . . . . . . . . . . . . Visualization of product architecture . . . . . . . . . . . . . . . . . . . Definition of a modular product system structure . . . . . . . . Modularity of product units . . . . . . . . . . . . . . . . . . . . . . . . . . Hierarchical decomposition of a product . . . . . . . . . . . . . . . Network of product components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fleet of multiple product systems . . . . . . . . . . . . . . . . . . . . . Flow-oriented life cycle model . . . . . . . . . . . . . . . . . . . . . . . Life cycle assessment framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Processes with input and output flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life cycle impact assessment . . . . . . . . . . . . . . . . . . . . . . . . . Classification and characterization of the impact assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life cycle costing by the VDI . . . . . . . . . . . . . . . . . . . . . . . . Fixed and variable costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life cycle costing—stages and costs . . . . . . . . . . . . . . . . . . . Chain of effects for environmental impacts . . . . . . . . . . . . . Life cycle impact assessment midpoint framework . . . . . . . Shares of greenhouse gases and main causes . . . . . . . . . . . . Depiction of the triple bottom line . . . . . . . . . . . . . . . . . . . . Eco-efficiency vs. eco-effectiveness . . . . . . . . . . . . . . . . . . . .

4 7 11 13 15 16 17 21 23 24 25 26 27 30 31 33 34 35 37 38 40

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List of Figures

Figure 2.20 Figure 2.21 Figure 2.22 Figure 2.23 Figure 2.24 Figure Figure Figure Figure Figure Figure Figure

2.25 2.26 2.27 3.1 3.2 3.3 3.4

Figure 3.5 Figure 4.1 Figure Figure Figure Figure Figure Figure Figure Figure

4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9

Figure Figure Figure Figure

4.10 4.11 4.12 4.13

Figure 4.14 Figure 4.15 Figure 4.16 Figure 4.17

Defining an environmental target . . . . . . . . . . . . . . . . . . . . . . Transport-related CO2 emissions in the European Union . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of NEDC and WLTP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Comparison of CO2 emission limitations worldwide . . . . . The product design paradox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Life cycle engineering framework . . . . . . . . . . . . . . . . . . . . . Eco-effectiveness and life cycle engineering . . . . . . . . . . . . Categorization of problem types . . . . . . . . . . . . . . . . . . . . . . Focus area for literature review . . . . . . . . . . . . . . . . . . . . . . . Liaison graph for module connections . . . . . . . . . . . . . . . . . Comparison of conventional LCA and modular LCA . . . . . Representation of alternative module combination options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Design alternatives for product design . . . . . . . . . . . . . . . . . Framework for the optimization of eco-effectiveness for modular product systems . . . . . . . . . . . . . . . . . . . . . . . . . Functionality of the user interface . . . . . . . . . . . . . . . . . . . . . Undirected graph and undirected and induced subgraph . . . Weighted and directed graph and the shortest path . . . . . . . Network for minimum-cost flow problem . . . . . . . . . . . . . . . Integration of additional nodes . . . . . . . . . . . . . . . . . . . . . . . . Multiple limitations for different life cycle stages . . . . . . . . Modular product unit as a network . . . . . . . . . . . . . . . . . . . . Product system structure network of a modular product system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Structure networks with interdependencies . . . . . . . . . . . . . . Network B with a separated structure network . . . . . . . . . . Network structure with correction factor . . . . . . . . . . . . . . . Separation of use stage from product system network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Difference between node demand and data demand . . . . . . Applying reduction strategies to a system structure network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Flow chart of the general logic of the optimization model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of key variables and arc design . . . . . . . . . . . . . .

42 46 48 51 52 54 56 59 69 71 73 80 81 95 97 98 99 99 101 103 105 107 109 112 114 117 120 121 125 126

List of Figures

Figure Figure Figure Figure Figure Figure Figure Figure Figure

4.18 4.19 4.20 4.21 4.22 4.23 4.24 4.25 4.26

Figure 4.27 Figure 5.1 Figure 5.2 Figure Figure Figure Figure

6.1 6.2 6.3 6.4

Figure 6.5 Figure 6.6 Figure 6.7 Figure Figure Figure Figure Figure

6.8 6.9 6.10 6.11 6.12

Figure Figure Figure Figure Figure Figure

6.13 6.14 6.15 6.16 6.17 6.18

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Definition of arc design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Definition of optimization objectives . . . . . . . . . . . . . . . . . . . Definition of constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exemplary provision of input data (I) . . . . . . . . . . . . . . . . . . Exemplary provision of input data (II) . . . . . . . . . . . . . . . . . Export of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Visualization of results in the network . . . . . . . . . . . . . . . . . Scatter plot for visualization of results . . . . . . . . . . . . . . . . . Scatter plot visualization of results for single product optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Scatter plot visualization of results for multiple product optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Summary of implemented software tools to cover all required steps of the approach . . . . . . . . . . . . . . . . . . . . . . . . Exemplary user interface for data provision to the optimization algorithm . . . . . . . . . . . . . . . . . . . . . . . . . Life cycle of a vehicle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of materials within a vehicle . . . . . . . . . . . . . . . . . . . . End of life treatment of vehicles . . . . . . . . . . . . . . . . . . . . . . Greenhouse gas emissions of different types of powertrains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of emission sources along the life cycle stages . . . . . . . . . . . . . . . . . . . . . . . . . . . Total costs of ownership from customer’s perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Distribution of the TCO along the different life cycle stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Possible emission reduction approaches . . . . . . . . . . . . . . . . Module network for case study application . . . . . . . . . . . . . Network logic for optimization network . . . . . . . . . . . . . . . . First 120 lines of data structure table . . . . . . . . . . . . . . . . . . Emission and TCO profile of all vehicle configurations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network flow for optimized vehicles . . . . . . . . . . . . . . . . . . Scatter plot results for single vehicle optimization . . . . . . . Vehicle fleet optimization for scenario one . . . . . . . . . . . . . . Vehicle fleet optimization for scenario two . . . . . . . . . . . . . Network flow for fleet optimization scenario two . . . . . . . . Vehicle fleet optimization for scenario three . . . . . . . . . . . .

127 128 129 130 131 132 133 134 135 136 138 140 146 147 149 150 151 155 155 158 160 170 172 173 176 178 179 179 180 181

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List of Figures

Figure 6.19 Figure 6.20 Figure Figure Figure Figure Figure Figure Figure

6.21 6.22 6.23 6.24 6.25 6.26 6.27

Network flow for fleet optimization scenario three . . . . . . . Vehicle fleet optimization for scenario three with use stage limits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Vehicle fleet optimization for scenario four . . . . . . . . . . . . . Network flow for fleet optimization scenario four . . . . . . . . Results of sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . Fleet composition for sensitivity analysis scenarios . . . . . . Comparison of vehicle and fleet results . . . . . . . . . . . . . . . . Result overview for all fleet scenarios . . . . . . . . . . . . . . . . . Share of powertrains for all emission limit scenarios . . . . .

181 182 183 184 186 187 188 190 191

List of Tables

Table 2.1 Table 2.2 Table 2.3 Table 3.1 Table 3.2 Table Table Table Table

4.1 6.1 6.2 6.3

Table Table Table Table

6.4 6.5 6.6 A.1

Selected environmental impact categories, indicators, and reference units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Limitation values for air pollutants set by Euro Standard 6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Complexity classes and big O notation . . . . . . . . . . . . . . . . . . Criteria for optimization of eco-effectiveness for product systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Evaluation of published approaches for LCA and LCC optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Data structure table for an exemplary network structure . . . . Purchasing costs of different powertrains . . . . . . . . . . . . . . . . Assumed costs of use stage for different powertrains . . . . . . Summary of input data for basic vehicles and energy carriers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Input data for production-focused alternatives . . . . . . . . . . . . Input data for use stage-focused alternatives . . . . . . . . . . . . . . Single vehicle optimization results . . . . . . . . . . . . . . . . . . . . . . Data structure table with algorithm input data . . . . . . . . . . . .

27 45 60 85 87 123 153 154 162 164 165 175 200

xix

1

Introduction

1.1

Present Situation and Problem Statement

Driven by an increasing global population, economic globalization, and higher standards of living in developing countries, the global primary energy demand has rapidly grown in the past (Herrmann 2010, p. 35). For the future, the International Energy Agency (IEA) predicts a continuation of this trend. The primary energy demand is supposed to increase by more than 30% towards 207.78 billion MWh until 2040 compared to the level of 2014 (IEA 2016, p. 61). Along with the rising demand for energy, the global exploitation of resources and raw materials such as biomass, metals and nonmetallic minerals is supposed to rise from 88.6 billion tons in 2017 to over 180 billion tons in 2050, following the current trend (UNEP IRP 2017). These human-made interactions with the earth’s environment are accompanied by numerous direct and indirect consequences. The extraction of resources and the utilization of various energy forms impact the air, soil, and water of the earth’s ecosystem through exhaust gases and wastes (Herrmann 2010, p. 13). The influence of such an extensive use of energy and resources on the ecosystem further leads to negative environmental effects like human or ecotoxicity, ozone depletion, climate change or land use impacts (Jolliet et al. 2004, p. 395). Different political organizations have ratified binding agreements to strengthen environmental protection or to limit environmental pollution, aiming to mitigate negative environmental effects. The Paris Agreement of 2015 is one example. 195 signatories have agreed to “holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels” (United Nations © The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 C. D. Gabrisch, Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry, AutoUni – Schriftenreihe 164, https://doi.org/10.1007/978-3-658-40594-6_1

1

2

1

Introduction

2015a, p. 3). In the seventh environment action program of the European Union (EU), the participating countries agreed to reduce the greenhouse gas emissions by 20% and to increase the share of renewable energies as well as the energy efficiency by 20% by 2020 (European Parliament 2013, p. 2). Furthermore, also customers start to demand products with less negative environmental impacts and a more ecologically friendly image (Cerri, Taisch & Terzi 2014, p. 1). Hence, reducing the environmental footprint of a product is also a competitive advantage for companies. Companies need to apply measures for the impact reduction already within the early product development phase to meet such environmental targets (Herrmann 2010). It is also important to evaluate the environmental performance of a product along the entire life cycle to reduce the environmental impacts (e.g. greenhouse gas emissions) in a holistic manner and to avoid the shifting of environmental burdens from one life cycle stage to another. Consequently, the environmental performance of a product needs to be measured in line with the principles of Life Cycle Assessments (LCA) (DIN ISO 14040). Considering the entire life cycle of a complex product, there are different measures or options that change the product system and help to reduce environmental burdens like the emissions of greenhouse gases (GHG) of the product. Measures that influence and improve the environmental performance of a product along its entire life cycle are e.g. utilizing recycled materials, reducing the energy consumption of the product, or increasing the energy efficiency as well as utilizing renewable energy sources (IPCC 2014). For many product types, such measures are implemented into a product’s life cycle in many different forms and during different stages along the life cycle. All measures with the purpose of reducing e.g. the GHG emissions of a product can be characterized and distinguished by differences regarding their impact power, time horizons, scalability, availability, interdependencies with other measures as well as cost structures. Companies that produce goods for customers in an open market operate within a field of economic competition. Their decisions regarding product changes therefore need to consider financial consequences (Paul 2015). New environmental targets concerning a specific product require changes to the product system which should therefore ideally be realized with least financial extra costs. Thus, it is possible for trade-offs between financial and environmental targets to occur that require an eco-efficient solution (Herrmann 2010, p. 47). Following this, an environmental target of a product should be achieved by choosing those measures that yield the most economically efficient realization. For products with a complex life cycle and a modular product architecture, many measures exist to alter the environmental performance. A modular product

1.1 Present Situation and Problem Statement

3

architecture is defined by individual and independent modules that are physically detached from or can be combined with each other via defined interfaces (Gu & Sosale 1999, p. 387 ff.). Targets for modular products can be realized by e.g. replacing one module with an alternative that provides a better environmental performance. Exploring alternative product module options consequently helps to optimize entire product systems. However, the number of theoretically possible configurations for a modular product quickly reaches an extremely high number. Each of the individual configurations provides individual financial and environmental performances over the product’s lifetime, adding more complexity. The complexity is even further increased if not only a single product unit is the target of regulation but an entire population, also called a fleet, of products. In the automotive context, such a fleet can be the yearly production of an original equipment manufacturer (OEM), but can also refer to all vehicles within a city or the fleet of a company. As every single product unit within such a fleet can be configured individually, many different fleet configurations are possible. Figure 1.1 shows multiple product units which together form a fleet of individual products. Such a product fleet is regulated from different perspectives following different environmental targets that need to be fulfilled. If the regulation (or an OEM with self-set goals) defines targets from a bottom-up perspective, every product unit within the fleet needs to meet these targets individually. In a top-down perspective, targets are set for the entire fleet in general. Here, the target must be fulfilled by all product units together, e.g. as an average result. This means that individual products can exceed the limit if other products remain below the limit. While the automotive industry is currently regulated regarding the use stage emissions only, the legislation can be expanded to cover the entire life cycle in future. Hence, the product development strongly depends on the given perspectives and targets that the product units and fleet must meet. Modular products in fleets quickly scale up to enormous combinatorial possibilities due to their nature of exponential growth. As equation 1.1 shows, the number of possible combinations (c) depends on the number of alternatives per module (a), the number of modules within a product unit (m) and the number of product units within a fleet (n). c = a mn

(1.1)

Following equation 1.1, an exemplary product that consists of only three modules with each module offering three alternatives and a product fleet of five product units, a total of over 14 million possible fleet configurations are possible. Consequently, the identification of the ideal set of measures for each product unit

4

1

Introduction

fleet perspecve

“top down”

unit perspecve

“boom up”

regulaon of product fleets

overall targets for fleets (e.g. CO2 compliant fleet) target must be fulfilled as average over all product units

specific targets per product unit (e.g. best in class) target must be fulfilled by every product unit of the fleet

life cycle perspecve

life cycle regulaon targets defined for the enre life cycle material extracon

producon

use phase

end of life

use phase regulaon targets for a single stage

Figure 1.1 Perspectives on regulation of product fleets

of a product given specific targets is a complex task that requires a systematic approach. Within this work, a method for calculating eco-effective pathways in order to achieve an environmental target for a single product or multiple product units by meeting an absolute environmental target with an optimized cost structure is developed. One of the biggest sources for GHG emissions is the transport sector and herein the automotive industry. In Europe, over 13% of the emissions are caused by road-based passenger traffic (European Parliament 2019). While other industries were able to reduce their emission level over the past years, the transportation sector has increased its emissions due to rapidly growing transport demand. The European Union therefore has set strict regulations for the automotive industry to drastically reduce its climate impact by 2050 (European Parliament 2019). Since 2020, the limit for CO2 emissions for an OEM’s fleet is set to 95 grams of CO2 per kilometer on average. This limit will be further reduced by −5% in 2025 and −37.5% in 2030. (European Parliament 2019) This demonstrates how urgently the automotive industry needs to identify solutions to comply with new emission standards in order to avoid potential penalties.

1.2 Research Objective and Structure of Thesis

5

Considering the automotive sector, strict targets for emission reduction demand extensive changes within the industry, as new solutions require new technologies that need to be integrated into the fleets in a short time span. Regarding the high relevance of the automotive industry for climate related issues and the ongoing technological change process in this industry, the method developed in this work will be closely designed for an automotive application and validated with a transfer to an example of a vehicle’s life cycle.

1.2

Research Objective and Structure of Thesis

The target of this work is to enable decision makers to select combinations of measures or module alternatives for modular product configurations that fulfill environmental targets with least costs. An analytical evaluation of possible measure combinations aims to disclose hidden potentials of individual measures on the environmental footprint of a product along the entire life cycle. Also, misconceptions of individual measures due to their performance within the whole product system are revealed. Furthermore, this work shall also support investment decisions for optimizing the environmental performance. Given the case that a company voluntarily plans to invest into the optimization of their products environmental footprint, the developed approach provides recommendations on measures that result in highest environmental benefits for the available budget. Within this work, a methodology is developed that systematically evaluates all technically and logically possible configurations of alternative product systems regarding their financial and environmental life cycle performance. Additionally, interdependencies between combined measures as well as external financial impacts, like varying material prices, are considered. Based on the data of the financial and ecological performance, an optimization algorithm is applied that, for an environmental target or financial budget, identifies and displays the most effective set of measures or module combinations for the product system or fleet of multiple product systems. The presented methodology is applicable to problems considering the configuration of just one product system, but can also be expanded to problems where the given target is set as the average for a group of multiple product systems. An example for the latter problem type is the limitation of carbon dioxide (CO2 ) emissions for the fleet of car manufacturers. While individual vehicles can be above the limit, the average fleet emission must meet the restriction. (European Parliament 2009)

6

1

Introduction

In detail, the objectives of this thesis are: • to provide decision support for decision makers in the automotive industry regarding the cost-effective compliance with environmental targets, • to develop a methodology that identifies the most eco-efficient product configuration while considering interdependencies between measures along the entire life cycle, and • to enable the application to not only single but also multiple products of the same type as e.g. a vehicle fleet. This work is structured in seven consecutive chapters (see figure 1.2). The second chapter introduces the reader to the theoretical background of the covered topic. Preliminary remarks and definitions of modular product systems are presented in subchapter 2.1. Afterwards, an introduction to the concept of product life cycles and their evaluation is given. Established procedures to describe the borders and individual stages of a product’s life cycle are presented, as well as methods to assess the ecological (life cycle assessment) and financial (life cycle costing) performance of a product (subchapter 2.2). Then, the theoretical background of sustainability and the current situation of general and automotive-specific legislation regarding environmental impacts is explained in subchapter 2.3. In subchapter 2.4, life cycle engineering and optimization methods are introduced. The third chapter introduces the challenges and requirements that need to be fulfilled to identify optimized eco-effective product systems and criteria that are required to evaluate the suitability of the developed approach (subchapter 3.1). Based on a literature review, the current approaches within this field of study are analyzed and an overview of the present state of research for the optimization of a product’s eco-effectiveness is provided (subchapter 3.2). The state of research is then evaluated regarding the criteria from subchapter 3.1 and the present research gap is highlighted (subchapter 3.3). In the fourth chapter, the requirements derived from the research gap are discussed (subchapter 4.1), followed by a detailed description of the newly developed framework and the selected optimization approach (subchapter 4.2). The modelling of the product systems and the provision and preparation of the required input data are elaborated in subchapter 4.3. Chapter four continues with the description of the developed data management (subchapter 4.4) and the adaption of the selected optimization approach to the problem statement (subchapter 4.5). Finally, chapter four closes with the visualization and interpretation of the output of the optimization (subchapter 4.5).

1.2 Research Objective and Structure of Thesis

7

1. Introducon movaon, research queson & objecves

2. Theorecal background preliminary remarks & technical overview 2.1

Modular product systems

Life cycle thinking

2.2

2.3

Sustainability & legislaon

2.4

Life cycle engineering

3. State of research challenges, current approaches and need for acon 3.1

Challenges and criteria

3.2

Current approaches

3.3

Idenficaon of research gap

4. Development of methodology Framework, input-data, opmizaon approach, interpretaon 4.1 / Requirements & framework 4.2

4.3 / 4.4

Modelling & input data

4.5

Opmizaon algorithm

4.6

Visualizaon & interpretaon

5. Prototypical implementaon Exemplary implementaon and applicaon cycle of methodology 5.1

Prototypical implementaon

5.2

Applicaon cycle

6. Exemplary applicaon of methodology Applicaon to a problem statement of the automove industry 6.1

Life cycle of automobiles

6.2 / 6.3

Measures and input data

6.4 / 6.5

Results & interpretaon

7. Summary & outlook summary, crical acclaim & outlook

Figure 1.2 General structure of the dissertation

Chapter five describes a prototypical implementation of the developed methodology using selected tools for the individual steps to present one feasible way for a technical application of the developed methodology (subchapter 5.1). Also, an exemplary application cycle is described to show how the tool must be operated to receive the desired output (subchapter 5.2). Chapter six discusses the exemplary application of the methodology to a current problem within the automotive industry. In subchapter 6.1, the typical life cycle of a vehicle (based on the concepts of Section 2.2) is described, followed

8

1

Introduction

by a selection and evaluation of applicable measures for improving the environmental performance of a vehicle and the preparation of the specific input data (subchapter 6.2 and 6.3). The subchapters 6.4 and 6.5 present the results of the calculations and derive recommendations by interpreting the results. The seventh chapter finally closes with a summary, a critical appraisal of the approach and an outlook on further fields of study.

2

Theoretical Background and Technical Overview

In order to identify the ideal product system configuration for single or multiple products, several fundamental terms and definitions need to be discussed. This chapter introduces the fundamentals regarding the structure of modular products, the life cycle perspective, aspects of sustainable development, environmental legislation as well as life cycle engineering and the principles of mathematical optimization.

2.1

Modularity of Product Systems and Product System Fleets

Products and their modular architecture are the foundation of the environmental optimization along the entire life cycle of product systems. In the following sections, the definition of products, product systems and a modular product architecture are discussed. Furthermore, product families and product fleets are also defined. Lastly, it is shown how the step from a single product unit to a population of multiple units changes the complexity regarding available configurations of product families.

2.1.1

Modular Product System Architectures

A product is defined as the result of processes or activities. Products are distinguished between physical goods, software, knowledge, information, and services.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 C. D. Gabrisch, Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry, AutoUni – Schriftenreihe 164, https://doi.org/10.1007/978-3-658-40594-6_2

9

10

2 Theoretical Background and Technical Overview

A product can also be a combination of these forms. (Müller 2013, p. 10) The DIN ISO 14040 for environmental management and life cycle assessment defines a product as “any good or service” and distinguishes between services, software, hardware, or processed materials (DIN ISO 14040, p. 8). The term “production” focuses on the human caused transformation of objects regarding changes in quantity, quality, spatial or temporal aspects. While temporal and spatial changes refer to logistics, the changes of quantity and quality strive for the creation of new goods or services, leading to an added value. This makes the production a core process of every company. (Dyckhoff & Spengler 2007, p. 3) Matiz-Rubio et al. (2020) define a product system as the entire life cycle from resource provision over the production and use stage to the end of life of the product, including co-products (Matiz-Rubio, Eltrop & Härdtlein 2020, p. 223). In the DIN ISO 14040, a product system is defined as a “collection of unit processes with elementary and product flows, performing one or more defined functions, and which models the life cycle of a product” (DIN ISO 14040, p. 11). For this work, a product system is understood as the product itself, the required inputs and outputs as well as the production and end of life treatment of the product. Modular product architecture As a product can have many forms and serve many different purposes, the architecture can vary from simple forms with little functionality (e.g. a coin) to very complex structures (e.g. a vehicle). The design of the product architecture is an important aspect during the product development phase since the functionality as well as the environmental and financial impacts of a product system highly depend on the architecture of the product. Ulrich defines the product architecture as “the arrangement of the functional elements, the mapping from functional elements to physical components and the specification of the interfaces among interacting physical components” (Ulrich 1995, p. 420). The functional elements represent the parts of the product that define its functions and characteristics, while the physical components are the actual parts that enable these functions (Ulrich 1995, p. 420). Müller (2013) describes the product architecture as the organization of how the functional elements are brought together to form the final product. The smallest element is defined as a part. Such a part is also described as the smallest physical level of a product which cannot be disassembled any further without destroying it. It usually shows no higher complexity and specialization for a certain function. Multiple parts form components, while multiple components form a module.

2.1 Modularity of Product Systems and Product System Fleets

11

Such a module is defined by its ability to realize one or multiple functions for the product. (Müller 2013, p. 12) Kim and Moon (2019) describe the product architecture similarly, as the final product is built out of multiple modules, while each module is formed by multiple components. As the lowest level, Kim and Moon (2019) do not include the level of parts but introduce a level for the utilized material (see figure 2.1).

Figure 2.1 Visualization of product architecture (Kim & Moon 2019, p. 388)

For this work, the architecture of a product is based on the definition of Müller (2013) and Kim and Moon (2019) and adapted to fit the definitions of a product and a product system of the DIN ISO 14040. The term “product (system) structure” is used synonymously for a product’s (or a product system’s) architecture in the following chapters. Figure 2.2 shows the structure of a modular product unit and how it is embedded into a whole product system. In the example of figure 2.2, the product unit consists of three different modules (A, B and C). These modules represent the highest level of elements in the product architecture below the level of the assembled product. Each module then is made from two different components, which in turn also consist of two different parts. The product units’ structure is formed from lowest to highest level: part  component  module  product unit. Adjunct processes of the life cycle perspective, like material extraction, production steps, the use stage and end of life treatment are

12

2 Theoretical Background and Technical Overview

associated to the product unit as well as input (materials, energy, etc.) and output streams (waste etc.). All together, these elements of a product unit and supporting processes form the entire product system. As already defined in chapter one, a modular product design, as shown in figure 2.2, consists of individual and independent modules that can be physically detached from or combined with each other via defined interfaces (Gu & Sosale 1999, p. 387 ff.). Wilhelm (1997) defines modularity as “a complex assembly forming a closed function unit which permits specific differentiation and which, as a consequence of defined interfaces (function, geometry), can be developed, manufactured and assembled independently. Such an assembly must be interchangeable and/or capable of alternative installation, it must represent an efficient unit in terms of production and logistics, and it must require a minimum of modifications.” (Wilhelm 1997, p. 147). The more detailed the functions of the product are described, the more clearly the individual functions the products modules need to provide can be understood. The mapping from the required product functions to the modules can be done in different ways. One option is the “one-to-one” approach, where each function is provided by one module. Other approaches have multiple functions combined within one module (“many-to-one”) or have many modules within the product that all support one function (“one-to-many”). If a product consists of a one-to-one mapping of modules and functions, it is called a modular product architecture, while one-to-many or many-to-one indicate an integral product architecture. (Ulrich 1995, p. 420 ff.) Interactions between the modules of a product usually consist of the exchange of material flows, energy, or signal information that lead to a higher functionality of the final product (Gu & Sosale 1999, p. 387 ff.). Jose and Tollenaere (2005) describe modularization as “an approach to organize complex designs and process operations more efficiently by decomposing complex systems into simpler portions. It allows the designer to play with combinations of groups of components to develop and customize a larger quantity of products.” (Jose & Tollenaere 2005, p. 371). Product modularity including module alternatives The complexity of modular products increases when for each module multiple alternatives exist that provide the same function but differ in e.g. material choices or shape. Thus, switching between module alternatives does not change the functionality of the final product, but leads to different configuration options during the product design. Figure 2.3 shows an exemplary product structure for a product unit that is built from four different modules that each provide a unique function

part 2

part 4 part 4

component 2

part 3

product unit

module B

component 1

part 2

product development part 1

producon

Component 2

part 3

module A

component 1

part 1

Figure 2.2 Definition of a modular product system structure

input streams (material, energy, …)

material extracon

module

component

part

architecture level

product system part 2

use phase

part 4

component 2

part 3

module C

component 1

part 1

output streams (waste, …)

end of life

life cycle perspecve

2.1 Modularity of Product Systems and Product System Fleets 13

14

2 Theoretical Background and Technical Overview

to the product (module A, module B, module C and module D). Every module offers multiple alternatives, where each alternative may lead to differences in its environmental and/or financial performance compared to the initial module. For module A, these alternatives are denoted as MA1 , MA2 , MA3 and MA4 (correspondingly for the remaining modules). Each module in turn consists of three components (component A, component B and component C), while each component has three alternatives (CA1 , CA2 and CA3 for component A). On the lowest level of the product’s architecture, the level of parts, each component consists of two parts (part A and part B), each having two alternatives (PA1 and PA2 ). Regarding the entire product unit, 16 module alternatives are available on the highest architecture level. Of these options four must be chosen to be part of the final product, one for each necessary module. One level deeper into the architecture, on the level of components, 144 alternatives are available of which twelve must be selected for the product unit. On the lowest architecture level, 576 part alternatives form the basis of the product unit, here 24 parts need to be selected in total. To build an entire product, only one, but also always a minimum of one alternative of the pool of alternatives, must be chosen. In this work, the terms “alternatives”, “variants” and “options” are used synonymously to describe alternatives for modules, components, or parts with the same functionality but different properties e.g. material or weight. Each individual product unit of the possible product unit configurations is further referred to as individual or unique “product configuration”, as each unit is configured from one possible combination of the available module alternatives. A higher modularity and a higher number of module alternatives lead to a higher number of possible product configurations. Degree of modularity and dependencies In order to be able to assess the quantity of modules a product unit consists of and determine the ideal degree of modularity, many studies have been performed. Following Ulrich (1995), two modules are coupled if a change to one module requires a change of the other module. It is also stated that two physically connected components are almost always coupled. The physical connection between two modules often leads to dependencies between the modules as the characteristics of one module influence the features of the other module. For a product with a fully modular product architecture, a change of one module does not affect the other modules. Considering a more integral product design, a change of one module can also lead to required changes to other modules. (Ulrich 1995, p. 422 ff.)

2.1 Modularity of Product Systems and Product System Fleets

15

modular product unit

architecture level PA1

PA2

PB1

part A

CA1

PB2

part B

CA2

CA3

component A

part

CB1

CB2

CB3

component B

CC1

CC2

CC3

component C

MA1 MA2 MA3 MA4

MB1 MB2 MB3 MB4

MC1 MC2 MC3 MC4

MD1 MD2 MD3 MD4

module A

module B

module C

module D

component

module

product unit

Figure 2.3 Modularity of product units (based on Jose & Tollenaere 2005, p. 374)

A high modularity and functional independence of the modules reduces changes of the entire product, due to changes in selected functionality, to a minimum. Ulrich (1995) distinguishes the performance of a product into local and global performance characteristics. While the local performance of a function can be optimized by enhancing the functionality of the module that is responsible for this function (e.g. brighter lights for better sight), a global performance (e.g. the fuel efficiency of a vehicle) depends on the characteristics of multiple modules. Hence, the optimization of the global performance requires the consideration of all modules and their interdependencies. (Ulrich 1995, p. 432 f.) Chen et al. (1994) have shown that the independence between the individual modules of a product can be increased by decreasing the interactions between these modules (Chen, Rosen, Allen & Mistree 1994, p. 31). The research of Gu and Sosale (1999) link a modular product architecture to the goals of life cycle engineering (LCE). It is stated that to reach the objectives of LCE with a modular product, “the relationships between the objectives and the modules should be established” (Gu & Sosale 1999, p. 387). Following Gu and Sosale (1999), a physical interaction between product modules is described by the form of attachment, the positioning of the modules, the motion and the containment. When individual components are grouped into modules, these interactions need to be considered. For product design goals like high standardization, mass customization and potential reuse and recyclability, Gu and Sosale (1999) introduce a method for multiple criteria decisions to identify the ideal product

16

2 Theoretical Background and Technical Overview

modularity with minimal interactions between the modules. (Gu & Sosale 1999, p. 387 f.) Sosa, Eppinger and Rowles (2007) define modularity “as the level of independence of a component from the other components within a product” (Sosa, Eppinger & Rowles 2007, p. 1120). Hence, the modularity of a component increases with a higher independence of other components, offering more degrees of freedom. Figure 2.4 shows the hierarchical decomposition of a product into its components. By assessing the connections between individual components, the degree of connectivity is quantified. Using the approach of graph theory, Sosa et al. (2007) identify the most important components of the products network by evaluating the centrality of the nodes. A high centrality of a node occurs when the respective component is “directly connected” to many other components, “close to” all other components and “between” many other components and therefore connecting them. (Sosa et al. 2007)

Figure 2.4 Hierarchical decomposition of a product (Sosa et al. 2007, p. 1119)

Building on the design dependencies between components that are defined by physical connections and flows of energy, material or information, Sosa et al. (2007) also include virtual connections into their modularity assessment. Due to these virtual connections, not only physical relations but also design dependencies are considered. These design dependencies are represented by the distance to other components and the connection the component enables between other components. Three types of modularity are introduced that can be separately assessed to measure the independence of modules within a product (Sosa et al. 2007):

2.1 Modularity of Product Systems and Product System Fleets

17

• Degree modularity: Describes the physical connections between components. The more components of a product are affected by a change of a component, the lower the modularity of this component. If a change of a component affects no other component, this component is “completely disconnected”, resulting in the highest possible modularity. • Distance modularity: The distance between a specific component and other components also indicates the level of modularity. The distance only depends on the direction of the connection and not on the strength of the connection. A high distance modularity indicates that a component is connected to other components by many linking components making it an isolated component with a higher modularity. • Bridge modularity: The bridge modularity focusses “on those components that lie in the dependency path of two components” (Sosa et al. 2007, p. 1122). If a component serves as a bridge between many modules by connecting components, it becomes responsible for the propagation of the design dependencies between the connected components. The bridge modularity of a component is defined by its appearances in the dependency paths of other components. The higher the number of paths a component occurs in, the lower its bridge modularity (Figure 2.5).

Figure 2.5 Network of product components (Sosa et al. 2007, p. 1120)

By assessing these three different kinds of modularity, a deeper understanding of the interdependencies between the modules of a product is achieved. This

18

2 Theoretical Background and Technical Overview

understanding helps optimizing the separation of components into modules. Furthermore, the effect that changing one module has on the entire product can be estimated. (Sosa et al. 2007) Modular product systems Based on the principles of modularity in products, it is also possible to define modular product systems. It is required to transfer the adjunct and supportive processes as shown in figure 2.2 into the structure of a product unit to expand the modularity from product units to the entire product system. In this way, production processes or material choices can also be modular and exchangable. Thereby they contribute to the level of modularity of a product and expand the number of possible product system configurations as well as the possibility of occurring interdependencies between several choices of alternatives.

2.1.2

Multiple Product Systems and Population Fleets

The introduced product system perspective cannot only be applied to a single product system, but also to multiple product systems. A collection of multiple product systems is referred to as e.g. a product system population or a fleet of product systems. Such fleets often come from the same product family, as e.g. multiple vehicles of the same type form a fleet of vehicles. Product families Every single product unit within such a product family is described as a product family member or product variant of the product family. The basis for the product family, a modular product platform, is used to produce variations of a basic product to create new variations of products by reconfiguring different module alternatives. This approach is called “configurational product family design”, as it is based on modular product architectures as new products are built from existing and standardized models. (Jiao, Simpson, & Siddique 2007) Simpson (2004) defines a product family as “a group of related products that is derived from a product platform to satisfy a variety of market niches” (Simpson 2004, p. 4). Simpson (2004) further describes a product family as the result of a platform-based product development where new product units of that product family are created by “adding, substituting and/or removing” the modules of the basic product system that provides the desired functionality (Simpson 2004, p. 5). It is important to design the product structure in such a way that the resulting modules lead to a high variety of possible product family alternatives to serve

2.1 Modularity of Product Systems and Product System Fleets

19

many different purposes from the existing modules to utilizing product families in an efficient way. When designing product families and defining the module partitioning, four major influences are described by Dahmus, Gonzalez-Zugasti & Otto (2001): (1) market variance to cover different customer concerns, (2) market variance to cover variety needs after the purchase, (3) technology change for updating the product design and (4) design for X to consider life cycle criteria. Yang et al. (2014) describes one central aspect of product families as the improvement of commonality by defining fixed modules that are shared by all product units of the product family. These modules form the basis while further modules help to create different functionalities by varying additional modules. (Yang, Yu & Jiang 2014) Advantages of product families Building product fleets based on product families from a shared platform leads to many advantages for the OEM of these products, as many different authors have shown. Most of these advantages occur due to the utilization of standardized modules across a large scale of product systems. One advantage is an easier and more cost-efficient mass customization of product units to fit customer needs. Pandremenos et al. (2009) describes the modularity in production as the basis for mass customization of vehicles. A modular production allows the OEM to “pre-combine a large number of components into modules” while still allowing individual combinations of modules (Pandremenos, Paralikas, Salonitis & Chryssolouris 2009, p. 148). For OEMs, e.g. in the automotive sector, the utilization of product fleets based on product families also saves expenses when building many product units on the same platform with a high commonality of components. Such product families have many shared parts which simplifies production and makes it more cost-efficient. (Dahmus, Gonzalez-Zugasti & Otto 2001) These reduced production costs are also realized due to components or modules not only being similar, but sharing many production processes. Also, with modular product architectures, product derivates can be developed quicker and more cost-efficiently as developers focus on specialized modules to customize and differentiate the new product from the product family. (Jose & Tollenaere 2005, p. 371 ff.) Simpson (2004) also adds that not only development time is reduced, but also the time and effort that is required for testing and certification of new product derivates. Modular product families also lead to a better ability of upgrading product units. (Simpson 2004, p. 4) In the extensive literature review, Bataglin and Ferreira (2020) also identified a positive impact of product families on the reusability of product units as well as the

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2 Theoretical Background and Technical Overview

reparability and quality issues as broken modules can more easily be replaced. This supports the longevity of a product unit. (Bataglin & Ferreira 2020) Kim and Moon (2019) describe a better recyclability as an advantage of product families with shared modules, as the disassembly and recycling processes can be optimized and standardized to improve the product recovery. (Kim & Moon 2019) Additionally, Yang et al. (2014) describes that modular product fleets and their shared modules with a high commonality use their economy of scale to bring environmental and financial optimization into a large amount of product units in a short time. Wedler and Vietor (2019) point out that with a modular concept for autonomous vehicles (e.g. separation in life and drive modules), more passenger transport scenarios can be realized without having to increase the number of vehicles (Wedler & Vietor 2019). Fleets of product systems As described by various authors, product families based on a modular product platform with variable modules offer many advantages. However, besides these positive aspects, more complexity is also added when modularization is applied for mass customization. When not only a single product unit is considered but a fleet of multiple units, the optimization process needs to cover all units and not only one single unit of the product fleet. As every unit of the product fleet is configured freely and individually from all available alternatives per module, the total number of possible options for a fleet configuration grows quickly compared to the options for only a single product unit, as shown in equation 1.1. Figure 2.6 shows the structure of a product system as introduced in figure 2.2 expanded as a fleet of multiple product systems and expanded with a modular product architecture as introduced in figure 2.3.

2.2

Life Cycle Thinking

Product systems, as introduced in the previous chapter, are described and assessed in different ways and from different perspectives. In order to fully consider all environmental and financial aspects that are related to the product system, it is important to consider the entire life cycle and not only parts of it. Following this life cycle perspective, the environmental life cycle assessment and the financial life cycle costing can be performed.

Figure 2.6 Fleet of multiple product systems

input streams (material, energy, …)

material extraction

life cycle perspective

fleet of product systems

production

product unit

product development

use phase

n

… n

= number of product unit within fleet

output streams (waste, …)

end of life

1

2.2 Life Cycle Thinking 21

22

2 Theoretical Background and Technical Overview

Life cycle perspective Product life cycle concepts are descriptive models that depict the economic and ecologic effects of the individual life cycle stages of a product or a product system (Mateika 2005, p. 8). Today, there are many ways to describe the life cycle of a product, focusing on different stages or performance indicators. The basic model of a product’s life cycle has been introduced by Hofstätter (1977), which describes financial figures like the revenue of a product over its market time. Since then, other life cycle concepts have been developed that also cover additional aspects of the life cycle. These additional aspects include the production and end of life stage of a product as these stages also define the properties and performance of the product over its lifetime. (Herrmann 2010, p. 70 ff.) Formative life cycle concepts that go beyond the concept of Hofstätter (1977) are e.g. the integrated life cycle concept by Pfeiffer and Bischof (1981), which includes the period before the market entry, or the systemic product life cycle by Klenter (1995) that expands to a production and an end of life stage (Mateika 2005, p. 9 ff.). A different form of life cycle modelling is found in flow-oriented concepts. These models focus on the material and energy flows from the beginning to the end of a life cycle in a linear model or even in a circle back to the beginning. Such a circular model applies when recycling is used to retrieve secondary material from the old product at the end of a life cycle. (Herrmann 2010, p. 65) A flow-oriented life cycle concept can roughly be divided into the phases of material extraction, production and manufacture, use and service and finally disposal and recycling (Keoleian, Kar, Manion & Bulkley 1997, p. 5). In figure 2.7, this typical flow-oriented life cycle model is depicted. The consecutive steps of “production stage”, “use stage” and “end of life stage” and their characteristic contents describe the life cycle of a product from cradle to grave. The typical life cycle, as depicted in figure 2.7, presents the boundary framework for the environmental and financial assessment of a product system in this work. A detailed introduction to the different forms of life cycle concepts is found in Hermann (2010).

2.2.1

Environmental Life Cycle Assessment

Assessing the life cycle of a product system regarding its ecological performance is done by analyzing the inputs and outputs of a product system and linking them to environmental impacts. As already stated in section 1.1, it is important to evaluate the environmental performance of a product system along the entire

2.2 Life Cycle Thinking

23

cradle to grave production phase

use phase

end of life phase

extraction of raw materials & manufacturing of the (intermediate) product

production of operating materials & utilisation or operation of the product

reutilisation and recycling & disposal of the waste- and scrapmaterials

Figure 2.7 Flow-oriented life cycle model (based on Broch 2017 and Keoleian et al. 1997)

life cycle to avoid shifting environmental burdens between life cycle stages. The global standard for evaluating the ecological life cycle performance are life cycle assessments (LCA) based on the DIN ISO norms 14040 and 14044. Such an LCA is distinguished in three different types. The first type is a gate-to-gate analysis linked to the processes related to the activities of a single company or factory. The second type is a more detailed analysis of a single process and the third type the life cycle assessment of an entire product system from cradle to grave. This third type covers all processes linked to this product, going beyond the borders of a single company or factory. (Hermann 2010, p. 151 f.) A life cycle assessment is a scientific method to quantify the impacts of a product system along the entire life cycle. It is a suitable tool to compare environmental impacts of two different alternatives. LCAs also help to avoid problem shifting between life cycle stages and provide a detailed database to support decision making regarding the product system, e.g. on how to reduce environmental impacts (Koffler 2007, p. 12 and UNEP 1996, p. 5 ff.). In the DIN ISO norm 14040, an LCA is defined as follows: [A life cycle assessment is the] “compilation and evaluation of the inputs, outputs and the potential environmental impacts of a product system throughout its life cycle.” (DIN ISO 14040, p. 7)

The general approach of an LCA is based on an iterative framework consisting of four steps: goal and scope definition, inventory analysis, impact assessment and interpretation of results (DIN ISO 14040, p. 4). After the LCA has been interpreted, a report for the intended audience has to be written. This report shall address the scope, data, assumptions and limitations of the study to make the calculated results and their interpretation as transparent as possible. (DIN ISO 14040, p. 32) Figure 2.8 depicts this life cycle assessment framework and its

24

2 Theoretical Background and Technical Overview

iterative steps. This norm-based approach of an LCA is referred to as a “regular” or “conventional” LCA. The functional unit needs to be the same for each comparative assessment to make sure that the result of an LCA is comparable to other LCA studies. This functional unit defines the reference unit to which all the inputs and outputs are related to by describing the functions the analyzed product shall fulfill. The DIN ISO norm 14040 provides an example of a specific number of dried hands serving as the functional unit in order to compare paper towels and blow dryer systems. (DIN ISO 14040, p. 23 f.) life cycle assessment framework step 1

step 2

step 3

goal and scope definition

inventory analysis

impact assessment

interpretation

step 4 Figure 2.8 Life cycle assessment framework (based on DIN ISO 14040, p. 16)

Step 1: Goal and scope definition In the step of goal and scope definition, the intended application and the reason why the LCA is performed have to be explained. The addressed audience needs to be named as well as the motivation behind the LCA (e.g. comparing two alternatives or identifying ecological hotspots of a single product). Also, the functional unit, the system borders (e.g. cradle-to-gate or cradle-to-grave) and the intention on whether the study is supposed to be published or not have to be defined. (DIN ISO 14040, Kloepffer & Grahl 2009 and Koffler 2007, p. 13) For the scope definition, details regarding the procedure of the LCA have to be described. Among them are e.g. the applied allocation procedures, cut-offs, the selected impact categories, the methodology of impact assessment, the assumptions and limitation of the study and the quality requirements for the input data. (DIN ISO 14040, p. 23)

2.2 Life Cycle Thinking

25

Step 2: Inventory analysis The step inventory analysis of the life cycle is defined as follows: “Inventory analysis involves data collection and calculation procedures to quantify relevant inputs and outputs of a product system. The process of conducting an inventory analysis is iterative. As data are collected and more is learned about the system, new data requirements or limitations may be identified that require a change in the data collection procedures so that the goals of the study will still be met.” (DIN ISO 14040, p. 25 f.)

Figure 2.9 shows the flow of materials as well as the inputs and outputs of a production process with three consecutive processes. The dotted line represents the system boundaries for this inventory analysis.

energy, water, air & process materials inputs raw materials

process A

process B

process C

product

outputs wastes, heat & emissions

Figure 2.9 Processes with input and output flows (based on Broch 2017, p. 15 and Kloepffer & Grahl 2009, p. 67)

For the inventory analysis it is crucial that the processes and material flows of the product system are correctly analyzed and modelled. Those processes and flows are then matched with the identified inputs and outputs like energy, raw materials or emissions and wastes. The goal of the inventory analysis is the calculation of the elementary flows, which represent the energy or materials that either come from or go into the environment without any further treatment. The outcome is the added results of the elementary flows per functional unit. Also, the cut-off procedures need to be documented to reduce complexity without compromising the relevant influences. (Kaltschmitt & Schebek 2015, p. 215 ff.)

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2 Theoretical Background and Technical Overview

Step 3: Life cycle impact assessment The third step of an LCA transfers the findings of the inventory analysis to the calculation of the potential environmental impact of each identified and quantified reference flow of the product system (Koffler 2007, p. 16). The impact assessment consists of three mandatory (selection, classification, characterization) and three optional elements (normalization, grouping, weighting) as depicted in figure 2.10. During the selection step, the relevant impact categories, category indicators and characterization models are chosen. The classification serves as the link of the results from the inventory analysis to an impact assessment. Due to this step, it is possible to determine whether an elementary flow has a specific environmental impact. The classification associates the results of the inventory analysis with their midpoint environmental impact (see figure 2.10). (DIN ISO 14040 and Koffler 2007) mandatory elements selection

classification

characterization

selection of impact categories, category indicators & characterization models

assignment of LCI results

calculation of category indicator results

+ optional elements (normalization, grouping, weighting)

Figure 2.10 Life cycle impact assessment (based on DIN ISO 14040, p. 30)

The different midpoint impact categories are expressed by means of a specific category indicator. This indicator characterizes all the elementary flows that are assigned to the same impact category into the same unit of measurement. The category of global warming is e.g. expressed by the indicator kg of CO2 eq. (DIN ISO 14044 and Koffler 2007, p. 18 f.). As each elementary flow has an individual impact on the selected midpoint category, the results of the inventory analysis are converted—using a characterization factor—into the dimension of the category indicator. Regarding the midpoint impact category of climate change, every elementary flow of the inventory analysis potentially has an individual effect on global warming. The emission of one kg methane for example has the same global warming potential (GWP) as 25 kg of CO2 , giving methane a CO2 equivalent of 25. (Kaltschmitt & Schebek 2015, p. 276) Figure 2.11 visualizes the

2.2 Life Cycle Thinking

27

process from the inventory analysis, classification and characterization towards the category indicator for the example of climate change.

inventory analysis

classification

characterization

category Indicator

midpoint category of climate change

quantification of the increase of infrared radiation

global warming potential in [kg CO2 eq.]

CO2 CH4 N2O …

Figure 2.11 Classification and characterization of the impact assessment (based on Broch 2017, p. 17)

Table 2.1 lists selected environmental midpoint impact categories and their respective indicators and reference units. The optional steps of normalization, grouping and weighting are applied to further increase the comparability of the LCA results. The normalization focuses e.g. on regions, grouping sorts categories according to similar impacts and weighting is applied to rate different categories and add them into one total indicator. (Koffler 2007, p. 20 f.) Further information on impact categories can be found in Herrmann 2010, p. 157 ff. Table 2.1 Selected environmental impact categories, indicators, and reference units (based on Stranddorf, Hoffmann & Schmidt 2005, p. 36) impact category

impact indicator

reference unit

climate change

global warming potential (GWP)

kilogram carbon dioxide equivalents [kg CO2 eq.]

stratospheric ozone depletion

ozone depletion potential (ODP)

kilogram trichlorofluoromethane equivalents [kg CFC-11 eq.]

summer smog formation photochemical ozone formation (PCOF)

kilogram ethylene equivalents [kg C2 H4 eq.]

acidification

kilogram sulfur dioxide equivalents [kg SO2 eq.]

acidification potential (AP)

Step 4: Interpretation The fourth step of an LCA is the interpretation of the results, which is supposed to assist the decision making regarding the analyzed product system. Based on the findings, alternatives or replacements can be assessed and the most ecologically

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2 Theoretical Background and Technical Overview

friendly configuration is identified. Furthermore, it is important to evaluate the LCA regarding completeness, sensitivity of critical parameters and consistency. (DIN ISO 14040, p. 22) Life cycle assessments for complex product systems are conducted by using specialized databases and software for the process modelling, inventory analysis and impact assessment. Examples and overviews of suited software and databases are found in Broch (2017), Kaltschmitt & Schebek (2015) and Kloepffer & Grahl (2009).

2.2.2

Life Cycle Costing

In addition to the environmental life cycle assessment, the life cycle of a product system including its financial performance has to be evaluated in order to be able to evaluate its eco-efficiency and/or eco-effectiveness. Having assessed a measure for the CO2 reduction of a product system regarding its environmental impact (by means of an LCA) and its financial impact, that measure can be ranked with regard to its CO2 reduction potential per spent budget. The economical evaluation is based on the same life cycle definition as the LCA in order to make sure that all relevant financial flows are covered and the comparability with the LCA results is sufficient (Rebitzer & Hunkeler 2003). In the field of cost accounting, there are many different approaches to evaluate the costs of a product. The concept of life cycle costing (LCC) is applied to assess a product regarding the economical expenses over the entire life cycle. The term of life cycle costing is used for many different forms of accounting the preliminary costs, the costs for the operation and the subsequent costs of a product in a holistic manner. Analyzing the costs of a product along its entire life cycle helps to assign the incurring costs and revenues to the different life cycle stages. (Hoch, Heupel & Kachel 2016, p. 331) A strict definition of LCC (compared to the DIN ISO norms 14040 and 14044 for LCA) does not exist. However, it is scientific consensus that the subsequent costs should be integrated and trade-offs between the life cycle stages due to cost substitution have to be considered (Ulmschneider 2004, p. 50). Rebitzer and Hunkeler (2003) define life cycle costing as follows: [Life cycle costing is the] “assessment of all costs associated with the life cycle of a product that are directly covered by any one or more of the actors in the product life cycle (supplier, producer, user/consumer, EOL-actor), with complimentary inclusion

2.2 Life Cycle Thinking

29

of externalities that are anticipated to be internalized in the decision-relevant future.” (Rebitzer & Hunkeler 2003, p. 254 f.)

Following Rebitzer and Hunkeler (2003), costs are understood as expenses within the operational service process of all goods within a defined period consisting of basic and imputed costs (Paul 2015, p. 204). Following Herrmann (2010), the evaluation of the costs along the entire life cycle of technical products is especially relevant if the share of the total costs within the use stage and/or end of life stage is relatively high compared to the acquisition costs. If the life cycle costing only contains the costs of the different life cycle stages, life cycle costing in the narrow sense is performed. The scope can be expanded if the revenues are also included in the calculations. (Herrmann 2010, p. 131 f.) Life cycle costing can be accounted from different points of view, which leads to different results if certain costs or revenues are either included or excluded. Two typical perspectives for an LCC are the manufacturer’s point of view and the customer’s point of view, each having a different focus on the product’s cash flow. If the target is to reduce the total cost of ownership (TCO) of a product, those two different cost perspectives lead to different approaches. This is due to the different financial hotspots or cost types that those perspectives have and that result in different concepts of cost optimization. (Ulmschneider 2004, p. 49 ff.) Evaluating the total cost of ownership of a product is not only relevant for customers that strive to identify the most cost-efficient alternative of different interchangeable products, but also for the manufacturers that produce these goods. Being able to offer a product with lower costs during the use stage, this technological advantage will be reflected in the selling price. A cost-reduced use stage is one possible way for companies to compensate detriments such as higher wage costs when producing in developed countries. (Cerri et al. 2014) Kloepffer and Ciroth (2011) state that “environmentally preferable products often have higher purchasing costs, whereas the LCC may be much lower (examples: energy saving light bulbs, low energy houses, and cars)” (Kloepffer & Ciroth 2011, p. 99). The aspect of covering multiple life cycle stages makes LCC a suitable tool for customers to compare different products. Given the customer behavior of comparing costs over the entire life cycle, the manufacturers should be interested in reducing the TCO of a product as far as possible as otherwise it will be less successful on the market compared to alternative products (Kloepffer & Ciroth 2011, p. 99). The results of life cycle costing can either be calculated in retrospective or in advance for a future product. Each result is influenced by many uncertainties

30

2 Theoretical Background and Technical Overview

that increase with a higher complexity of the product and with a longer lifetime (Scope, Ilg, Muench & Guenther 2016). Figure 2.12 shows a representation of life cycle costs by the German association of engineers (Verein Deutscher Ingenieure, VDI). In line with the definition of a product’s life cycle, the life cycle costs result in the addition of the individual costs of each life cycle stage. Following the definition of the VDI, the life cycle costs are calculated by equation 2.1: Li f ecyclecosts = Costs Pr oduction + CostsU se + Costs Endo f Li f e

(2.1)

The costs of the different life cycle stages each consist of various cost types like development and manufacturing costs (production stage) or operating and maintenance costs (use stage). As figure 2.12 depicts, these costs are paid by different cost owners. While the costs of the production stage are paid by the manufacturer, the costs of the use stage are paid by the operator or customer. The shift of the financial responsibility is marked by the transfer of the ownership of the product from the manufacturer to the customer including a profit for the manufacturer. (VDI 2005, p. 5)

Figure 2.12 Life cycle costing by the VDI (VDI 2005, p. 5)

2.2 Life Cycle Thinking

31

total costs

variable costs fixed costs

total costs [€]

Costs that occur along the life cycle of a product are either classified as fixed or variable costs. Fixed costs are expenses that do not change when an influencing variable is altered while the variable costs are affected by such a change. (Plinke, Rese & Utzig 2015, p. 29) An example of variable costs are the costs for the input material that is needed to produce goods. The demand for the product then denotes the influencing variable. As a result of a rising demand and a constant price for a fixed unit of the input material, the total expenses for input material will increase (as shown in figure 2.13).

product demand [quantity] Figure 2.13 Fixed and variable costs (based on Plinke et al. 2015, p. 30)

Another form of cost categorization is the differentiation into direct costs and overheads. While fixed and variable costs are separated by their reaction to an influencing parameter, direct costs and overheads can be distinguished by cause. Direct costs are costs that are directly assigned to one produced unit of goods while the overheads cannot be linked to the explicit production of just one product. As overheads are caused by more than one unit of goods, they are usually evenly distributed among all produced goods. (Plinke et al. 2015, p. 36) Costs of the production stage The costs of a product that occur during its production stage are split into the sub-stages of product development, production, and distribution. Adding up these cost categories leads to the prime costs of the product. Together with the profit margin, these costs constitute the acquisition costs for the customer. Usually, the development costs are overheads and not explicitly linked to the development of one product unit. The production costs are defined by the direct material costs,

32

2 Theoretical Background and Technical Overview

acquisition costs of vendor parts and a material overhead as well as the manufacturing direct costs and the manufacturing overhead. While the material costs include the expenses for raw material, the material overhead includes expenses for inhouse logistics. The manufacturing costs consist of the personnel costs and costs for e.g. energy, electricity and maintenance that are required to keep the production running. Those costs typically have a share of direct costs and overhead costs. The costs for distribution account for e.g. warehousing or transport. (Nickenig 2016; Rudorfer 2017; Schlink 2014) The costs of the production stage are summarized based on Nickenig (2016), Rudorfer (2017) and Schlink (2014) with equation 2.2, with CPP being the costs of the production stage, CDE the development costs, CPR the production costs and CDI the distribution costs CP P = CDE + CP R + CDI

(2.2)

Costs of the use stage The costs that incur during the use stage of the product consist of the operating costs, maintenance and service costs and possible additional taxes, contractual costs and insurance expenses. The operating costs are defined by the costs for the operating materials as well as lubricants or other additional consumable materials. The maintenance and service costs include e.g. spare parts, repair costs and service costs for regular inspections. (Herrmann 2010, p. 134) The costs of the use stage are summarized based on Herrmann (2010) and VDI (2005) with equation 2.3, where CUP represents the costs of the use stage, COP the operating costs, CMS the costs for maintenance & service and CTI the taxes and insurance costs. CU P = C O P + C M S + C T I

(2.3)

Costs of the end of life stage The costs of the end of life stage (CEP ) of a product usually consist of the incurring costs for dismantling (CDM ), recycling (CRE ) and disposal (CDI ) (see equation 2.4) (Herrmann 2010, p. 134; VDI 2005). CE P = CDM + CRE + CDP

(2.4)

Figure 2.14 depicts the life cycle stages of a product system (a vehicle in this case) and the corresponding costs that occur in each stage for the example of a vehicle as a product system.

manufacturer costs

product life cycle

customer costs

2.3 Sustainable Development and Corresponding Legislation

• operating costs

• contractual costs

• maintenance costs

• tax costs

• service costs

• insurance costs

production

use phase

33

recycling

• development costs

• production costs

• dismantling costs

• material costs

• personnel costs

• recycling costs

• vendor part costs

• distribution costs

• disposal costs

Figure 2.14 Life cycle costing—stages and costs (based on Herrmann 2010, p. 134)

2.3

Sustainable Development and Corresponding Legislation

Due to a growing need for energy and resources, humans influence their environment (see section 1.1). Production and consumption of products and services cause emissions and waste streams which affect the environment and have an impact on the local and global eco systems. Environmental targets and regulations are derived to avoid or reduce the negative impacts on the environment. The following subchapters describe relevant environmental impacts, the need for sustainable development and corresponding legislation for environmental protection. A special focus lies on the regulation for the automotive industry to comply with global environmental targets.

2.3.1

Environmental Impacts and Impact Assessment

The earth’s environment is a complex system of atmosphere, hydrosphere and lithosphere and the relations of those with all living and non-living organisms (Günther 2018). It is impacted by the extensive use of natural resources and energy through human activities (Herrmann 2010, p. 13). The growing population and the growing demand for energy and resources induce a rapid growth of the economic output, but also a growing impact of those actions on the environment and its natural resources (Kaltschmitt & Schebek 2015, p. 1 f.). These

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2 Theoretical Background and Technical Overview

natural resources are defined as renewable and non-renewable resources, physical space, environmental media such as air, water, soil, flowing resources like geothermal energy, wind, waves, solar radiation, and biodiversity. The utilization of these resources occurs either due to extraction of these resources (environmental source) or due to absorption of emissions and wastes into these resources (environmental sink), in both ways leading to an environmental impact. (Umweltbundesamt 2016) The anthropogenic impact on the environment is understood as measurable changes in the ecosphere, which are considered either positive or negative from the human perspective. These environmental impacts comprise technical, economical, socio-cultural, and ecological changes. (Kaltschmitt & Schebek 2015, p. 15 f.) The chain of effects from the cause of the impacts to the final responses consists of the drivers, pressures, states, impacts and responses of an environmental impact. Figure 2.15 depicts this chain of effects. The drivers are the fundamental cause for the chain of effects. Typical examples for such drivers are the growing global population or the growing demand for energy. The following environmental pressures are the actual and potential interferences with the natural environment that are triggered by the drivers and are distinguished between indirect (e.g. emissions from burning fuels) and direct (e.g. deforestation) impacts.

drivers • growth of global population • demand for energy

pressures • deforestation • emissions of substances

states • global average temperature • number of species

impacts

responses

• melting of glaciers • extinction of species

• cleaning of exhaust gases • renewable energy

Figure 2.15 Chain of effects for environmental impacts (based on Smeets & Weterings 1999, p. 6 ff.)

The states of the environment then describe, based on scientific parameters, the condition of the natural environment for a fixed point in time. Possible key figures that express the environmental state are the global average temperature or the number of species. The impacts (either short-term or long-term) describe the actual effects of the environmental pressures, such as the melting of glaciers or the extinction of species. The last link in the chain of effects for environmental impacts are the responses, which interact with all preceding steps, leading to interactions. This phase describes the human reaction to the previous environmental impacts and includes (if the impact is considered negative) e.g. cleaning of

2.3 Sustainable Development and Corresponding Legislation

35

exhaust gases or the utilization of renewable energy sources. (Smeets & Weterings 1999, p. 6 ff.) While the human influences on the environment (the pressures) can exactly be quantified, the resulting consequences (the environmental impacts) cannot. These impacts are usually complex and often influence the environment on a global scale—in long-term perspective and in multiple environmental categories. Hence it is complicated to measure, quantify, and predict these impacts precisely. (Kaltschmitt & Schebek 2015, p. 4 f.) The life cycle impact assessment (LCIA) midpoint-damage framework of the UNEP/SETAC life cycle initiative shows that the human caused environmental pressures lead to environmental midpoint impacts. These midpoint impacts e.g. include human and eco-toxicity, eutrophication, land use, (a-)biotic resource depletion, climate change, acidification, or ozone depletion. These effects in return affect e.g. the human health, the (a-)biotic resources, the (a-)biotic natural environment and the (a-)biotic man-made environment, as figure 2.16 depicts. (Jolliet et al. 2004, p. 395)

inventory results

midpoint

endpoint

area of protection

climate change stratospheric ozone depletion

human health

elementary flows

human toxicity particulate matter formation photochemical ozone formation

natural environment

ecotoxicity acidification eutrophication land use water use abiotic resource use

natural resources

Figure 2.16 Life cycle impact assessment midpoint framework (Hauschild & Huijbregts 2015, p. 9)

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2 Theoretical Background and Technical Overview

An important question when dealing with environmental impacts is whether the impacts can be categorized as a positive impact on the environment or a negative impact that causes an environmental problem. An environmental impact constitutes an environmental problem if e.g. the time period, spatial effect, urgency and irreversibility of the damages that occur change the environment compared to the previous state in an undesired way judging from the human perspective (Kaltschmitt & Schebek 2015, p. 17). The GHG emissions are known to be a major factor for the anthropogenic climate change. Their primary effect is the radiation absorption by the molecules in the atmosphere and are indicated as carbon dioxide equivalents (CO2 eq.) (Kloepffer & Grahl 2009, p. 195 ff. and 224). Accounting for roughly 76 % of the global anthropogenic carbon dioxide equivalents, carbon dioxide is the most important greenhouse gas. Consequently, carbon dioxide has the biggest impact on the anthropogenic climate change. It is followed by methane (16 %) and nitrous oxide (6.2 %) (IPCC 2014, p. 6). For Germany in 2018, approx. 78.7 % of the methane emissions and 81.6 % of the nitrous oxides emissions are emitted by the agricultural sector and waste disposal, while 93.2 % of the carbon dioxide emissions are energy related emissions coming from industry processes (see figure 2.17) (Umweltbundesamt 2019a). One reason for the high emission level of carbon dioxides is the high share of fossil-based energy carriers like coal, oil, and gas with 81.0 % (in 2014) for the provision of the world’s primary energy demand (IEA 2016, p. 64).

2.3.2

Sustainability and Sustainable Development

Environmental targets can be classified within the concept of sustainable development. The term “sustainable development” was defined in the Brundtland Report “Our Common Future” by the World Commission on Environment and Development (WCED) in 1987 as follows: “Sustainable development is development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” (WCED 1987, p. 41)

At the United Nations Conference on Environment and Development in 1992 in Rio de Janeiro, the United Nations (UN) published the “Rio Declaration on Environment and Development” wherein it is stated that “Human beings are at the centre of concerns for sustainable development. They are entitled to a healthy

2.3 Sustainable Development and Corresponding Legislation

37

Share of agriculture in Germany: 81.6%

Share of agriculture and waste disposal in Germany: 78.7%

6% 2% Share of energy in Germany : 93.2%

16%

76%

Carbon dioxide

Methane

Nitrous oxid

F-Gases

Other

Figure 2.17 Shares of greenhouse gases and main causes (based on IPCC 2014, p. 6 & Umweltbundesamt 2019a)

and productive life in harmony with nature.” (United Nations 1992, p. 1) and “the right to development must be fulfilled so as to equitably meet developmental and environmental needs of present and future generations.” (United Nations 1992, p. 2). This underlines the concept of the Brundtland definition. In 1998, the Enquete Commission of the German parliament published their final report on the concept of sustainability for the protection of humans and the environment defining the concept of the “triple bottom line” as the simultaneous integration of ecological, economic, and social goals for a successful sustainable development (Deutscher Bundestag 1998). Only if all three dimensions are sufficiently fulfilled at the same time, sustainable development can be achieved (see figure 2.18). One of the biggest initiatives regarding the persecution of sustainable development are the “Sustainable Development Goals” of the UN. In 2015, the UN introduced 17 different goals for a sustainable development of the Earth’s society until 2030, aiming at more secure and prosper living conditions. With goals number 12 (“responsible production and consumption”) and number 13 (“climate action”), two goals are directly targeted at environmental protection and a sustainable product development. (United Nations 2015b)

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2 Theoretical Background and Technical Overview

Figure 2.18 Depiction of the triple bottom line (based on Elkington 2018)

People Social development

Sustainability

Planet

Profit

Environmental development

Economic development

Eco-efficiency and eco-effectiveness To derive solutions with an optimized effectiveness, the term “effectiveness” needs to be distinguished from the term “efficiency” as both terms describe the success of a measure regarding a desired target. “Efficiency” brings the dimensions of input and output into relation and defines the productiveness of the input. “Effectiveness” on the other hand describes the degree of target attainment by expressing the ratio of the actual state and the desired state. (Eichhorn & Merk 2016, p. 183; Hauschild 2015) While efficiency is a relative expression, effectiveness is an absolute expression of e.g. improvement measures. The term “eco-efficiency” has been coined by the World Business Council for Sustainable Development (WBCSD) during the “Earth Summit” of 1992 in Rio de Janeiro, Brazil, and is defined as follows: “Eco-efficiency is achieved by the delivery of competitively priced goods and services that satisfy human needs and bring quality of life, while progressively reducing ecological impacts and resource intensity throughout the life-cycle to a level at least in line with the Earth’s estimated carrying capacity.” (WBCSD 1995, p. 4)

Eco-efficiency is often defined as the ratio of economic value and environmental impact (based on Schaltegger & Sturm 1990, p. 281 ff.): EcoE f f iciency =

Economicvalue Envir onmentalimpact

(2.5)

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However, building on the definition of the term “productivity”, which expresses the ratio of output and input, the definition of equation 2.5 must be put into the perspective of efficiency as a measure for improvement. Therefore, to understand whether the eco-efficiency of a product is sufficiently high, it must be compared to the eco-efficiency of a reference product as defined in the following equation: Evaluating EcoE f f iciency =

EcoE f f iciencyactual EcoE f f iciencyr e f er ence

(2.6)

To improve the eco-efficiency, either the economic value must be increased for the same environmental impact or the environmental impact for a constant economic value needs to be reduced (see equation 2.5). Measures for environmental protection become relevant for profit-oriented companies in two different ways. Either if it adds a value to the company’s business and increases its productiveness or if self-set or legislative environmental limits must be met. Since this shall be done with the highest cost-efficiency, the eco-efficiency plays a key role for companies that follow environmental goals. (Schrack 2016, p. 34 f.) The term “eco-efficiency” is used in the context of a relative improvement of sustainability. In comparison to eco-efficiency, the term “eco-effectiveness” is defined by Jakobsen (1999) as: “[…] the total impact on environment when the consumers need or demand is satisfied by alternative fulfilment of the function in question. One way of fulfilling a function is said to be more eco-effective than by fulfilling it in a different way, if it gives a larger contribution to sustainability of the eco-system in question than the second way of fulfilling the function.” (Jakobsen 1999)

Additionally, Braungart, McDonough and Bollinger (2007) state that “the concept of eco-effectiveness proposes the transformation of products and their associated material flows such that they form a supportive relationship with ecological systems and future economic growth. The goal is not to minimize the cradle-to-grave flow of materials, but to generate cyclical, cradle-to-cradle “metabolisms” that enable materials to maintain their status as resources and accumulate intelligence over time (upcycling).” (Braungart, McDonough & Bollinger 2007). Figure 2.19 visualizes the differences between the terms eco-efficiency and eco-effectiveness. While eco-efficiency strives to reduce the negative impact for the ecological system, eco-effectiveness expresses the positive impact. While the eco-efficiency improves the relative environmental impact of a product, it is not guaranteed that a product with an increased eco-efficiency is a sustainable product. The “rebound effect” causes a situation where the efficiency

2 Theoretical Background and Technical Overview

Eco-effectiveness optimizing positive impact

time

Eco-efficiency reducing negative impact

-

Impact to ecological system

+

40

Figure 2.19 Eco-efficiency vs. eco-effectiveness (based on Braungart, McDonough & Bollinger 2007; Koeijer, Wever & Henseler 2017)

gains are compensated by a more extensive use of the improved product which equalizes its relative improvements. In such cases, eco-efficiency does not contribute enough to sustainable development. And even without the rebound effect, an increased eco-efficiency does not necessarily yield a sustainable product as the product can still have an environmental impact which is too high to be sustained over time. (Hauschild 2015) While the eco-efficiency only expresses the relative improvement of the environmental impact of a product, the eco-effectiveness of a product describes its ability to contribute to an absolute sustainability, considering a possible rebound effect due to a higher utilization or a growing consumer group. (Hauschild 2015; Hauschild, Herrmann & Kara 2017; Kara, Hauschild, Herrmann 2018) An approach to quantify the absolute sustainability and to define a “safe operating space for humanity” is described by the concept of “planetary boundaries” by Johan Rockström et al. (2009). A total of nine different environmental categories (e.g. freshwater use or biosphere integrity) are analyzed and individual limits for a safe global operating space defined. Surpassing the limits of these safe operating spaces implies that the Earth has left the controllable state for human activities. According to Rockström et al. (2009), this safe operating space has already been exceeded for the category of climate change. (Rockström et al. 2009)

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While the definition of sustainability by the triple bottom line (see figure 2.18) brings the three aspects of “people”, “Earth” and “profit” together, eco-efficiency only describes the relation between “Earth” and “profit”. Thus, the aspect of social development is not included in this perspective. In contrast to sustainability, eco-efficiency only focuses on the economic value, the factor of social sustainability is not quantified and not considered in the concept. Another difference between sustainability and eco-efficiency is that for sustainability, all three dimensions shall be optimized or increased. For eco-efficiency, the selected dimensions “Earth” and “profit” are related by determining the ratio of both aspects. The triple bottom line on the other hand does not define such a qualitative relation between its dimensions.

2.3.3

General Environmental Legislation

Environmental targets are set when an environmental problem is detected and has to be diminished or if the path of a sustainable development is followed. An environmental target is defined as a limit for an environmental burden (e.g. emission reduction targets) or a vision for an environmental state that is strived for. Such environmental targets are the foundation for legislative activities which are a compromise between environmental goals and socio-economic targets. Environmental goals can either be defined for specific elements (e.g. limitation of CO2 emissions) or spatial regions, but can also be expressed more generally (e.g. limiting the rise of the global mean temperature). (Feess 2018) One possible way to reach such an environmental goal is the production and consumption of eco-effective products and services. Figure 2.20 shows an example of a possible definition of an environmental target. Following the concept of Rockström et al. (2009) and starting with the goal of preserving the safe operating space of the Earth, it is important to limit climate change to a maximum of 2° C. This leads to the need of reducing GHG emissions and finally results in a limitation of CO2 emissions. After environmental targets have been defined, general guidelines and principles follow in order to set effective regulations. The Enquete Commission of the German parliament defined five guiding rules for material flows to comply with the given goals for a sustainable development. These five rules can be seen as guidelines on how to reach a sustainable development: (Deutscher Bundestag 1998, p. 25)

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2 Theoretical Background and Technical Overview

preservation of Earth`s safe operating space Defining an

limiting climate change to max. 2 °C

reducing greenhouse gas emissions

cutting CO2 emissions to a minimum

environmental target

Figure 2.20 Defining an environmental target

• The degradation rate of renewable resources shall not be higher than their recovery rate, • Non-renewable resources shall only be utilized in a way that either an equivalent form of renewable resources is created or the productivity of (non-) renewable resources is increased, • Material flows into the environment shall be scaled to the absorption capacity of the environment, • Material flows into the environment shall only occur in a suitable period for the environment to react, • Threats and unacceptable risks for human health by anthropogenic causes shall be avoided. Damages already caused to the environment shall be removed following the “polluter pays principle”. This principle implies that the environmental damages shall be removed by those organizations that are responsible for causing them. Following the “precautionary principle”, generally known and proven hazards to the environment shall be avoided in advance, just as well as potential threats to the environment (Herrmann 2010, p. 49 f.). For economically oriented companies, the “efficiency strategy” is the most suitable approach to reduce environmental burdens. The efficiency strategy assumes that natural resources and the environment are scarce input factors whose consumption needs to be reduced over time (Schrack 2016, p. 2). Measures that help to reach environmental targets e.g. include an increased energy efficiency of products or factories, applying renewable energy forms or energy carriers with a lower environmental impact and the utilization of secondary materials coming from recycling streams (IPCC 2014). Many legislative approaches to operationalize environmental targets are implemented in national or multi-national laws. A prominent example of a global-wide cooperation for climate protection is the Paris Agreement of 2015. In this agreement, 195 signatories have agreed to “holding the increase in the global average

2.3 Sustainable Development and Corresponding Legislation

43

temperature to well below 2° C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5° C above pre-industrial levels” (United Nations 2015a, p. 3). In the EU, the “seventh environment action program” describes the ambition to reduce the greenhouse gas emissions by 20 % and to increase the share of renewable energies as well as the energy efficiency by 20 % by 2020 (European Parliament 2013, p. 2). Until 2030, the targets are tightened to a reduction of at least 55 % regarding GHG emissions compared to 1990. (European Commission 2020c) This target is also implemented in the European Union Emissions Trading System (EU ETS), which regulates the GHG emissions of several industries since 2005 following the “cap and trade” approach. Within the EU ETS, a limited amount of emission certificates is made available for each participating company, which in turn can trade these emission rights on a marketplace. The EU ETS is decreasing the allowed GHG emissions in general by reducing the amount of emission certificates that enter the market every year. This leads to a situation where the emissions are reduced by those companies that achieve them in the most cost-efficient way (European Commission 2015). In 2019, the European Commission introduced their plans for a “European Green Deal” that sets out the vision for a decarbonized Europe by 2050. An intermediate target for 2030 is defined including a reduction of GHG emissions by 55 % compared to 1990. (European Commission 2019) On a national level, the German constitution e.g. proclaims in article 20a that the state of Germany must protect the natural environment and the basis of all life to meet the responsibility it has for its current and future inhabitants (MoJ 2020d). The German “Bundes-Klimaschutzgesetz” from 2019 sets the reduction of GHG emissions to at least 65 % by 2030 and therefore follows the targets of the European Commission. The German climate action plan 2050 sets the target to meet the Paris Agreement by reducing the GHG emissions by up to 95 % by 2050. (FME 2016)

2.3.4

Environmental Regulations for the Automotive Industry

The methodology of this thesis is developed as an example of an application in the automotive industry. This industry is chosen as the transport sector is responsible for a high share of the total CO2 emissions as shown in section 2.3. In Germany, approx. 17 % of the GHG emissions are caused by transports (Umweltbundesamt 2015). Due to the high environmental impact of the automotive industry, many aspects of a vehicle are subjected to regulations. These regulations aim to control,

44

2 Theoretical Background and Technical Overview

limit, and reduce the emissions of the automotive industry over time. In this subchapter, an overview of the worldwide regulations regarding the emissions from a vehicle is given. Along the major stages of the life cycle of an automobile (production stage, use stage and end of life stage), different environmental impacts occur that lead to regulations for each life cycle stage. Regulations for the production stage Regarding the production of a vehicle or its sub-parts, no laws exist that define limitations of environmental pollutants that directly address the automotive industry. Instead, the factories and production sites of the OEM or their suppliers are bound to the general environmental laws and regulations of the region they are located at. For example, in Germany such regulations include: • Abwasserabgabengesetz (AbwAG): Regulation regarding the discharging of waste waters into waters (MoJ 2020a) • Bundes-Immissionsschutzgesetz (BimSchG): Federal act for pollution control regarding harmful effects on the environment caused by air pollution, noise, vibration, and similar phenomena (MoJ 2020b) • Chemikaliengesetz (ChemG): Federal act on the protection against hazardous substances, defining which materials are classified as hazardous and how these materials must be treated (MoJ 2020c) Factories in Germany always have to comply with those kinds of environmental regulations to maintain a secure production of e.g. a paint shop or a vehicle assembly. However, these regulations are subject to all production sites in Germany and do not include specific targets for the automotive industry. On top of these regulations, German OEMs have set up individual campaigns to further reduce their environmental impact by reducing e.g. the amount of fresh water used, the primary energy demand, or the amount of waste per produced vehicle. (VDA 2014) As an example, the Volkswagen AG targets to reduce the environmental impact of its vehicle production by 45 % by 2025 compared to 2010. This reduction involves greenhouse gas emissions, use of fresh water, waste production and energy demand. (Volkswagen AG 2017) Regulations for the use stage As the use stage of a vehicle causes emissions that impact the environment as well as the human health, numerous regulations exist that control the emissions of a vehicle during its operation. The legislation in this field is divided into two

2.3 Sustainable Development and Corresponding Legislation

45

different focus areas: (1) regulation of air quality by limiting air pollutants in exhaust gases and (2) regulation of greenhouse gases emitted by vehicles. The legislation regarding air quality limits the emission of air pollutants per driven kilometer. These limits must not be exceeded by any vehicle. The European Union for example restricts the emission of seven different components of exhaust gases. The following table shows an example of the current limitation values for those substances for diesel and gasoline fueled vehicles under the newest Euro Standard 6. (European Parliament 2007) (Table 2.2) Table 2.2 Limitation values for air pollutants set by Euro Standard 6 (European Parliament 2007) Substance

Gasoline

Diesel

Carbon monoxide (CO)

1000 mg km 100 mg km 68 mg km 60 mg km

500

Total hydrocarbons (HC) Non-methane hydrocarbons (NMHC) Oxides of nitrogen (NOx ) Combined mass of total hydrocarbons and oxides of nitrogen (HC + NOx )

–

Mass of particular matter (PM)

mg km 6 ∗ 1011 number km

Number of particular matter (PN)

4.5

mg km

– – mg km 170 mg km

80

mg km 6 ∗ 1011 number km

4.5

As these substances do not have a relevant impact on climate change, the regulation of air pollutants is not further considered in this thesis. Regulations for the end of life stage The end of life legislation is different for each region and market around the world. In Europe for example, the directive 2000/53/EG of the European Parliament regulates the end of life treatment of vehicles. Its goal is to harmonize the country specific processes of the EU members to increase the reuse and recycling rate of returning vehicles. This directive determines the framing conditions regarding (1) the prevention of waste, (2) the collection of end of life vehicles, (3) the treatment of end of life vehicles, (4) the targets for reuse and recovery quotes, and (5) the coding standards for dismantling information. Since 1st January 2015, at least 95 % of the vehicle (regarding mass) must be reused or recycled. Furthermore, a bigger focus on design for recycling is demanded. (European Parliament 2000)

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2 Theoretical Background and Technical Overview

Regulations regarding greenhouse gas emissions from vehicles In order to make the automotive industry contribute to the decarbonization, many countries and regions have added stricter regulations regarding the emission of GHG. While the target of reducing GHG emissions is the same for every region, the approach and the target values are different. As shown in figure 2.21, over three quarters of all CO2 emissions are caused by the energy, transport and manufacturing industries. In the European Union, the transport sector is responsible for almost 30 % of those emissions. 72 % of them are caused by road-based transportation. Figure 2.21 also shows how the transportation-related emissions are distributed among the different transportation modes. Most of those emissions are caused by passenger cars. Together with the light duty vehicles, the passenger cars cause 72.6 % of the road-based transport emissions. (European Parliament 2019) In the largest automotive markets (European Union, United States of America [USA], and China) the greenhouse gas legislation for vehicles is divided into (1) regulations for passenger cars plus light duty vehicles and (2) regulations for heavy duty vehicles. The regulations between those classes differ regarding the limit values, time frames and calculation methods. The following sections give an overview of the greenhouse gas legislation for passenger cars in the three named markets.

13.4%

Within this group • Passenger cars: 60.7% • Light duty vehicles: 11.9% • Heavy duty vehicles: 26.2% • Motorcycles: 1.2%

1.0%

13.6%

72.0%

Road transportaon

Water navigaon

Civil aviaon

Other

Figure 2.21 Transport-related CO2 emissions in the European Union (based on: European Parliament 2020)

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47

Greenhouse gas legislation in the European Union In contrast to the regulation of air pollutants, the regulation of greenhouse gas emissions does not apply to every single vehicle. Instead, for each OEM, a fleet wide average is calculated and set as a limit value. This limit value is defined as grams of CO2 per driven kilometer in the tailpipe emissions. The regulations 443/2009 and 333/2014 of the European Parliament limit the greenhouse gas emissions to 95 gCkmO2 by 2021, which applies as the allowed average emission level of an OEM’s fleet. (European Parliament 2009, European Parliament 2014) l Converted to fuel consumption, this limit value corresponds to approx. 4.1 100km l of gasoline or 3.6 100km of diesel. In concession to different types and classes of vehicles, the average weight of an OEM’s fleet is taken into account for the calculation of the emission limitation. Compared to the overall average of vehicles in the market, an OEM can have a higher emission level if the vehicles are heavier than the average. An additional weight of 100 kg leads to an allowed excess of the emission limit by 3.33 gCkmO2 . . (Transportpolicy 2020a) These values are measured for each of the OEM’s vehicles by test cycles to guarantee comparable results. In Europe, the New European Driving Cycle (NEDC) and the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) are used. While the current values are measured by the NEDC, the WLTP will replace the NEDC over time. (European Parliament 2009, European Parliament 2014) Both test cycles consist of several sequences of defined velocities over a defined time. While the NEDC is 1,180 seconds long, the WLTP lasts for 1,800 seconds. During these 1,800 seconds, the NEDC covers 10.97 km and the WLTP 23.27 km. The WLTP has a higher mean and maximum velocity with 47 km/h to 34 km/h and 131 km/h to 120 km/h. (Volkswagen AG 2020) Figure 2.22 shows a comparison of the NEDC and WLTP regarding the velocity over time. If an OEM reduces the CO2 emissions by introducing an “eco-innovation”, credits regarding the emission limit are applied, even if the eco-innovation is not effective in the test cycle. Such credits can sum up to 7 gCkmO2 for each OEM. (Transportpolicy 2020a) This incentive is introduced to increase the innovation regarding emission reduction technologies. To further increase the introduction of more environmentally friendly vehicles, “super credits” for zero or low emission vehicles (ZLEV) are accounted with a maximum of 7.5 gCkmO2 for each OEM. A ZLEV is defined as a vehicle with an emission level of less than 50 gCkmO2 . . In 2020, a ZLEV is counted as two vehicles, in 2021 as 1.67 vehicles and in 2022 as 1.33 vehicles. (European Commission 2020a) If the defined emission limit (95 gCkmO2 under consideration of weight impact and eco-innovations) is exceeded

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2 Theoretical Background and Technical Overview

Figure 2.22 Comparison of NEDC and WLTP (Volkswagen AG 2020)

by an OEM, a penalty has to be paid. This penalty amounts to e95 per exceeded gram of CO2 and per sold vehicle. (Transportpolicy 2020a) In the regulation 2019/631 by the European Parliament, the emission goals for the time after 2020 are defined. For 2025, a further reduction of the emission limit by 15 % is defined, for 2030 a reduction by 37.5 %. (European Parliament 2019) For ZLEVs, a new credit system is applied, starting in 2025. This credit system is activated when the share of ZLEVs of all newly registered vehicles of an OEM in the given year is above a threshold value. In 2025, this value is at 15 %, from 2030 on at 35 %. For every percentage point an OEM exceeds those threshold values, the specific CO2 emission target will also be increased by one percent with a maximum of up to 5 %. An even greater weighting applies when ZLEVs are introduced in markets with a slow growth in the ZLEV segment. (European Commission 2020b) In a further outlook, the European Commission will collect and evaluate data on real driving emissions from cars using the on-board fuel consumption monitoring devices. If required, further steps shall be defined to adjust the OEM’s emission targets. The expansion of the emission reporting from tailpipe emissions

2.3 Sustainable Development and Corresponding Legislation

49

to full life cycle reporting is also discussed. By 2023, the European Commission is supposed to assess a methodology that is used to collect and report data regarding the CO2 emissions along the entire life cycle of a vehicle. (European Commission 2020b) This is a first step into the direction of setting regulations not only to the emissions of exhaust gases but to the whole environmental footprint of a vehicle product lifetime. Further information about the CO2 standards for new vehicles in Europe is found in ICCT 2017. Greenhouse gas legislation in China In China, the emissions of greenhouse gases are not regulated by the emissions per driven kilometer but by the fuel consumption efficiency of a vehicle. The emission standards are defined by the ministry of industry and information techl nology. Since 2016, phase IV is active, which sets the standard to 5 100km for 2020. This level of fuel efficiency is applied to vehicles with a total mass below 3,500 kg. In total, 16 different classes of vehicle weight are defined which all have individual efficiency targets. Corresponding to the European legislation, this is done to consider the impact of different vehicle classes. While in Europe the emission limit is based on a fleet average, the weight-oriented fuel efficiency in China must be met by each vehicle individually. A fleet average in China is defined in a metric called “Corporate Average Fuel Consumption” (CAFC). OEMs must comply with both the individual mass-oriented limit and l the CAFC-oriented efficiency, which is currently set to 5 100km , which converts gC O2 to 117 km . . The basis for the fuel efficiency calculation is the NEDC test cycle. (Transportpolicy 2020b) Like the European Union, China also has implemented incentives for innovative powertrain technologies and eco-innovations. Vehicles with an electric range of more 50 km are accounted as five vehicles into the CAFC. Vehicles with a l combined fuel efficiency of below 2.8 100km are counted as three vehicles. Ecol . . It efficiencies with an off-cycle effect are credited to a maximum of 0.5 100km is also possible for OEMs to transfer a surplus of fuel efficiency from the present into a following year if the current emission level is below the specific limit. (Transportpolicy 2020b) For 2025, a further decrease to a fuel efficiency of only l 4 100km or 93 gCkmO2 is enacted. (ICCT 2020) Greenhouse gas legislation in the United States of America The regulation of greenhouse gas emissions of vehicles is the responsibility of the Environmental Protection Agency (EPA) and the US department of transportation. Beyond the nationwide rules, the state of California has its own limits

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2 Theoretical Background and Technical Overview

based on the regulation of the California Air Resources Board (CARB). The limits are defined as emission limitation and fuel efficiency standards and are applied to vehicles with a mass below 4,536 kg. They are measured by the Federal Test Procedure 75 (FTP-75) and weighted with the highway cycle. In contrast to the legislation in Europe and China, the US regulations do not take the vehicle weight into account, but instead vary the detailed emission limits with regard to vehicle size. By calculating the vehicle’s footprint, individual limits are set. (Transportpolicy 2020c) O2 For 2025, a target value of 163 gC mile is defined. This is a reduction of the mi previous limit by 35 % and corresponds to a fuel efficiency of 54.5 gallon or l 4.3 100km or to an emission level of 107 gCkmO2 . Since the US limit standard is designed for passenger vehicles and light duty trucks, a single limitation for passenger cars results in a limit of 91 gCkmO2 in 2025 when transferred to the NEDC. (Transportpolicy 2020c) In the state of California, the greenhouse gas O2 emission limits are lower than for the rest of the USA with limits of 183 gC mile gC O2 in 2020 and 144 mile in 2025 (Transportpolicy 2020d). Like Europe and China, the USA also has a credit system for eco-innovations that are effective off-cycle:

“Emission reduction compliance credits include air-conditioning system technology, flexible fuel vehicle deployment, off-cycle technologies, incentives for electric vehicles, and “game-changing” technologies installed on pickup trucks. Of these credits, only the air-conditioning credits and some off-cycle technology credits reflect real-world emission reductions that are not included on the compliance test cycles. The others reduce the overall stringency of the standards, without corresponding reductions in real-world emissions.” (Transportpolicy 2020c)

Figure 2.23 shows a summary of worldwide CO2 emission legislations for vehicles, normalized to the NEDC driving cycle of the European Union. In this figure, the emission regulations of California are equivalent to the level in China. It becomes visible that the European Union has, as of today, set the strictest emission regulations. The European Union is also the region that has set regulations for the longest time span into the future with targets for 2030. China and USA have set limit values only up to 2025.

2.4 Life Cycle Engineering and Mathematical Optimization

51

Figure 2.23 Comparison of CO2 emission limitations worldwide (ICCT 2020)

2.4

Life Cycle Engineering and Mathematical Optimization

The tools of life cycle engineering and mathematical optimization can be used to help developers and decision makers to design sustainable and eco-efficient product systems and product system fleets. In this way, it is possible to transfer the requirements of environmental targets of legislations and regulations into the development of new product systems. The following sections explain the fundamentals of LCE and mathematical optimization.

2.4.1

Life Cycle Engineering

In order to be able to optimize a product system in terms of its performance along the entire life cycle, the product needs to be designed in a life cycle oriented scope already during its development phase. LCE is an established concept to incorporate environmental targets into the product development phase. Being aware of the entire life cycle of a product and its defining phases during the process of decision making is an essential part for a holistic reduction of environmental impacts. This approach is also called “life cycle thinking”. (Koffler

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2 Theoretical Background and Technical Overview

2007, p. 6) Due to the product design paradox (see figure 2.24), it becomes obvious that the degree of freedom for changes to the product system is the highest during the early stages of the product development.

100%

Product Knowledge

Modification Cost

Freedom of action 0% Timescale Figure 2.24 The product design paradox (O’Reilly et al. 2016, p. 751)

As more time during the product development passes, this degree of freedom decreases, while the costs for changes to the product system increase. This leads to the situation that decisions that influence important parts or functionalities of a product need to be made as early as possible. Unfortunately, this opposes the knowledge about the product system, as the product knowledge increases over time. Important and influencing decisions for the product system often need to be made with relatively little product information, enhancing the importance of the life cycle perspective and LCE in general. As a consequence, the developed methodology in this study is meant to help decision makers during the early phases of product planning and product development to make decisions that lead to reduced environmental burdens over life cycles, when the costs for changes are still relatively low. The concept of Life Cycle Management (LCM) can be selected to apply this principle to a product system. In the background report for the United Nations Environment Programme (UNEP) guide to Life Cycle Management in 2006, LCM is defined as follows:

2.4 Life Cycle Engineering and Mathematical Optimization

53

„LCM is the application of life cycle thinking to modern business practice, with the aim to manage the total life cycle of an organization’s products and services towards more sustainable consumption and production. LCM is about systematic integration of product sustainability e.g. in company strategy and planning, product design and development, purchasing decisions and communication programs.” (Jensen & Remmen 2006, p. 10)

Along the concept of LCM, the principle of LCE is a framework for many approaches that cover the field of the optimization of eco-efficiency or ecoeffectiveness. LCE comprises engineering approaches that are meant to operationalize the life cycle perspective in a way that decision support for the development phase of a product can be provided (Alting 1995). LCE is applied to develop products that have a reduced environmental impact by minimized pollution and waste. Evaluation methods for the environmental impact are required as well as the integration of the life cycle perspective and engineering activities into the stage of product development to achieve this goal (Kaluza et al. 2017). Hauschild, Herrmann and Kara (2017) introduced an “Integrated Framework for Life Cycle Engineering” that integrates product development and manufacturing, meaning the earliest stages of a product’s life cycle, in the context of the planetary boundaries and the concept of absolute sustainability and eco-effectiveness. This framework (shown in figure 2.25) is positioned in the dimensions of temporal concern and environmental concern. Based on a top-down approach, LCE is described with a scope of multiple products and their entire life cycle stages. All main aspects of these stages (development, raw material extraction, production, after-sales, reuse/remanufacturing/recycling) are covered. (Hauschild, Herrmann & Kara 2017) In view of this new framework, LCE is defined as: “[…] sustainability-oriented product development activities within the scope of one to several product life cycles. The methods and tools used in LCE must support reducing the total environmental impact associated with technology change and volume increase from one product generation to another, in order to ensure that new product technologies stay within their environmental space as derived from the planetary boundaries.” (Hauschild, Herrmann & Kara 2017, p. 6)

A well-known approach to quantify the environmental impact and to put the focus on the human made technology factor is the IPAT equation. This equation was introduced by Ehrlich and Holdren (1971) and describes an approach to measure the environmental impacts based on the global population, the human affluence, and the technology (see equation 2.7). The main objective of the IPAT equation is

Figure 2.25 Life cycle engineering framework (Hauschild, Herrmann & Kara 2017)

54 2 Theoretical Background and Technical Overview

2.4 Life Cycle Engineering and Mathematical Optimization

55

the deconstruction of the individual factors that result in the final environmental impact. By understanding which factors are drivers of environmental impacts and how these factors are interacting, strategies can be developed to mitigate the impacts. The IPAT equation shows that the environmental impact (I) increases if one of the three defining factors is raised. These factors are the population factor (P), the affluence factor (A) and the technology factor (T ). As all three factors are in a direct relation, an achieved decrease in one of these factors is compromised by an increase of another factor. Hence, a growing population can equalize the efficiency improvements made within the technology factor. I = P∗ A∗T

(2.7)

As economic companies do not have influence on the global population or the affluence, their only focus is to reduce the technology impact. (Ehrlich & Holdren 1971) Ideally, the reduction of this factor is high enough to compensate gains in the other factors to comply with global environmental targets and to stay within the limits of absolute sustainability (meaning eco-effective products) (Kara, Hauschild, Herrmann 2018). The efficiency is derived by using the inverse value of the technology factor: T1 . . Regarding the environmental concern, the concept of LCE influences the technology factor of the IPAT equation. (Hauschild, Herrmann & Kara 2017) The Kaya identity, which was introduced by Kaya and Yokobori (1997), further deconstructs the IPAT equation. The Kaya identity states that the global CO2 emissions are the result of the factors population, gross domestic product (GDP) per capita and energy consumption. Again, the reduction of population or GDP per capita are not desired targets, requiring the reduction of either the energy consumption or the CO2 emissions per energy consumption or energy provision. The Kaya identity is expressed in equation 2.8, with F being the man-made CO2 emissions, G the GDP per capita, P the population factor and E the energy consumption. (Kaya & Yokobori 1997) F=P∗

G E F ∗ ∗ P G E

(2.8)

Figure 2.26 shows the concept of Hauschild, Kara and Røpke (2020) on how absolute sustainability is integrated into life cycle engineering based on the IPAT equation. In order to be able to not only improve a single product regarding its environmental impact (relative eco-efficiency) but to consider absolute limits, it is required to also include a growing market for the product by an increase of the

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factors population and/or affluence. When the total environmental impact of an entire product generation remains within the limits of the planetary boundaries by not exceeding its available emission budget, it contributes to eco-effectiveness.

Figure 2.26 Eco-effectiveness and life cycle engineering (Hauschild, Kara & Røpke 2020, p. 3)

The IPAT equation and the Kaya identity point out that in order to reduce GHG emissions, the efficiency of products has to be improved or the consumed energy source freed from GHG emissions, or, ideally, both. Those reductions need to outperform the remaining factors to lower the overall environmental impact and to contribute to eco-efficiency. Transferred to the economic world and technological industries, there is a huge need for environmentally optimized products that reach absolute limits that are ambitious enough to support absolute sustainability. Thus, newly developed products need to contribute to sustainability in absolute terms and not only be more sustainable than the product they aim to replace.

2.4 Life Cycle Engineering and Mathematical Optimization

2.4.2

57

Optimization Approaches for Decision Support

Optimization approaches from the field of operations research are applied to support decision makers with regard to many aspects of product systems concerning their financial and environmental aspects. The following section introduces the general principle of optimization methods and problem complexity that are necessary to select a suitable approach for the optimization of the relevant parameter. Operations research and optimization The term “operations research” is defined as a discipline that addresses the analysis of complex problem statements within the scope of a planning process to support the decision-making process by using mathematical methods. The typical process of operations research follows the steps of (1) problem analysis, (2) evaluation of targets and options for action, (3) mathematical modelling, (4), data gathering, (5) problem solving and (6) evaluation of solution. These steps are iterative loops that are constantly revised during the entire process. In a narrow sense, operations research is often defined as the mathematical modelling of decision support problems and the development of algorithms to identify solutions for the designed models. (Domschke, Drexl, Klein & Scholl 2015 p. 1 f.) In a mathematical sense, an optimization problem is solved by identifying the optimal solution of all possible options. All solutions must be comparable regarding certain criteria to be able to classify the quality of each solution and to identify the ideal solution. The comparability of solutions is achieved by defining a specific objective that has to be reached. (Ellinger, Beuermann & Leisten 2003, p. 2 f) The optimal decision-making process is described as follows: “While considering the decision-restrictions and impacts, identify the decision that fulfills the specific objective the most.” (Papageorgiou, Leibold & Buss 2015, p. 1)

Operations research offers the required tools to enable a systematic decision support. It is fundamental to formulate a precise problem definition using mathematical notifications for the objective functions and secondary restrictions in order to be able to use these tools. It is important to differentiate between given input parameters and the sought parameter that is to be optimized with the objective function. This objective function (or target function) is usually expressed with either a minimization or maximization function of the target value. (Papageorgiou et al. 2015, p. 1 f.) Equation 2.9 shows a general formulation of the instance of an optimization problem over R with an optimization of ϕ over F. In this equation,

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ϕ is the objective function and F the feasible region where every x ∈ F is permitted. When a maximization of the objective function is desired, equation 2.10 is added to the objective function, in the opposite case, a desired minimization of the objective function, equation 2.11 is applied. (Gritzmann 2013, p. 1 f) n ∈ N ∧ F ⊂ G ⊂ Rn ∧ ϕ : G → R ∧ opt ∈ {min, max}

(2.9)

  x ∈ F → ϕ x ∗ ≥ ϕ(x)

(2.10)

  x ∈ F → ϕ x ∗ ≤ ϕ(x)

(2.11)

Such optimization problems are solved with optimization algorithms or heuristics. An algorithm is defined as follows: “An algorithm is an exactly defined processing description to solve a given problem or a special group of problems. Typically, an algorithm is characterized by a limited amount of actions that are applied consecutively and are repeated in a determined way.” (Kastner & Schildt 2005, p. 83)

Heuristics are described as general approaches for the search of an ideal solution that accept intermediate deteriorations of the solution to leave a local optimum in search for a better solution. However, a heuristic will not result in a guaranteed optimum, as it is not possible to determine how far off the selected solution is from the overall optimum. (Suhl & Mellouli 2013, p. 13) The selection of either an algorithm or a heuristic depends on the type and size of the given problem. In the field of operations research, many different types of algorithms and heuristics exist that can be adapted and applied to a given problem statement. Among the most spread procedures for solving optimization problems are linear optimization (e.g. simplex approach), nonlinear optimization (e.g. Lagrange multiplier), dynamic optimization and integer programming (e.g. branch and bound). Popular (meta-)heuristics are e.g. tabu search, greedy algorithm, or genetic algorithms. (Zimmermann 2008) Complexity and problem size The combinatorial problem size for a modular product system increases quickly given many module options, as already shown in section 2.1. The large number of possible combinations requires the selection of an appropriate solution approach. The number of possible combinations represents the search space or

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solution space of the optimization problem. Next to the type of the problem statement, the size of the search space influences the complexity class of the problem. The computational complexity theory (CCT) deals with the question if a given problem that is described by an algorithm (and thus can be calculated) also can be practically solved with limited computational capacity. CCT also strives to identify the most efficient algorithm to solve the given problem. Figure 2.27 shows that the practically solvable problem types form a subgroup of the calculable problem types which in turn form a subgroup of all problem types. Limiting factors for problem types to be practically solvable are time and calculation capacity and depend on the size of the search space and the required calculation operations. (Ernst, Schmidt & Beneken 2016) The complexity of a problem and the behavior of an algorithm is described by the “Big O Notation”. Due to the big O notation, it becomes possible to classify the performance of an algorithm depending on the required computation time and space to solve the given problem. (Chivers & Sleightholme 2018) The defining feature of the big O notation of a problem statement is always the biggest mathematical term of a function. Forthefunction f (x) = x 3 +10x 2 + 3 11x+17, the big O notation  2  results in f (x) = O x . . (Rubinstein-Salzedo 2018) A complexity of O x is significantly quicker to solve than a complexity of  O x 3 . Still, basically all polynomial problem statements are practically solvable with an existing algorithm. For exponentially growing problems or algorithm types on the other hand, the required calculation time is increasing rapidly. (Ernst et al. 2016) Table 2.3 gives an overview of different big O notations and their complexity. Figure 2.27 Categorization of problem types (based on Ernst et al. 2016)

all problem types

calculable problem types

praccally solvable problem types

An algorithm for the complexity class of O(n) is programmed in such a way that each of the n elements of the input data need to be processed only once or a constant number of times. Doubling the input data hence leads to a twice

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  as long calculation time. For a complexity class of O n 2 , , every element has to be processed twice (e.g. sorting algorithm). Doubling the input data leads to a processing duration of four times the original duration. For a data set N and a complexity O(n), , a computer that calculates a thousand times faster can process an input data that is thousand times bigger (1000N ) at the same   time. For a complexity class of O n 2 , , a thousand-fold calculation capacity leads to an increase of only 32 times the original input data (32N ) and for O(2n ) only to N + 10. The quickly increasing calculation time and the little effect of a higher calculation speed of exponential complexity classes mark a crucial difference between exponential and polynomial complexity classes. While polynomial complexity classes are usually practically solvable, the exponential classes are usually not. (Ernst et al. 2016) Table 2.3 Complexity classes and big O notation (based on Chivers & Sleightholme 2018)

Big O notation

Complexity

O(1)

Constant complexity

O(n)

Linear complexity

O(log n)   O n2   O n3

Logarithmic complexity

O(x n )

Exponential complexity

O(n!)

Factorial complexity

Quadratic complexity Cubic complexity

This situation is the foundation of the categorization of problems into the classes of P and NP. Problems that fall into the category P are all decision problems that can efficiently be calculated within a polynomial runtime. It is solvable if there is an algorithm with a complexity of O( p n ), where p n is a polynomial function of an arbitrary degree. Hence, all problems that cannot be solved within a polynomial time are sorted into the category NP (nondeterministic polynomial time). (Ernst et al. 2016) If the ideal solution for a decision support problem is desired, the problem type has to be in the problem class P and a suitable algorithm with a polynomial runtime has to be applied.

2.5

Conclusions Regarding the Theoretical Background

Summarizing the findings of this chapter, the extensive use of carbon-based energy sources to cover the high energy demand of human activities causes an

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enormous impact on the GHG emissions and thereby on the change of the global mean temperature. Legislations on global, continental, and national level have been installed to control the GHG emissions to reach sustainable development and absolute sustainability. This includes strict regulations for the automotive sector, where GHG emissions need to be drastically reduced to comply with future regulations. In order to improve product development and to design products that are in line with these decarbonized restrictions, the tools of life cycle engineering need to be applied into the early stage of product development. As shown, a modular product architecture of product systems leads to many possible alternatives for product system configurations, which makes it difficult to identify the ideal combination of modules that leads to financially and environmentally optimized life cycle properties. Therefore, approaches of mathematical optimization need to be applied for supporting decision makers during the product development. The methodology that is developed in this work, in general, is applicable for various environmental targets that have been discussed previously. However, for better understanding, it is explained and demonstrated for the reduction of GHG emissions. For this study, the social aspect of the triple bottom line will not be included in the calculation models, leaving the focus on the economical and the environmental aspects (in the sense of eco-efficiency). Considering this exemplary scope, this work contributes to the reduction of greenhouse gases by providing a systematic methodology that analyses the most cost-efficient pathways for product decarbonization. This methodology helps to develop eco-effective product systems if the determined environmental target is in line with the goals of the Paris Agreement.

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State of Research and Identification of the Research Gap

3.1

Criteria and Requirements for Optimizing the Eco-effectiveness of Product Systems

Several prerequisites need to be fulfilled when facing the challenge of supporting the product development by optimizing the eco-effectiveness of a single product system or multiple units of this system. These prerequisites describe the framework of the given problem statement and describe the required conditions that a solution to this problem needs to meet. Some of these conditions focus on the input data and information that is included in the framework. Other conditions describe the requirements on the modelling of the problem statement as well as the algorithm and the solution that is to be found. Furthermore, it is described which knowledge and framing conditions regarding the product system in question have to be available to derive useful results with a suitable approach or tool. The prerequisites that are described in this chapter are distinguished between criteria that a general solution approach has to meet in order to enable correct solutions on the one hand and additional requirements that help the user to generate stable and comparable solutions on the other. The criteria described in the following subchapters are briefly summarized after each paragraph and later used to evaluate the current state of research. The additional requirements, however, are not used for the comparison of possible approaches as they do not describe the solution approach but the product system itself. Therefore, those requirements do not influence the suitability of an approach regarding the suitability to solve the given problem statement.

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 C. D. Gabrisch, Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry, AutoUni – Schriftenreihe 164, https://doi.org/10.1007/978-3-658-40594-6_3

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3.1.1

State of Research and Identification of the Research Gap

Criteria to Handle Product System Modularity

It is relevant for a proper solution approach to not just compare different available product systems and identify the most suitable out of them, but to assist in creating a new product system that meets the given restrictions to support the development of future product systems. This requires a shift from the retrospective assessment of product systems towards an approach that strives to engineer a product that is designed to meet the targeted limits. In order to be able to propose new product system configurations, different product modules or components and their alternatives need to be given as input data for each function of the product. Those different product modules that are combined for the final assembly of the product serve as a modular construction system. 

Criteria (C)1: Availability of individual product modules and their alternatives

The availability of many different modules and module alternatives quickly leads to a high number of possible combinations (see section 2.1). If the combination of those modules is not regulated in any way, it is possible that, among all resulting product systems, there are many combinations that are either technically impossible or undesired. These combinations will unnecessarily be part of the solution space and thus the optimization process. Including such combinations ultimately makes the search for the best possible solution more complex. In order to reduce complexity, only module combinations are to be created that represent a valid product system. This requires the application of a model or an algorithm that avoids the consideration of certain combinations. 

C2: Avoiding undesired module combinations

It is important that the selected approach covers both the ecological and the financial impacts of the final product system along its entire life cycle in order to achieve a higher eco-effectiveness. This requires having both the LCA and the LCC results available for each product module. These results are used to calculate the life cycle performance of the final product system configuration by adding up the results of each module. 

C3: LCA and LCC results for each product module are available

3.1 Criteria and Requirements for Optimizing …

65

However, combining several modules and adding up their individual LCA and LCC results may lead to wrong results if interdependencies between different modules occur (see section 3.2.1). If different modules of a product interact and the specification of one module influences the performance of another module (e.g. a reduced weight of a module enhances the energy efficiency of the engine), the static results of LCA and LCC (e.g. for the engine) can be incorrect because each configuration would have an individual result. This means that those interdependencies between individual modules have to be considered for the overall results regarding the ecological and financial performance. Hence, a modular optimization approach of the product system has to meet the requirements of considering such interdependencies. 

C4: Consideration of interdependencies between combined modules is possible

3.1.2

Criteria to Handle Use Case Specific Requirements

The optimization of eco-effectiveness can result in the implementation of modules that require special production processes or infrastructures in its supply chain or operation. A holistic approach for the optimization of the product system must consider the investments in or scale-ups of supporting processes that do not belong to the direct product system. This is required if despite the investment in new production technologies or infrastructures, the new technologies lead to a higher eco-effectiveness. 

C5: Consideration of additional production processes or infrastructure for optimization of product system

As the developed methodology shall also be applicable to not only single product systems, but also to multiple product systems (e.g. a fleet of vehicles), it is required to identify the ideal configuration for not just one product, but also for multiple units of this product system. Therefore, the applied solution approach must be flexible in optimizing any amount of product units given the same framing conditions. 

C6: Optimization of single product system or multiple units of the product system (product fleet)

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However, not every product module or measure might be available for every product system. The optimization algorithm needs to consider these limitations and must identify the optimal assignation of modules and product units given these conditions. 

C7: Consideration of possible limitations regarding the availability of modules

Also, a time perspective needs to be included into the calculation of the results. Over the time of operation, increasing or decreasing costs for e.g. fuels or energy for the use stage can influence the choice for the ideal product configuration. 

C8: Consideration of impact from time-dependent price developments

3.1.3

Criteria for Optimization Approach

The described requirements from the problem statement need to be fulfilled by the optimization approach. Furthermore, the approach needs to be able to manage and process the given input data and handle the given problem statement in an efficient way. An additional criterion for the optimization algorithm is being able to switch easily between the optimization of a financial minimum for a given environmental limit and a minimum environmental impact for a limited financial budget. As the target is the optimization of eco-effectiveness, the limit values need to be absolute figures. Furthermore, the algorithm must be designed in a way that it can handle extensive product alternatives with many possible module combinations.  

C9: Enabling both absolute financial and environmental targets as optimization objective C10: Handling a large number of product alternatives

Next to the interchangeable optimization objective, it is also relevant for the solution approach to enable an optimization that can focus on different life cycle stages. For certain applications (e.g. in the automotive industry), it is important to minimize environmental impacts along the entire life cycle to comply with absolute sustainability goals. However, at the same time it is necessary to meet a limitation of a governmental regulation that considers the use stage only. This means that the optimization algorithm must meet several secondary constraints

3.1 Criteria and Requirements for Optimizing …

67

and needs to be able to separate the individual life cycle stages in terms of financial and environmental performance. 

C11: Enabling the consideration of secondary constraints regarding different life cycle stages

A high solution quality, a fast solution time and an appropriate presentation of the results leads to an informative visualization of the results. 

C12: High solution quality, quick runtime and appropriate visualization of problem and results

3.1.4

Additional Requirements to Obtain Useful Results

Further requirements and information regarding the product system in question are required to achieve applicable solutions. These requirements are not used to evaluate the suitability of a solution approach, but are still helpful to select a suitable method. The considered product system that is to be optimized (e.g. a vehicle) needs to be well known by the product developer in order to be able to apply the optimization concept. It is important that the product system and its requested functions are precisely described to make sure that the algorithm combines the modules in such a way that all required functions are provided. Ideally, there is a reference product unit or a predecessor model that can be used as a benchmark for the new product system. Furthermore, the product architecture and the hierarchical composition of the product’s components have to be known. The information regarding the number of modules, their connection and position within the product is a crucial aspect to model the product system correctly. In order to increase the level of detail and the quality of the result, specific details regarding the components, such as weight, dimensions and material used, is required for the quantification of the financial and environmental burdens. This information can e.g. be provided by a bill of materials for the given product or a predecessor model. The required information exceeds the narrow boundaries of the physical product unit and applies to the production processes and the end of life treatment as well. For a complete modelling and optimization of the life cycle performance, the supply chain of the components and their materials also have to be known. The same applies to the assembly and dismantling processes and the specific use

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of energy and materials for those steps. For the materials used and the production processes the corresponding prices need to be available. In addition to the description of the initial product system, it is relevant to know which components or modules can be replaced by alternatives. These alternatives are characterized by e.g. different materials, a different geometry or different principles of functionality while maintaining the function of the original product system. These alternative modules and other possible measures during the production, use or end of life stage form the basis for the alternative product configurations of which the optimization model chooses the ideal set. For the optimization of multiple product units, it is also required to have information regarding the availability of individual modules and measures. If a certain module cannot be provided for the entire product fleet (e.g. because of limited material supply), these boundary levels have to be known to make sure the optimization algorithm does consider those limitations. Moreover, clearly defined assumptions need to be determined for the framing condition of its life cycle, especially with regard to the use stage. A fixed use stage needs to be defined that describes the time frame of the use stage and a defined way of utilization. This standardization of the use stage is required to cover the financial and environmental impacts over the use stage for all product alternatives in an equal manner. The time frame of the use stage is defined as a temporal limitation (e.g. 15 years) or as a limited number of operations (e.g. 100,000 km of mileage for a vehicle). The use profile is defined by using comparable and reproducible test cycles that include all relevant actions and represent the average use profile of the product. For the automotive industry, such cycles are described in section 2.3 (NEDC or WLTP cycles). For the evaluation of life cycle costs, the time horizon for the use stage has to be defined to make suitable predictions for the development of energy and material prices. Additionally, the financial perspective has to be declared as the optimization can lead to different results for either the customers’ or the manufacturer’s point of view (see section 2.2.2).

3.2

Current Approaches and State of Research

As stated in section 3.1, approaches for the optimization of eco-effectiveness require (1) a sufficient product modularity to incorporate interdependencies between modules, (2) data input of LCA and LCC for the individual modules and the final product systems, and (3) a suitable method for the optimization of the information regarding the eco-effectiveness. To identify relevant literature

3.2 Current Approaches and State of Research

69

that covers all these aspects at the same time, all three areas are searched for approaches that also overlap with the other fields. For the field of product modularity, approaches and publications are analyzed regarding how the introduced modularity is used and if this modularity has a benefit in terms of optimization of modular products with regard to the LCA and LCC results. The literature on modular LCAs is analyzed by assessing if the modular approach is used for optimization of the product system by selecting the ideal module for each step. The literature focusing on LCA & LCC optimization is analyzed with regard to modular product systems and product population fleets. Figure 3.1 shows how the analyzed literature is selected by focusing on approaches that combine all relevant aspects. On the left, a three staged graph is shown that shows whether a topic field is considered (dark box) or not (white box). Every stage adds another prerequisite, starting with product modularity and modular LCAs to the optimization of LCA and LCC. As only those approaches are relevant that cover all three aspects, only the lowest route through the graph is relevant for the literature research. Only for this path all stages are marked as “yes”, resulting in the overlapping focus area that is also shown on the right hand side of Figure 3.1. + Stage III: Opmizaon of LCAs & LCCs

1 + Stage II: Modular LCAs Stage I: Product modularity

1 Product modularity

2

5

3 4

7

7

6

Focus area

5 6

8

3

4

2

Legend

No

Yes

8

Figure 3.1 Focus area for literature review

The current state of the art of these areas of research in the field of life cycle engineering is described in this subchapter.

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3.2.1

3

State of Research and Identification of the Research Gap

Product Modularity

Based on the definition of modular products in section 2.1, the methods to divide a product system into the ideal number of modules and the analysis of the relations between the individual modules have developed over time. Umeda et al. (2008) introduce a methodology to determine modular product designs that fulfill both LCE objectives such as upgradability or recyclability and a geometrical feasibility of the product architecture (Umeda, Fukushige, Tonoike & Kondoh 2008). Tseng et al. (2008) evaluate and calculate the liaison intensity of components and depict these connections by using liaison graphs. Then, a grouping genetic algorithm is applied to cluster the components into modules. The modular product architecture is then evaluated in terms of production cost and environmental impact. Afterwards, iterative changes to the components are evaluated to identify “green life cycle engineering”. (Tseng, Chang & Li 2008). ElMaraghy and AlGeddawy (2013) introduce an analysis of the granularity of modules in a product system, with a deeper hierarchy of components indicating the level of the granularity. For a high interchangeability of modules within product families, the level of granularity needs to be considered. A design structure matrix is combined with a hierarchical clustering, resulting in a clustering tree that is used to identify the ideal level of module granularity. (ElMaraghy & AlGeddawy 2013) The mentioned research mainly focuses on improvements for manufacturing and assembly of product systems. Halstenberg et al. (2015) state in their research that a modular product architecture is also useful for addressing targets of a sustainable product design. Also, based on a broad literature review, different forms of dependencies between components have been identified. They form the basis for the clustering of components into modules. (Halstenberg et al. 2015) Those forms of module interdependencies are: “Independence and similarity analysis, component position pattern, assembly dependency, accessibility, cost of reusability, interface openness and interface design effort” (Halstenberg et al. 2015, p. 604). Wang et al. (2016) studied the interdependencies between individual components of a product system in order to be able to include these interactions into their approach to enable a green modular design. A prediction model has been developed that helps to identify how many other components are affected by the change of one component. A propagation tree is used, providing the tracking of the probability of influences between models, based on stochastic variables. The results are then displayed in a dependency matrix between the modules. The individual likelihood of a dependency between two components is derived from the

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71

experience of the product designer. The influence of a module on the fulfilment of the desired product function is also evaluated in order to understand if the change of a module leads to a reduction of the product’s performance. The intensity of the physical connection between components is analyzed and expressed by liaison graphs. (Wang, Tang, Yin & Yang 2016). Mutingi et al. (2017) present a liaison graph for the assembly of a pen. The nodes of this liaison graph symbolize the different product modules and the arcs show the liaisons between the modules. The weight of each arc defines the intensity of the liaison (see Figure 3.2). Ideally, the liaisons within the components of a module are maximized while the liaisons between different modules are minimized. (Mutingi, Dube & Mbohwa 2017).

1. Button

2. Body

4. Head

3. Cap

5. Tube

6. Ink

Liaison intensity

Figure 3.2 Liaison graph for module connections (Mutingi et al. 2017, p. 473)

The research on product modularity shows that the product components and the architecture in which the components are structured have been analyzed in many aspects by various authors. The biggest focus of the studies is on the optimized grouping of components into modules. Also, the interdependencies between individual components and modules have been considered. The knowledge of product modularity is often used for the ease of manufacturing or a simpler mass customization. In terms of sustainable product development, the results and findings have mostly been linked to life cycle aspects like upgradability or recyclability.

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3

State of Research and Identification of the Research Gap

Modular Life Cycle Assessments

Performing LCAs in general is a complicated and cost and time intensive task. The accumulation of extensive input data and process information, the process modelling, and the result calculation require time and calculation capacities. Also, the LCA results need to be interpreted correctly by an expert to derive relevant knowledge. (Kuo, Smith, Smith & Huang 2016; Otto et al. 2002) Simplifications to the LCA calculation can be made by the principle of modular LCAs to help making LCAs more easily applicable to many products and companies. Figure 3.3 shows how a modular LCA is defined in contrast to a conventional LCA. As defined in section 2.2, a conventional LCA is performed by modeling the entire product system connecting it to the background data and aggregating all occurring elementary flows. The classification of the LCI results leads to the system’s LCIA result. In contrast to this approach, a modular LCA consists of individual information modules, which are modelled including both foreground and background processes of those modules. The impact assessment is done before the entire product system is modelled instead of afterwards, which leads to reusable LCA modules. After the individual modelling of each module, all modules are connected in order to result in the total product system. (Buxmann, Kistler & Rebitzer 2009). The final LCIA result of the entire product system is the sum of the individual LCIA results of each module. The conventional and modular LCA both result in the same indicator result. The main difference is the order in which the different processes are modelled and calculated. While the conventional LCA models the entire system first and then calculates the LCIA result, the modular LCA models and calculates separated modules and recombines them afterwards. (Buxmann, Kistler & Rebitzer 2009). The systematic literature review of Sonego et al. (2018) regarding “the role of modularity in sustainable design” shows that the general topic of modularization in life cycle engineering is becoming more and more relevant. Most published and reviewed articles in this field focus on the aspect of green modularization. Their goal is to provide solutions for the challenge of clustering components into modules in a way that environmental burdens are minimized or life cycle concerns such as reuse or recycling are improved. Another focus lies on the implementation of modularization following the principle of “Design for X”. Only four out of the 145 identified publications connect the product modularization to the topic of life cycle assessment. (Sonego, Echeveste & Debarba 2018). Jungbluth (2000) introduces modular LCAs by dividing the initial product system into clearly separated modules and assessing the life cycles of these modules

3.2 Current Approaches and State of Research

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Figure 3.3 Comparison of conventional LCA and modular LCA (Buxmann et al. 2009, p. 96)

individually. In his work, Jungbluth (2000) uses the example of meat production and separates the life cycle into five independent modules: (1) agriculture, (2) processing and distribution, (3) transport, (4) packaging and (5) consumption. For these modules, individual LCAs are calculated. This approach results in individual LCAs for each module of the product system. The final LCA of the entire product is then calculated by recombining the modules and adding up their LCA results. The advantage of this method is the calculation of LCAs for modular product systems where several modules can be replaced by alternative modules. As every module has its own LCA result, many different product systems are configured by adding up the module results without having to perform new LCAs for every possible product system. To assess alternatives regarding e.g. transport, packaging, or conservation, only the corresponding module needs to be adapted and afterwards combined with the other modules again. (Jungbluth 2000, p. 249 f.) Modular LCAs help to evaluate the impacts of different product

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parameters and show the differences between different product system alternatives. These features make the approach extremely helpful when a wide range of possible product configurations is assessed. (Jungbluth, Tietje & Scholz 2000) In comparison, a conventional LCA is not flexible enough to calculate the same large amount of different LCAs for differently configured product systems in a comparable time span, as every variant needs to be modelled individually. According to Jungbluth (2000), modular LCAs lead to new challenges, as the separation of a product system into modules can be accompanied by problems regarding allocations. This occurs when an environmental impact is linked to more than one product module. This environmental impact then has to be attributed to one module and separated from the other. As a consequence, changes in the isolated module cannot be considered anymore regarding that specific environmental impact. Furthermore, modular LCAs lead to a higher effort for the interpretation of the results as often not all theoretically possible module combinations result in technically feasible products. (Jungbluth 2000, p. 249 f.) Otto et al. (2002) have shown that the complexity of integrating LCAs into the product development phase, especially for product families, can be reduced by identifying common modules within all products of the product family. Assessing these modules once but considering their results for all products reduces the LCA effort. (Otto et al. 2002) Rebitzer (2005) has proven that the results of a modular LCA for a product system are equivalent to the results of a regular LCA. While the method of calculation differs from the conventional approach, a modular LCA is in line with the ISO 14040 and 14,044 standards. Rebitzer (2005) states that modular LCAs are a suitable tool to analyze product alternatives or product variations with less complexity. Dose (2005) shows in her research that modular LCAs can be affected by interdependencies between modules as changes between module alternatives can affect the whole product system. Dose (2005) introduces exchangeable life cycle inventories (LCI) of modules in order to be able to still use modular LCAs for product systems that are sensitive to module interactions. These interchangeable LCIs of the modules are clearly separated by cut-offs from their surrounding modules in the product system, leading to parametrized modules with interfaces for a modular recombination. Those separated LCAs of modules help to calculate LCAs for different product systems much faster. (Dose 2005). The combination of LCAs with a modular product design has also been studied by Reccioni et al. (2007) in 2007. According to their research, “the modularity concept implies many aspects which allow some simplifications on the application of the Life Cycle Assessment (LCA) methodology” (Reccioni et al. 2007, p. 54). For product systems with a high modularity, the change of a single module does

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not affect the rest of the product system. Hence, the changes to the LCA result only refer within this module. Thus, if a module is exchanged, only the new module has to be assessed. Reccioni et al. (2007) state that LCAs of independent modules decrease the required calculation time and the required amount of data as less information needs to be processed. In a review for sustainable product design, Chio and Chu (2012) have identified that “most sustainable design methods developed in the past failed to address the interdependencies among different stages in a product’s life cycle. This deficiency may result in biased estimation and wrong decisions.” (Chiu & Chu 2012, p. 1268). This leads to a need for approaches that integrate “the scopes of product, process, system, and ecosystem, while balancing conflicting product development perspectives.” (Chiu & Chu 2012, p. 1268). Such an approach can e.g. be implemented by applying modular LCAs into the product development phase (Sonego et al. 2018). For the development of a predictive eco-design process for the product development phase, Kuo et al. (2016) use product modules as attributes of the product system’s LCA. This helps to reduce time and cost efforts as well as the required amount of calculated LCAs. The predictive LCA approach incorporates modular LCAs as well as graph modelling and graph searching approaches like depth first search. Based on a similarity threshold, the individual components and modules are grouped. Their LCA is predicted based on the LCA results of previous similar designs. (Kuo et al. 2016). Kim and Moon (2019) introduced an approach to utilize modular product architectures to optimize the product design with regard to the product recovery at the end of life stage of a product’s life cycle. The target is to enable a modular product design in such a way that each module does not have to be dismantled further to the level of sub-components or material to be recycled. Thus, the module is the smallest required unit for dismantling and further steps can be avoided to reduce costs and environmental impacts. (Kim & Moon 2019).

3.2.3

Optimization of LCA and LCC

Azapagic and Clift (1998) first introduced the combination of LCA and LCC analyses of a product system and the joined optimization of both dimensions for an optimized product system. Their research is based on a multi-objective linear programming approach to optimize multiple environmental or financial objectives of the product’s life cycle. (Azapagic & Clift 1998; Azapagic & Clift 1999) Since then, the research field for the optimization of both environmental

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(LCA) and financial (LCC) aspects of a product’s life cycle has been analyzed and studied by many authors, covering different subareas. The results provide multiple approaches to identify product systems with an optimized ratio of lifetime costs to environmental impacts. The literature review of Miah et al. (2017) categorizes the different approaches of LCA and LCC integration into six separate types: independent LCA and LCC (Type I), independent LCA and LCC as part of an overarching framework (Type II), independent LCA and LCC analysis integrated by MCDA (Type III), optimization of LCA and LCC analysis (Type IV), environmental LCC (Type V), and eco-efficiency methods (Type VI) (Miah, Koh & Stone 2017, p. 850). For the research of this work, the types IV and VI are most suitable in terms of methodology and desired results. In approaches that fall into category IV, the optimization of LCA and LCC is typically designed by an objective function that is to be minimized or maximized with respect to boundary conditions and constraining functions. The solution space becomes extremely large due to a high number of possible outcomes. Therefore, optimization algorithms like (non-)linear programming are applied to the problem statement. Across the 32 identified approaches of this type, Miah et al. (2017) found a broad spectrum of applications, with most studies being performed for the analysis of buildings and energy systems. In these approaches, various algorithm types like multi-objective linear programming, mixed integer linear programming, genetic algorithms, or multidisciplinary design optimization were utilized. The identified drawbacks for these approaches are the required effort for system modelling and the required information for the implementation of the algorithm into a mathematical software. The 19 studies that form type VI of the LCA and LCC integration show a similar distribution of various fields of application. The central difference between the methods of type VI is their definition and calculation of the eco-efficiency index. (Miah et al. 2017). LCA and LCC optimization for product systems As defined in section 2.4, the optimization of one or more parameters of an objective function aims to identify the ideal set of parameters to either maximize or minimize the desired value. Such optimization approaches can also be applied to the use cases of LCA or LCC. An exemplary adaption of the LCA and LCC-oriented optimization for the field of buildings is described by Islam et al. (2015). In this approach, possible combinations of modules such as alternative walls, floors and roofs regarding different designs and materials are assessed and optimized. A single-objective optimization algorithm minimizes either the life cycle costs or the environmental impacts. Within this study,

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a total of 31 alternative building configurations are evaluated while interactions or interdependencies between modules are neglected. (Islam, Jollands, Setunge & Bhuiyan 2015). The study of Herrmann et al. (2014) regarding a “structural” LCA approach describes alternative life cycle pathways of a product system as variations or alternatives of individual processes. A “structural LCA” is defined by Herrmann et al. (2014) as the systematical addressing of many different variations or alternatives. These alternatives lead to an extremely high number of possible pathways due to the high number of possible alternative combinations. The introduced example is a set of 1.09 ∗ 1012 different product system configurations (or alternative life cycle pathways). However, not all combinations are technically feasible. The regular approach of LCAs, where only a few variants are analyzed, fails to analyze these pathways in a short time as every time a completely new LCA has to be performed. The structural LCA approach on the other hand manages to increase the possible amount of LCAs and provides new possibilities for analyzation and optimization. This optimization is either focused on single-objective or multi-objective functions. The individual pathways are organized by a unique ID number within a structural table. The complexity increases if interdependencies of different pathways occur. Herrmann et al. (2014) recommend statistical software tools to cover these interactions, but did not include them in their model. (Herrmann et al. 2014). Buchert et al. (2015) describe an approach in which decision trees are used to model the hierarchical structure of the product system. Such a decision tree visualizes the alternatives that occur at each new step of the product system’s life cycle. Each branch of the decision tree is quantified by the environmental impact of the given alternative. The decision tree resembles the “value creation network” of the product. The ideal path for a sustainable product (covering LCA and LCC) through this network can be identified by applying a multi-objective decision algorithm to it. Buchert et al. (2015) show that the effort of modelling and the solution space grow quickly with every new hierarchy level in the decision tree as the number of possible combinations grows. A higher level of detail for each step also increases data demand and effort for modelling and calculation of the ideal solution (Buchert, Neugebauer, Schenker, Lindow & Stark 2015). Nadoveza et al. (2013) present a link from conventional LCAs to LCAs based on graph theory to reduce the required time for decision making. The choice of the graph theory for modelling the algorithm is based on the advantages regarding visualization of the problem statement and solutions as well as on the broad availability of different optimization algorithms for graphs and networks. The approach uses those advantages by modelling a product system and their life cycle factors as a graph connecting the interfaces of each factor with edges. The relations between

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the nodes are represented by interdependencies that occur between the individual life cycle factors. A change of one factor and the resulting effect on another factor can be visualized within the graph. (Nadoveza, Koukias, Karakoyun & Kiritsis 2013) The approach of Nadoveza et al. (2013) “provides the capacity to set up the goal and scope of analysis (e.g. carbon footprint below a threshold), and the system may then find all the possibilities and opportunities in order to achieve that goal” (Nadoveza et al. 2013, p. 413). The outlook of their study indicates the necessity to expand the graph based LCA optimization by a financial dimension like life cycle costing. (Nadoveza et al. 2013). The idea of analyzing and optimizing the LCA result of a product system with the tools of graph theory is further described by Jahandideh et al. (2015) “to overcome the complexity of traditional Matrix-based analysis” (Jahandideh, Aminikhanghahi, Salehnia & Muthukumarappan 2015, p. 1). For lignin derived chemicals, a network for the current and possibly alternative production processes is designed. The goal is to optimize the environmental and economic dimensions of the product. The connecting edges of the graph are weighted with the conversion yield of the corresponding production process as well as the environmental impact of that path. If a certain combination of chemicals is not feasible, the network stops at the last possible node and illogical combinations are eliminated. As the graph is designed following the physical material flows, the model is an acyclic graph with a single source and a single destination node offering multiple pathways in between. (Jahandideh et al. 2015) The decision problem is formulated as a minimization problem to optimize the production process with respect to a design path with reduced environmental and economic burdens. This minimization problem is programmed as a shortest path problem through the network and solved with a modified version of the Dijkstra algorithm. Maximizing the profit on the other hand leads to a problem formulation equivalent to the identification of the longest path through the network which resembles a NP-complete problem regarding the complexity class. A multi-objective analysis is performed leading to trade-offs between both dimensions to find a solution with minimal environmental burden and a high financial profit. (Jahandideh et al. 2015). A further approach to enable the evaluation of an extensive number of product configuration alternatives for modular products is presented by Steubing et al. (2016) who focus on streamlining the LCA calculation in order to reduce the calculation effort. The analyzed example describes alternative process paths to generate heat from biomass. The process chain contains five consecutive steps with two to four alternatives per step, resulting in 14 process modules that can be connected to 144 different process alternatives (see Figure 3.4). The idea of the modular LCA is then to only calculate the LCAs for the 14 modules and recombine them to the 144

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options instead of directly modelling all alternatives. A drawback of this approach is the computational structure of LCAs as it relies on process-process links with every input of a process coming from a certain upstream process. Therefore, regular LCI databases are not suitable for large scenario evaluations. Hence, in order to be able to calculate all 144 options in a modular way, copies of upstream processes for each module are required to cover each individual path. For the 144 options of the example, this leads to 212 required processes (see Figure 3.4). (Steubing, Mutel, Suter & Hellweg 2016). Figure 3.4 a) shows the 14 different modules in five different hierarchical steps. The black arrows represent a selected path while the dotted arrows show all other possible connections between the modules resulting in 144 alternatives. Figure 3.4 b) depicts the further growth of process modules if specific upstream paths are considered. LCI modules of intermediate process steps need to be cleared of their own supply chain impacts to avoid a double counting of processes, as this information is provided by the previous module. The full enumeration of these paths results in 212 required modules. (Steubing et al. 2016). The process system in Figure 3.4 is used by Steubing et al. (2016) as a model for an optimization problem with the target to minimize the environmental burdens. The organization of the input data is done using a “module-product matrix” that contains the environmental information for the possible alternatives of the product system and links input amounts with outputs for each process. In order to link every possible value chain with unique module configurations, a square matrix is needed. Steubing et al. (2016) use a recursive depth-first graph traversal algorithm to identify all possible value chains that are offered by the module-product matrix, as they cannot be directly identified from the matrix. The final optimization model is then based on a generic linear programming algorithm that optimizes the product system based on the data of the matrices. (Steubing et al. 2016). Ameli et al. (2017) propose a methodology for the optimization of a product system with trade-offs between life cycle costs and environmental impacts. Their approach is optimized for typical product design problems of products with many components and many alternatives for each component (e.g. automotive or airplane manufacturing). As an example, a product containing 50 different components is described (while a typical automobile has up to 10,000 parts) where every component has three alternatives. This leads to a total of 717 ∗ 1021 options with a required LCA and LCC evaluation for every single option. (Ameli et al. 2017). Figure 3.5 shows the data structure for the alternative product designs by Ameli et al. (2017) that is used for the optimization. The optimization problem of the approach by Ameli et al. (2017) is designed in such a way that the environmental

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a

b Module A1 Module B1

Module D1

Module B2

Module C1

Module C2

Module D2

Module D3

Module E1

Module A1

Module A2

Module E2

Module B3

B1A1 C1B1-A1

Module D4 Module E3

B2A1 C2B1-A1

Module A2

B3A1

B1A2 C1B2-A1

B2A2 C2B2-A1

B3A2 C1…

…

…

Figure 3.4 Representation of alternative module combination options (based on Steubing et al. 2016, p. 512)

burden shall not surpass a defined limit value while the financial effort is to be minimized. While the environmental impacts are assessed over the entire life cycle, the costs cover only the expenses of certain life cycle stages which are directly related to a single component (e.g. material prices, transport costs, end of life costs). Costs of the use stage are not considers, as well as the influences of the components on the use stage costs are not considered. The optimization model is based on linear integer programming, where the objective function is the cost minimization and the constraining functions define the environmental threshold value. The result shows which alternative of each component should be selected to form the product system. By changing the limit value for the environmental impact, the financial effort of the product system either increases or decreases. The most cost-efficient configuration is selected without setting an environmental limit at all. By changing the objective function to the environmental impacts, the most cost-efficient configuration complying with the given emission level is identified. (Ameli et al. 2017). Automotive focused approaches of LCA and LCC optimization Research regarding LCA and/or LCC results and their optimization for multiple products, such as a fleet of automobiles, has been done by various authors like Reichmuth, Lutz, Manley & Keller (2013), Garcia, Gregory & Freire (2015), Ercan, Zhao, Tatari & Pazour (2015), Onat, Kucuvar, Tatri & Zheng (2015) and Lemme, Arruda & Bahiense (2019). Their common focus is the analysis of the environmental and financial impacts of automobiles and the optimization of the fleet composition in terms of sustainability goals.

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Figure 3.5 Design alternatives for product design (based on Ameli et al. 2017, p. 2451)

Reichmuth et al. (2013) and Garcia et al. (2015) both use a dynamic fleet model that, based on an initial fleet composition, considers annual vehicle sales and the amount of replaced vehicles for the fleet transition. The research objective of these authors is to come up with the best combination of up to six different types of powertrains for a fleet in the United States for the reduction of greenhouse gases in the use stage (Reichmuth et al.(2013)) or greenhouse gases over life cycle in Portugal (Garcia et al. (2015)). Both studies neither include a cost perspective nor

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an optimization model. The result calculation is based on simple comparisons of scenario. (Garcia et al. 2015; Reichmuth et al. 2013). The authors Ercan et al. (2015), Onat et al. (2015) and Lemme et al. (2019) present methodologies that use mathematical optimization tools to identify the ideal fleet composition in order to achieve environmental targets. Ercan et al. (2015) developed an approach to minimize both life cycle costs and environmental impacts based on a multi-objective linear programming algorithm. The methodology is shown as an example for a bus fleet in public transport. The results can be adapted to different budget limitations or emission reduction targets. For a fleet of 100 busses, six different powertrain options are considered. For different driving cycles and financial or environmental constraints, the ideal fleet composition with regard to powertrain shares is calculated. (Ercan et al. 2015) Onat et al. (2015) follow a similar approach, considering seven powertrain options for a multi-objective optimization in terms of financial, social, and environmental dimensions. Their case study focuses on passenger cars in the USA and offers different scenarios regarding criteria weighting and influencing parameters such as electricity supply. The calculated results show how intense the individual powertrains should be used to reach the given sustainability targets. (Onat et al. 2015) The approaches by Ercan et. al (2015) and Onat et al. (2015) do not consider configurable product systems based on a modular product architecture. Thus, the possibilities to create new product system variants that may lead to lower emission levels are limited. Further, no interdependencies between different component selections are considered, making it impossible to detect unwanted effects for certain configuration options. Lemme et al. (2019) apply their methodology to an example of small-scale car sharing, where three different types of powertrains and their infrastructure are considered. The analyzed dimensions cover financial and environmental aspects which are optimized with a single- and a multi-objective linear programming model. The results show the trade-off of financial and environmental burdens between conventional and electric powertrains and the significance of the infrastructure. (Lemme et al. 2019) The presented approach lacks the same flexibility for new product variants due to missing product modularity like the research of Onat et al. (2015) and Ercan et al. (2015). Furthermore, the optimization approach of Lemme et al. (2019) is not explicitly designed to handle a large number of combinations, but is designed to a smaller example. The presented research regarding the analyzation and optimization of sustainability aspects of multiple products such as product fleets differ greatly from the research regarding the optimization of the modular product architecture of a single product. The approaches for product fleets do not consider modular product systems and the interdependencies between the modules. Typically, the fleet optimization

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approaches do not focus on product development, but on the composition of available products. The considered number of variations or alternatives with only three to seven different powertrain options is much lower than the possible options that are considered for a modular product system covering the entire life cycle.

3.3

Identification of the Research Gap

The research field regarding the optimization of the product configuration during the product development phase with focus on the environmental and financial characteristics has been studied by many different authors as presented in the previous subchapters. The high diversity of published approaches, methodologies and frameworks covers a broad spectrum of different purposes within this large area of research. Many studies explicitly focused on detailed problem statements or a limited field of application. The current state of research regarding product modularity shows that the modularity of a product system and its hierarchical architecture is a well-established concept. Many authors have built their concepts on the knowledge of interdependencies between modules which occur when a change to a module affects other modules. These interdependencies have been categorized into different types of interaction (e.g. distance modularity or bridge modularity) and visualized (e.g. by using liaison graphs). Due to this information, the most centralized modules as well as the modules that show no or little interaction with the rest of the product system are identified. Based on the concept of modular interactions, many approaches have been developed to consider these interdependencies in the product architecture to minimize the mutual influences by designing products with clearly separated modules for each function. The main driver behind this research is the optimization of product families, remanufacturing or easier upgradability. The concept of product modularity has been transferred to LCAs for product systems by previous authors. Such modular LCAs help to simplify and speed up the calculation of LCAs for extensive product variants while still leading to results that are in line with the ISO norm 14,040. The individual assessment of modules and module alternatives and the following recombination leads to fewer required LCAs than possible module combinations exist. This, in return, allows product developers to compare large numbers of design alternatives with a reduced effort in data analysis and calculation time. The challenges of modular LCAs are described as the correct separation of the product system into modules, the right allocation of material or energy flows to the modules and the

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handling of technically illogical module combinations. The principle of modular LCAs is especially suitable for products with a high modularity and no or little interdependencies between modules. However, when interdependencies between modules become more relevant, a simple application of modular LCAs leads to wrong results. Currently only very few methods are published that deal with module interdependencies in modular LCAs. The further development of modular LCAs and their LCCs has been described by various authors, often going into different directions regarding their approaches and targets. The optimization of product fleets focuses on dynamic fleet models with the target to identify the ideal product composition, but without considering extremely large numbers of variants, interdependencies, or improvements for product development. For single product systems, often multi-objective algorithms are used to optimize the product configuration. Optimization using the graph theory has been introduced by only a few authors. Use cases that provide large numbers of possible configuration alternatives are either structured with tables using unique ID numbers for the variants, module-product matrices, or decision trees. The consideration of interdependencies only occurs occasionally. The overview of the current research shows that the general principles of product modularity have been included into the calculation of LCAs while the interdependencies between modules and their influence on the result are yet rarely incorporated into LCA modelling. Thus, these aspects do not find sufficient recognition during the optimization of the eco-efficiency or eco-effectiveness for products and product fleets. Evaluation of identified publications The analyzed approaches in the identified publications need to be assessed in terms of the formulated criteria and challenges to the problem statement of this work (see section 3.1) in order to determine the suitability of each approach to the introduced criteria. This assessment is used to evaluate which aspects have already been discussed and which have not yet been covered in the available methodologies. Table 3.1 summarizes the criteria and challenges for the evaluation that are introduced in section 3.1. The comparison of the criteria with the identified approaches is evaluated using a scale of three possible outcomes: either the given approach fulfills the considered criteria completely (●), covers the criteria, but does not fulfill all requirements ( ( )) or does not consider these criteria at all (◯). The overall suitability of an approach to all criteria of this study’s problem statement is then indicated in five categories, ranging from a total suitability (●) in 25% steps down to no suitability at all (◯).

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Table 3.2 gives an overview of the evaluation of the publications for the optimization of LCA and LCC of product systems regarding the criteria of Table 3.1. The selected studies that are benchmarked against the criteria are separated into two different types comprising either selected representative studies that focus on the optimization of a single product system or on the optimization of a product fleet. As some approaches by different authors resemble each other, one author is selected in table 3.2 to represent all approaches that are based on the same principles. The twelve criteria are also grouped as they form three different subtypes of required aspects. These subtypes are the criteria regarding the product system architecture and its modularity, the criteria regarding the specific aspects of the use case and problem statement and the criteria concerning the type and functionality of the applied optimization algorithm. Table 3.1 Criteria for optimization of eco-effectiveness for product systems Requirements from product system architecture C1

Availability of individual product modules and module alternatives

C2

Avoiding undesired module combinations

C3

LCA and LCC results for each product module

C4

Consideration of interdependencies between combined modules

Requirements from problem statement and use case C5

Consideration of production processes or infrastructure for optimization of product system(s)

C6

Optimization of a single product system or multiple units of the product system

C7

Consideration of possible limitations regarding the availability of modules

C8

Consideration of the impact from time-dependent price developments

Requirements for optimization approach C9

Enabling absolute financial or environmental targets as optimization objective

C10

Handling a large number of product alternatives

C11

Enabling the consideration of secondary constraints regarding different life cycle stages

C12

High solution quality, quick runtime and appropriate visualization of problem and results

For the criteria “product system architecture”, all studies that focus on single product optimization cover most relevant aspects. Especially the consideration of modular products is implemented in every study. Also, the criteria with regard

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to avoiding unwanted module combinations and both LCA and LCC calculation is broadly represented. Still, not all studies apply their methods to LCC (e.g. Nadoveza et al. (2013), who only focus on LCA optimization). Only the aspect of interdependencies between modules and their influence on the LCA and LCC results has hardly or only partly been covered. The exception is the publication by Steubing et al. (2016), who also cover this aspect. The three selected studies that represent the research for the optimization of product fleets, however, are very different regarding the criteria for product system modularity. As these studies focus on a different application, product modularity, recombination of modules and occurring interactions are not covered by these methodologies at all. The difference between the studies for single or multiple-product optimization continues for the criteria of required application for multiple products. The studies that focus on just those aspects fully or at least partly cover all required aspects. The adaption of their optimization models is explicitly designed to integrate multiple products into their calculation. Furthermore, they offer the theoretical opportunity to expand these models to also include aspects such as infrastructure or production processes as well as module limitations. In comparison to these approaches, the single product focused methods are less focused on these criteria. A more diverse result is found in the analysis of the applied optimization algorithms. For all studies that consider financial and environmental aspects in general, switching between the objective values is possible. As most methodologies rely on multi-objective optimization, different weightings between the dimensions can easily be selected. Theoretically, those algorithms can also incorporate absolute threshold values for the financial or environmental aspects that must not be surpassed, enabling an optimization of not only eco-efficiency but also of ecoeffectiveness. The handling of many possible module combinations (for a single product unit) and the combination of many product units into a fleet (multiple products), is generally possible with the chosen types of algorithms as they can search and optimize large numbers of options. The limitation of the number of alternatives is more often set by the type of data storage and data provision to the algorithm. The data management of decision trees or graph theory networks on the contrary is more flexible than the module-product matrices. Additional constraints, e.g. simultaneously staying below limit values in multiple life cycle stages, have not been considered in the present studies. Theoretically, those constraints can be integrated into the approaches that allow assessments not only for the whole product life cycle, but also for the individual life cycle stages. The easiest way to include such constraints is done by approaches that use graph theory or methods with the focus on multiple products because they also consider use stage emissions from e.g. vehicles. The suitability of the applied optimization

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Table 3.2 Evaluation of published approaches for LCA and LCC optimization

algorithm is evaluated considering the provided results and their visualization. As most algorithms are based on linear programming, the results are plain numbers. The methods based on graph theory, however, express the results as the shortest paths through networks, enabling a visualization of the results more easily.

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Identified research gap The analysis in Table 3.2 shows that the selected authors have contributed important aspects to the general goal of the optimization of a product’s eco-efficiency and eco-effectiveness. The most promising approaches have been introduced by Jahandideh et al. (2015) and Steubing et al. (2016) as both approaches consider the most important and complex aspects regarding the introduced criteria. Still, these two approaches lack either the integration of interdependencies between modules (Jahandideh et al. (2015)) or the consideration of financial aspects (Steubing et al. (2016)). Additionally, both approaches do not cover multiple products and therefore cannot be applied to the ideal composition of product fleets. The analysis of the total fitness of the approaches of each study shown in table 3.2 indicates that, despite the high diversity of methods, there is no approach that incorporates and unites all features that are required to satisfy all the requirements of the introduced criteria. While every author shows a new and suitable approach for selected aspects, none of these approaches fully meets all the required criteria that are fundamental to provide a holistic solution to fulfill all described criteria. To fulfill all criteria at the same time, a new approach is required that covers all fields of interest. Compared to the existing approaches, the following aspects need to be included or strongly improved: • Sub-targets for life cycle stages: When optimizing a product like a vehicle, the emissions of single life cycle stages like e.g. the use stage are relevant, as the use stage itself is also regulated by legislation. Thus, an ideal solution cannot only focus on total life cycle emissions as such a solution might end up with emissions during the use stage that exceed the allowed limit value. Therefore, it must be possible to not only minimize the total life cycle emissions, but also to stay within additional limits for single life cycle stages at the same time. • Module interdependencies: The approach must consider interdependencies between modules to make sure that the effects on the whole product system are included that may occur when changing a single module. • Visualization of results: A proper visualization of the results is helpful in order to be able to integrate the findings into the product development. The visualizations are helpful to understand and explain the identified solution and make the derived knowledge accessible for the user of the approach and further decision makers along the process of product development. • Consideration of time-sensitive influences:

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A dynamic consideration of time-sensitive influences on the life cycle costs must be added to the existing approaches. The variation of energy costs along a use stage is one example of such time-sensitive influences. Considering timesensitive influences assures finding solutions that may not be the most costefficient solution today, but might be in future when the costs of e.g. fossil fuel-based energy sources will strongly increase. • Flexible optimization model: The optimization model must be adaptive with regard to the amount of product units that are included in the optimization process, making it possible to choose if only a single product system is considered or multiple products. Furthermore, when selecting multiple products, it has to be possible to have a flexible number of products entering the model. The user must have the possibility to choose how many product systems they want to include, ranging from a minimum of two units to up to multiple million product units. • Data management concept: For all those described new aspects, a suitable data management concept is required that organizes all data input and provides all relevant information to the optimization model. Such a data management must be flexible regarding changing input parameters, such as varying energy prices or varying CO2 eq. per kWh electricity. The data management system must also have an interface with the optimization algorithm to transfer the input data into the simulation. In conclusion, a new approach is required that combines the strengths of the methods for single and multiple- product optimization. This approach needs to be extended by a consideration of module interdependencies, module limitations, time-dependent cost influences, life cycle stage constraints and a comprehensive result visualization of the optimized eco-effectiveness of the product system(s).

4

Concept for the Optimization of Eco-effectiveness of Product Systems

The optimization of eco-effectiveness of a single unit or multiple product system units requires a holistic concept. Such a concept has to be in line with the criteria introduced in Section 3.1 and it also has to cover the process from generating input data, data management and data optimization to the visualization of the results. This concept will be introduced step by step in the following subchapters. First, the requirements based on the identified research gap are defined. Next, the general framework of the methodology is described and followed by the selection of the optimization approach. Afterwards, the concept is explained in terms of network modelling, input data provision, data optimization using graph theory and interpretation of results. As already stated in chapter one, the methodology is closely developed to particularly meet the specific requirements of the automotive industry.

4.1

Concept Requirements

In Section 3.1, criteria and requirements have been introduced that are required for a holistic solution approach for the optimization of a product system’s ecoeffectiveness. Section 3.3 describes the current research gap, where multiple aspects have been identified that have to be improved to result in an approach that allows a comprehensive optimization of product systems regarding eco-effective targets. Those criteria need to be transferred to specific requirements (R):

© The Author(s), under exclusive license to Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2022 C. D. Gabrisch, Eco-Effectiveness of Modular Products and Fleets within the Automotive Industry, AutoUni – Schriftenreihe 164, https://doi.org/10.1007/978-3-658-40594-6_4

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• R1: The entire optimization approach consisting of product system modelling, data management and mathematical optimization has to be constructed around the concept of modular product architectures. These modular products have to be separable and reconfigurable with alternatives in all technically possible ways to ensure all valid options to be considered. • R2: The approach does not only have to model the product system as a modular architecture, but it also has to be able to move through this modular structure in an intelligent way. That means that although multiple options per desired function are available, only one is selected. And it is also required that the algorithm does not only select a module, but remembers which module exactly has been chosen for each desired function for the entire product system structure. This is fundamental as interdependencies can occur when changing modules. • R3: The modular product structure has to be modelled in a flexible way. It has to be possible to add or replace single module alternatives or entire module families without making the entire model invalid. • R4: Every available choice from all module alternatives for the optimization algorithm has to be characterized individually with attributes that are only linked to that specific module. These attributes include the environmental impact (GWP) calculated by an LCA, the costs (e) of the TCO calculated by LCC and the availability (%) of this module for being included into a product fleet. This information has to be fixed to the module they describe, but it also has to be flexible in order to be changed if influences from interdependencies occur or if time-dependent influences such as varying costs arise. • R5: The approach has to be completely flexible regarding the amount of product systems that are to be optimized. The variety of product systems ranges from a single product unit to a fleet of product systems with millions of individual units. When optimizing product system fleets, it is required to not only provide average solutions for the entire fleet, but also individual results for every single product unit within the fleet. • R6: The optimization approach needs to be flexible regarding the targeted value that shall be optimized. It has to be possible to switch between an optimized (reduced) GWP while meeting a financial limit or reducing the costs while meeting an environmental limit. Both dimensions have to be available for optimization to provide ideal decision support for many applications in product development. • R7: The optimization approach has to be set up in a modular way, so that not only results for entire product systems are calculated, but also for smaller segments of the product system. This is required to e.g. represent different life

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cycle stages, such as only the production or the use stage. It has to be possible to reach a target value over the entire product system while meeting sub-targets for selected product system sections. In this way, e.g. a fleet emission value over the entire life cycle is calculated while meeting emission regulations during the use stage for vehicles. • R8: The results that are created by the algorithm have to be interpretable and visualized to assist the user in the decision-making process. The results have to be available as fleet averages, but also for every single product unit individually to be processed for visualization. The choices made by the algorithm have to be presentable in a comprehensible way by showing each selection for every module in the structure network of the product system. It has to be understandable how each product unit within a fleet is constructed and how the fleet in total performs. Considering individual data sets for every product unit, the results also need to be plottable in a scatter plot to visualize the ratio of costs to GWP for every product unit and also the fleet average. When visualizing all possible combinations of module alternatives, the efficient frontier can be used to quickly identify the ideal product configuration for single product optimization.

4.2

Framework for the Optimization Concept

A framework is required that describes the linkages between the individual steps of product system modelling, data gathering and optimization to incorporate all these requirements into a coherent approach. In the following sections, the general framework is introduced and the selected optimization approach described.

4.2.1

General Framework

The high number of possible product system configurations arising from modular products requires modular input data of LCA and LCC and a modular optimization. For such product systems, optimization has to be possible for single or multiple product units and consider the possible interdependencies between components and modules. This optimization needs an overarching framework that connects the individual aspects to a workflow that covers all the aspects and criteria that are introduced in Section 4.1. The framework for the developed approach in this work is shown in figure 4.1. That framework also defines the structure of

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this chapter, as the following subchapters are based on the introduced steps of the framework. The framework describes five required and consecutive steps that altogether form the optimization concept. The five steps include (1) the definition of the product system and its possible variations due to alternative modules and measures, (2) the generation of the combination specific LCA and LCC data, (3) the arrangement and management of this specific data into the modular product system structure. Finally, these data are transferred into the optimization algorithm (4). The results are then displayed and interpreted to provide decision support for the user (5). All those phases consider the three life cycle stages: production, use and end of life. The first step defines the technical specifications of the product. In this step, the decision is made whether a single or a multiple product optimization is performed. After this decision, the possible module alternatives, additional measures, and their availability need to be defined. Possible options are e.g. alternative materials or material sources, alternative energy supplies for the production or use stage or physical changes to the product like a change in the geometry. As a result of this information, the structure of the product and product alternatives is defined. Section one of figure 4.1 shows the configuration of the product system and the conversion of the product and the selected measures into a product system structure along the entire life cycle. This product system structure includes alternatives for each choice that represent the available measures. This step is required to fulfil the requirements R1 to R3. The first step of the framework is described in more detail in subchapter 4.3. The second step builds on the results of step one. Considering the defined alternatives of the product configuration at hand, the life cycle performance is calculated. The LCA and LCC results for the individual alternatives are calculated at first as described in Section 2.2 and then recombined according to the defined product configurations. Section two of figure 4.1 shows how the LCA and LCC results are assigned to the alternatives of the product system structure. This step is required to fulfil the requirement R4 and it is described in more detail in subchapter 4.4. The third step organizes the configuration-specific information of the individual product system option into a database where the information is structured along the hierarchical setup of the product. The data structure is designed in a way that the individual LCAs and LCCs of each alternative are linked to the represented life cycle allowing the optimization algorithm to directly take this information as input data. The results now have to include the individual influences of the alternative interdependencies and the financial effects of the time

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span of the use stage that occur due to varying prices for e.g. fuels over time. The data management is explained in detail in Section 4.4. The third section of figure 4.1 shows how the individual LCA and LCC results are linked with each path through the network. This step is required to fulfil the requirements R2 and R4 and also described in more detail in subchapter 4.4. The fourth step is the optimization of the input data to identify the ideal combination of alternatives to result in the product system with the required ecoeffectiveness. The optimization algorithm detects the shortest path of e.g. the costs through the products systems alternatives while not exceeding an environmental threshold value. This shortest path through the product systems network represents the recommended alternative combination. The optimization algorithm is described in Section 4.5 and visualized in section four of figure 4.1. This step is required to fulfil the requirements R5 to R7 and is described in more detail in subchapter 4.5. After the results for the ideal product configuration are calculated, the results need to be displayed and visualized in order to be interpreted by the user. The results have to be exported and converted to represent the network structure of the product system or a graph that shows the ratio of emissions to costs. This is provided in the fifth step of figure 4.1 and further described in Section 4.6. This step is required to fulfil the requirement R8. It is also described in more detail in subchapter 4.6. The user of the framework requires a user interface that provides relevant information, display of results and setting options to initiate the optimization of the product system and to support the decision making during the product development. Figure 4.2 shows the minimum requirements for such a user interface with the four steps of (1) definition of the optimization objective, (2) configuration of the product system, (3) input data and definition of the financial time-perspective and (4) calculation and visualization of the results. The detailed walkthrough through the user interface and the required steps towards the optimization of product systems are explained in an exemplary application cycle in Section 5.2.

4.2.2

Selection of Graph Theory as Optimization Approach

LCAs and LCCs are descriptive calculations that represent the status quo of the analyzed product system. However, both LCA and LCC do neither lead to optimized results nor in the ideal product configuration (Cerri et al. 2014). Using the results of LCA and LCC for the optimization of eco-effectiveness requires the

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I: Definion of the opmizaon objecve • Definion of the opmizaon objecve (CO 2 or costs) • Definion of the opmizaon threshold value (e.g., 10 tons of CO 2 or 500 €)

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II: Configuraon of the product system • Configuraon of the product system and its technical specificaons • Selecon of single product or mulple product opmizaon and fleet size

III: Input data and definion of the financial me-perspecve • Calculaon and provision of LCA & LCC input data • Selecon of relevant price development scenario for raw material • Selecon of relevant price development scenario for energy and fuels

IV: Calculaon and visualizaon of the results • Calculaon of ideal product configuraon and numerical export of selected modules and the resulng environmental impacts and life cycle costs

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Figure 4.2 Functionality of the user interface

interpretation of the given results and the application of a suitable optimization method to the results of the LCA and the LCC. By means of an optimization approach that combines both results, the ideal product system configuration for a desired eco-effectiveness can be identified. As the goal of this approach is the calculation a most eco-effective product system with the highest eco-efficiency, the optimization of only one target value (GHG emissions or costs) is required. The algorithm is either supposed to find the most cost-efficient way to reach the target value of the environmental goal or to identify the highest possible environmental benefit for a limited budget. From all possible product system configurations, the ideal solution has to be found. A special form of modelling problems is offered by the principles of graph theory. Using the graph theory, many different problem types can be modelled and solved by suitable algorithms. The advantages of the graph theory, such as flexible graph design, avoiding of unwanted connections between vertices and visualization of the results, offer a wide range of possible use cases. The choice of the graph theory is also based on the broad availability of adaptable algorithms and the ability to create solutions in a polynomial runtime. As shown in the previous subchapters, the product system’s architecture can be modelled as a network considering the possibly occurring interdependencies. Those networks can directly be applied to a graph searching algorithm. The modelling of networks and the optimization using the graph theory allows a clear problem structure and clearly defined module combination options for the algorithm to choose from.

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By linking multiple nodes or vertices (V ) with (weighted) edges or arrows (E), a graph (G) is created that defines and visualizes complex relations between the vertices. Every element of E has exactly one assigned pair of vertices i and j coming from V . If a graph consists of edges that each connect two vertices in both directions, it is an undirected graph. If the edges link two vertices with a defined direction, the edges are called arrows, making the undirected graph a directed graph. If the arrows or edges of a graph are assessed with an individual quantification, the graph is called a weighted graph. (Domschke, Drexl, Klein & Scholl 2015 p. 72 ff.) The weight of an arrow or an edge can e.g. be the financial costs of linking two nodes. An induced subgraph (H) of the original graph (G) is a graph H where the vertices (V (H)) and the edges (E(H)) all are elements of (V (G)) and (E(G)) (Korte & Vygen 2018, p. 16). Figure 4.3 shows an undirected graph (G) and an induced subgraph of G(H). Undirected & induced subgraph (H)

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For a given directed or undirected graph it is possible to calculate the shortest paths between two vertices with the established algorithms by e.g. Dijkstra or Moore-Bellman-Ford. The algorithms find the shortest path of connected edges or arrows from a defined starting node s to a desired target node v. . (Korte & Vygen 2018, p. 168ff) Figure 4.4 shows a weighted and directed graph (G) and the path from node 1 to node 6 with the least total weight on all arrows (right network). This path covers all nodes and results in a total weight of 110, hence, being the “shortest” path compared to all other possible routes. The algorithm of Dijkstra runs with a time complexity of O(m + n log n) and is solvable in a polynomial time (Korte & Vygen 2018, p. 170). A graph (G) is called a network if two nodes of a directed graph are defined as a source (s) and a sink (t) and each arrow (in networks also called arcs) has a limited capacity (c). A flow through this network begins at the source (s) and follows the arcs to the sink (t) without exceeding the given capacity of each

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arc. (Korte & Vygen 2018, p. 185 ff.) Building on the definition of networks in graph theory, network flow problems of different types can be modelled and solved. The Minimum-Cost Flow Problem (MCFP) is one prominent example. The MCFP describes a problem in which the flow through a network needs to be identified that goes from source (s) to sink (t) with the least financial costs without exceeding the arcs capacities. The costs are expressed as the weights of each arc. (Korte & Vygen 2018, p. 227 ff.) Figure 4.5 shows an exemplary network for a MCFP.

Network N = (G, c, s, t) (35/100) Costs = 35 Capacity = 100

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The mathematical notation of the MCFP for a network like the one shown in figure 4.5 is shown in equation (4.1) and (4.2). The flow through the network is represented by f , the edges of the graph (G) are represented by e (first edge) and E (last edge). The maximum capacity of an edge is represented by c.

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Minimi ze costs( f ) =

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While equation (4.1) is the objective function of minimizing the total costs for the network flow, equation (4.2) limits the individual flows for each edge to its maximum capacity. For the MCFP, there are different algorithms (e.g. networksimplex algorithm) that enable the calculation of a solution in polynomial time (Korte & Vygen 2018, p. 245 ff.). Given defined arcs between the nodes, the developer defines which connections and thus combinations are possible. Illogical or technically unfeasible module combinations (e.g. two modules from the same category) can be forbidden by leaving out such arcs within the network. Given the defined module hierarchy, it can also be avoided that certain node choices are skipped during the optimization. The structure of the network forces the algorithm to make exactly one choice per node option on its path, avoiding shortened or incomplete product systems that result in more cost-efficient solutions or solutions with less GHG emissions (e.g. leaving out the engine of a vehicle to reduce GHG emissions and costs). Given the target of optimizing the cost-efficiency for an absolute emission limit of GHG, the optimization regarding a single objective is sufficient. Therefore, a multi-criteria approach that requires weighting factors between multiple objectives is not required for the optimization. Due to the concept of modelling flows through networks, not only single product units can be optimized, but also product fleets. An additional restriction lets a defined number of units enter the network and forces the algorithm to have this number of units also exiting the network. This creates a fixed flow of multiple units through the network. In this case, the optimization goal is to find the ideal flow for all product units so that the total costs for all product units in total is minimized. Building on the flow of multiple product units through the network, limited carrying capacities of arcs within a network can be used to limit the availability of single modules. Without limiting the capacity of available modules by limiting the capacity of the arcs that lead to these modules, the algorithm is free to use as many modules of every type as it requires for the ideal solution. This can be applied to modules or options within the product system that cannot be included in all product unit paths. One example is a limited supply of green

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electricity for the production or the use stage of a product system. Given that, not every product unit can receive this green electricity supply and the algorithm needs to identify the product unit configurations that profit the most from the green energy source and connect the corresponding modules in this way. The flexibility of the graph design also allows the incorporation of further nodes that are integrated into the network or into individual paths to represent possible additional aspects for the respective affected nodes. This can be used e.g. to consider additional investments that are required before a certain technology can be implemented. Given an additional node, the cost structure of a module node does not need to include the investment costs but can remain on the level of costs per product unit. An example is shown in figure 4.6; the paths that lead to node D3 first have to flow through the newly added Invest node before reaching node D3 . In view of a node that represents the required investments for the following module being incorporated into the network, the optimization of a single product unit does not include node D3 in the ideal solution due to high investments for only one product unit. However, if multiple product units enter the network, the investment that is required to unlock the invest node before node D3 can possibly be compensated by the savings of now being able to choose node D3 .

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Consequently, additional investment nodes act as threshold values that require a minimum number of product units that are available to include node D3 into their optimized pathway through the network to make up for the high investment costs. Considering such an approach of modelling additional costs like investments that are required for individual modules, aspects like infrastructures or new factories for new technologies can be included into the model.

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Given these advantages, the optimization using the graph theory offers several options to identify the ideal solution regarding various flexible framing conditions. The identification of the ideal path through the network with the weighted arcs can be transferred to the search for the shortest path through a network. The shortest path for e.g. a given emission limit results in the shortest path regarding the costs of the product configuration. When the optimization objective is switched from emissions to costs, the shortest path for a given financial budget results in the product configuration that offers the minimal path of GHG emissions. Due to clearly defined modules and the individual use stage that belongs to each possible product configuration, it is possible to add further constraints to the solution space regarding threshold values for selected life cycle stages. For many product types it is relevant to not only contribute to sustainability by reaching emission levels along the entire life cycle, but also being below limiting values e.g. for the production stage or during the use stage due to political regulations. Figure 4.7 shows an example of a product system with a limit of 100 kg of CO2 eq. over the entire life cycle. The solution with the lowest GWP in this example is product system A which emits 30 kg of CO2 eq. during the production, 50 kg of CO2 eq. during the use stage and 20 kg of CO2 eq. during the end of life stage. The product system A consequently complies with the life cycle limit of maximum 100 kg of CO2 eq. If an additional limitation, e.g. for the use stage, is added, the algorithm possibly has to find a new solution which meets both limitations. If such a new limitation for the use stage is added which allows a maximum of 30 kg of CO2 eq. for this stage, product system A is not a valid solution anymore. In such a case, the algorithm has to switch to product system B, which is more expensive in this example but complies with both emission limitations at the same time. A focus on only the use stage, however, leads to missing absolute sustainability goals, as other life cycle stages still emit GHG. This makes it a requirement to meet limits for selected life cycle stages (e.g. use stage) and limits for the entire life cycle at the same time. Due to the modular design of the network, such additional emission limits for individual life cycle stages can be integrated into the solution identification. Furthermore, the results of the shortest path identification can easily be visualized in the already modelled product system network. In view of these characteristics, an optimization algorithm based on graph theory offers a solution approach that covers the stated requirements to an approach that were introduced in Section 3.1 and Section 4.1.

4.3 Modelling the Product System’s Structure in a Network

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4.3

Modelling the Product System’s Structure in a Network

This subchapter describes step one of the framework depicted in figure 4.1. Here, it is shown how the product system is modelled as a network considering interdependencies between components or modules. As the incorporation of interdependencies leads to a strong growth of the network, reduction strategies are introduced to reduce the demand of required nodes to model the product system. The procedure of structuring and mapping the product system’s architecture into a network that represents the possible combinations of modules and components with respect to the technical limitations of certain combinations forms the basis for the data generation.

4.3.1

Transformation of Modular Product Systems into Networks

Figure 2.3 in Section 2.1 introduced the architecture of modular product units and figure 2.6 showed the evolution to fleets of modular product systems. These

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depictions, based on the life cycle perspective and the product architecture, are suitable to explain the general structure of a product system, but are not applicable to graph theory algorithms. The foundation for the optimization framework is built by the possible product system configurations that form the options and thus the search space from which the algorithm combines the best options to the final product system. These product system configurations in return are defined by the available components and modules that can be used to assemble the product. The product system architecture and the adjunct life cycle stages need to be transferred into a network structure to be able to apply an optimization algorithm to the product system. Figure 4.8 shows how a simple modular product unit is transformed into a network structure. The product’s architecture structure is defined with a directed graph, where each alternative for the modules is represented with an individual node. The exemplary modular product structure in figure 4.8 is built from four different modules (module A, module B, module C and module D). Each module forms its own “module category” or “module family” that provides a unique function to the product. While module A of this example only offers one alternative within its module category, module category B offers two alternatives B1 and B2 with the same functionality. Module category C offers three different alternatives to choose from (C1 , C2 and C3 ) and module category D again provides two alternatives (D1 and D2 ). Given such a network structure for the product, the connecting arrows are weighted with e.g. costs or GWP that are associated with each module. This set-up allows shortest path algorithms to be applied. The network has to be followed from module A to module D along the direction of the connecting arrows to build an entire product unit. For every module category, only one, but also always a minimum of one alternative, has to be chosen. The grey highlighted modules in figure 4.8 show one possible way of module choices along the network to result in one product unit. In this work, the terms “module alternatives”, “module variants” and “module options” are used synonymously to describe alternatives for modules with the same functionality that come from the same module category. Further nodes and node categories need to be included to expand this network of a product unit to an entire product system including the life cycle perspective. These additional nodes represent alternative choices for adjunct processes regarding the production stage or the end of life stage. Figure 4.9 shows the evolution of the network in figure 4.8 into a network that represents not only the product unit, but also exemplary life cycle stages with their own node categories and alternatives. The product unit is embedded in adjunct choices for supportive

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functions. Such a network that represents the modular product system will further be referred to as a “product system structure network”. This network adds complexity by including additional life cycle stages via new node categories and by including the product architecture level of components into the modules. The lowest architecture level of parts is not included to reduce complexity for this work and for the visualization of the networks.

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Figure 4.9 shows an exemplary product system structure consisting of two different modules (module A and module B). These two modules are complemented by an upstream category for the production stage (P1 ) and a downstream category for the end of life processes (EoL1 ). Within the product unit, both module categories A and B of the product unit consist of two alternative modules (A1 , A2 and B1 , B2 ) which enables alternative paths for the product configuration resulting in four different options for the product system design (P1 –A1 –B1 –EoL1 , P1 –A1 – B2 – EoL1 , P1 –A2 –B1 –EoL1 and P1 –A2 –B2 –EoL1 ). On a deeper level into the product unit structure, module A and module B consist of three components (A, B and C). Each component has a total of three alternatives available (A1 , A2 & A3 | B1 , B2 & B3 | C1 , C2 & C3 ). For each module this offers nine different components that are structured in three different component categories. From every available component category, one option has to be selected.

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On the level of components, the options cannot be combined freely. The components A1 and A2 can be combined in all manners with B1 and B2 and these components again with C1 and C2 , which leads to eight alternative combinations between these six components. The components A3 , B3 , and C3, in contrast, form an exception as these components can only be combined as a trio due to exemplary technical limitation that excludes them from the other options. The arrows in the network show only one option for each of these components to represent these limited combinatorial options. Each entire module can be built in nine different configurations. The production stage and the end of life stage also offer nine alternatives with the same combinatorial logic as the product unit’s modules. For the production stage and end of life stage, three alternatives for the location (L1 to L3 ), three alternatives for the electricity mix (E1 to E3 ) and three alternatives for different machines (M1 to M3 ) can be used. Transferred to the automotive industry, the module category A can e.g. be the choice of powertrain, with module A1 being a combustion engine-based powertrain and module A2 an electric powertrain with a high-voltage battery for electricity storage. Module category B can represent different alternatives for the car body, with module B1 being a conventional, steel-based car body and module B2 a lightweight construction with a higher share of aluminum. The four product system categories with each two alternatives for module A and module B of the product unit offer a total of 54 alternatives from which twelve alternatives (three alternatives within the four categories) are part of the final product system. The grey highlighted nodes in figure 4.9 show one possible way of the option selection within the product system structure network. Providing nine options for the configuration of each the production and end of life stage and also for all four module alternatives, the network shown in figure 4.9 offers a total of 26, 244 different options of combinations to create a complete product system.

4.3.2

Product System Networks Including Interdependencies

Based on the principle of modular LCAs and modular LCCs, individual assessments for the 54 alternatives of figure 4.9 are sufficient to analyze all those 26,244 possibilities as the rest of the required information for the entire product system can be derived from the 54 initial results. This approach meets its limit when interdependencies between modules or components influence the performance, so that static LCAs or LCCs for modules do not apply to every variation of module combination. As the LCA or LCC of a single component or module can possibly

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module B2: lightweight car body

Comp. CA2

Comp. CC2

module A2: electric powertrain

Comp. CB2

Comp. CA2

Comp. CA1

module B1: conv. car body

Comp. CC1

Figure 4.9 Product system structure network of a modular product system

production category

Electr. E1

Loc. L1

production stage P1

Comp. CB1

Comp. CA1

module A1: combustion engine

modular vehicle

modular product system structure network

Electr. E2 Electr. E3

Loc. L2 Loc. L3

Mach. M3

Mach. M2

Mach. M1

end of life category

Electr. E1

Loc. L1

end of life stage EoL1

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change if the module is combined with another alternative, the sum of the static LCA values and LCC values results can differ from the value that arises when including interdependencies. The interdependencies need to be modelled directly into the product system’s structure network to overcome the limitations of regular modular assessments and to include the effects of interdependencies into the calculation. Modelling general interdependencies into networks Interdependencies between modules can change the performance of other modules and thus the specifications of the whole product system. The way how the interdependencies affect the modules of a product system can be different for every case. Some interactions may only relate to one module, some may influence all involved modules and sometimes it is possible that a change of a module does not cause any interdependencies at all. In a worst-case scenario, interdependencies occur between every module of the product system. If a product unit consists of only two modules (represented by node A and node B in figure 4.10) and each module offers two alternatives (represented by node A1 , node A2 , node B1 , node B2 ), the structure network of this product system looks like Network A in figure 4.10. The exemplary selected design alternative for this product is node A2 combined with node B1 , as highlighted in grey. If interdependencies exist between all design options and the performance of the whole product system depends on the exact combination of the modules, the results for the LCC and LCA of e.g. node A1 differs when it is combined with either node B1 or node B2 . For node A1, two different states exist, one for each possible combination it is part of. This logic also applies to node A2 , node B1 and node B2 . Network A II in figure 4.10 shows how a network is modelled that incorporates the different states of each node. For every node, two different “subnodes” exist. Each subnode is connected to only one specific node of the other class to indicate the combination partner that it is exclusively meant for. The required number of nodes in the network increases from four to eight to fully represent the interdependencies between nodes, while the number of connecting arrows does not change with four remaining edges. Also, the number of possible combinations remains at four options. The selected combination of nodes in Network A II is therefore clearly defined given node A2–1 and node B1–2 . Transferring the principle of incorporating interdependencies into the exemplary network design of Network B leads to an enormous growth of the number of nodes, assuming interdependencies are occurring within every configuration alternative and for every node. Network B offers a total of 729 different combination possibilities. One alternative from each node category (A to F) has to be selected to create a

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complete product system. Consequently, node A1 needs to be connected with five more nodes. As each of the five remaining category offers three choices, node A1 can be matched with 243 different following configurations. This leads to a required split up of node A1 into subnodes ranging from node A1–1 to node A1–243 and following that logic to the same split up for all other 17 nodes of this network. The number of required nodes thus increases from 18 nodes to 4, 374 nodes. Based on these 4,374 nodes, the complete network can be represented with respect to possible interdependencies between all nodes.

Network A II

Network A Node A1

Node B1

Node A2

Node B2

Node category A

Node A11

Node B11

Node A12

Node B12

Node A21

Node B21

Node A22

Node B22

Subnodes for Node B1

Network B Node A1

Node B1

Node C1

Node D1

Node E1

Node F1

Node A2

Node B2

Node C2

Node D2

Node E2

Node F2

Node A3

Node B3

Node C3

Node D3

Node E3

Node F3

Figure 4.10 Structure networks with interdependencies

The strong growth of required nodes to represent the interdependencies leads to an increase in data demand as every node has to be assessed individually to be used in a modular analysis and optimization. Without interdependencies, the

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product system of Network B and all 729 options can be assessed by modular LCA and LCC by assessing the 18 different nodes individually and thereby reducing the effort to only 2.5% compared to assessing all 729 options directly. Considering the expanded network though, the effort resulting from a modular approach rises to 600% as then 4,374 instead of 729 assessments are required. Instead of simplifying the assessments, the modular approach including interdependencies makes it more complicated as the number of nodes surpasses the number of possible initial node combinations. The product system structure in figure 4.9 quickly becomes too big to depict if all components had possible interdependencies with each other. This leads to a data demand that is too large to be calculated. Strategies are needed that help to reduce the effort of modular LCC and LCA and end up below the threshold of the possible module combinations (in this case below 729). Such strategies help to still be able to use the modular product approach for the assessment of the life cycle performance and for the product optimizations. (Gabrisch, Cerdas & Herrmann 2019)

4.3.3

Network Reduction Strategies for Interdependency Modelling

The reduction of the effort of data calculation, network modelling and optimization can be achieved by applying network reduction strategies to the model of the product structure to reduce the number of nodes. The number of required unique nodes to represent an interdependency-sensitive product structure network is calculated using equation 4.3. The variable N represents the number of required individual nodes, y represents the number of node categories and nx the number of alternatives for each node category. The number of required nodes depends on the number of alternatives per node category and on the number of node categories.   N = y ∗ n1 ∗ n2 ∗ . . . ∗ n y

(4.3)

The number of node categories defines the “length” of the product system’s structure. The length of the product structure network does not influence the solution quality as it simply defines the product system’s required functionality and design. The number of alternative options per node category, however, is a generally

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desired state, as more options lead to a bigger search space for the optimization and possibly to better results. Reduction via network partitioning As shown in equation 4.3, a “long” structure network with many connected node categories increases the need for individual nodes, represented by the factor y. Therefore, shortening the length of the structure network directly reduces the node demand. A shortening of the structure network can be achieved by partitioning the complete structure network into “subnetworks” or “substructures”. With such a split, two separated subnetworks are created, both having fewer node categories to calculate with. This reduced number of node categories then reduces the number of required individual nodes to model the interdependencies. The two subnetworks are then connected with a single connecting node serving as a bridge between the subnetworks. However, such a network partition can only be done when it can be guaranteed that there are no interdependencies occurring between nodes of different subnetworks. Interdependencies are only considered within each subnetwork, but not between them. This can be applied when (referring to figure 4.9) e.g. the production stage of a vehicle has interdependencies between the choice of machines and the selected energy mix, but none of the production stage nodes has interdependencies with the following use stage. Then, this network can be partitioned into a subnetwork for the production stage with its internal interdependencies and a subnetwork for the use stage with again its own internal interdependencies. However, now no nodes are required to model the non-existing interdependencies between the production stage and the use stage. A theoretical split of the product structure network of Network B in figure 4.10 into two separate chains with each having three connected node categories (see figure 4.11) leads now to only 8 1 individual nodes both for structure I and structure II (Gabrisch, Cerdas & Herrmann 2019). In this scenario, the alternatives of node category A, B and C can now only have interdependencies with each other, but they do not have a direct connection to the nodes of category D, E and F anymore. This means that interdependencies between e.g. node B1 and D1 now are not represented anymore and therefore no nodes are necessary to represent their relation. In sum, a separated structure network of Network B only needs 162 individual nodes to represent the network with interdependencies, reaching a number below the 729 combination options shown before.

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Network B II Node A1

Node B1

Node C1

Node D1

Node E1

Node F1

Node A2

Node B2

Node C2

Node D2

Node E2

Node F2

Node A3

Node B3

Node C3

Node D3

Node E3

Node F3

Structure I

Structure II

Figure 4.11 Network B with a separated structure network

Reduction using network adaption to include a correction factor The introduced reduction of required nodes using network separation only works when interdependencies between several node categories (along the separation) can definitely be excluded. If interdependencies cannot be neglected or if a subnetwork itself is still large, a reduction strategy to decrease the number of nodes is required also within these subnetworks. As shown by equation 4.3, the demand of nodes to model all interdependencies grows quickly if the assumption is made that the interactions occur between all nodes and every node is affected in its own performance by these interactions. Each node requires multiple subnodes for every interaction to represent those interdependencies in the network. Here lies a huge reduction potential. Instead of considering the individual interdependencies between each node pair, the information of the interdependency between all connected nodes can be concentrated and bundled into one single additional node. This newly added node as a newly added node category includes the information for all interactions that occur in a specific product system configuration and adds a “correction factor” at the end of each network path. This is feasible as only the interdependencies of all nodes coming together are relevant and not the interdependencies between two nodes within a product system because the target is to build an entire product system and not a fraction of it. In figure 4.12, this alternative modelling of the network is shown. For a network with three node categories and two alternatives per node like Network C in figure 4.12, the expanded network requires 24 nodes to depict all possible interdependencies (see Network C II). Network C III shows the network that represents the interdependencies with an additional node category N. This type

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of network is designed in a way that each node category does not contain all possible interaction information anymore. Instead, only the individual path through the network is modelled. While a node can have multiple outgoing arrows to the following node category, the incoming arrows always have a unique and clearly defined source. In this model, the network size grows with each category. The first categories in Network C III are relatively small compared to Network C II. The full set of alternative subnodes for interdependencies only occurs in the last node category. An additional fourth category is added at the end of the network to make up for the lost information along the reduced node categories. That new category holds an individual node for each subnode of the previous category (node C), as each subnode of category C stands for a unique pathway through the network. This new network category (N) contains the summarized information of the interdependencies of all nodes within this product system configuration. The selected path of A2 , B1 and C1 results in N2 , where the interdependencies of this configuration are stored. The required number of nodes to create the new network is reduced, as now 22 nodes (Network C III) instead of 24 nodes (Network C II) are needed. This is a reduction of required nodes by 8.33% for this example. For larger networks, the reduction quickly becomes more effective. If Network C would consist of ten node categories (A to J) with each category having two node alternatives, 1, 024 possible combinations existed. Based on equation 4.3, Network C II would need 10,240 nodes to represent all possible interdependencies. Network C III only needs 3,070 nodes to provide the same information as Network C II, resulting in a reduction of 70% of the required nodes. If Network C consisted of ten node categories with three alternatives each (e.g. A1 to A3 ), Network C III would reduce the number of nodes by 75% in comparison to Network C II. The larger the network regarding node categories and number of node alternatives per category becomes, the more effective the node reduction strategy of the additional correction node becomes as well. While the number of nodes for Network C II increases exponentially, the number of nodes for Network C III grows in a linear way. Introducing a correction factor N as an additional category at the end of the network helps to reduce the number of required nodes compared to a full expansion of the network for all nodes and interactions. Still, the number of nodes (and thus potentially the data demand) is higher than the initial number of possible combinations. However, in this type of network modelling, the number of nodes does not represent the required data as the individual subnodes for the node categories do not represent individual data, but only enable the tracking of the unique paths through the network. Hence, e.g. the subnodes C2–1 to C2–4 are four individual subnodes, but they all contain the same information or data, making only one calculation necessary to provide the information for four subnodes.

Node B2

Node A2

Node C2

Node C1 Node B2-1 Node B2-2 Node B2-3 Node B2-4

Node A2-2 Node A2-3 Node A2-4

Node B1-4

Node A2-1

Node A1-4

Node B1-3

Node A1-3

Figure 4.12 Network structure with correction factor

Node B1

Node A1

Network C

Node C1-2

Node B1-2

Node A1-2

Node C2-4

Node C2-3

Node C2-2

Node C2-1

Node C1-4

Node C1-3

Node C1-1

Node B1-1

Node A2

Node A1

Network C III

Node B2-2

Node B2-1

Node B1-2

Node B1-1

Node C2-4

Node C2-3

Node C2-2

Node C2-1

Node C1-4

Node C1-3

Node C1-2

Node C1-1

N8

N7

N6

N5

N4

N3

N2

N1

4

Node A1-1

Network C II

114 Concept for the Optimization of Eco-effectiveness of Product Systems

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4.3.4

115

Strategy Adaption to Reduce the Data Demand of LCA Values and LCC Values

As shown in the previous section, the implementation of possible interdependencies into the network leads to a strong increase in required nodes and input data. Therefore, reduction strategies have been introduced that show how the network size is reduced down to a manageable size. These network-based strategies need to be applied to the product system’s structure network and the modelling of LCC and LCA data to transfer the node reduction into a data reduction. In this subchapter, it is shown that the network reductions also result in a reduction of data demand for individual LCA and LCC assessments. The target is modelling the product system structure on component and module level that includes the interdependencies, but still enables a modular LCA and LCC assessment and a modular optimization. Reduction using network partitioning The reduction strategy of network partitioning reduces the number of nodes by splitting up a network into two subnetworks. By this separation, the two subnetworks become independent from each other. Interactions between the components of the separated networks are no longer considered and therefore do not need to be modelled with specific nodes. Partitioning networks can only be done between components or modules where it is known that no interdependencies occur to avoid losing information of interdependencies. Building on the definitions of modularity for product systems as shown e.g. by Mutingi et al. (2017) (see Section 3.2.2), ideally the liaisons between the components within a module are maximized and the liaisons between modules minimized (Mutingi et al. 2017, p. 473). Based on the assumption that modular products show no interdependencies between the components of one module and the components of another module, the network partitioning can be applied. For structures that show a high modularity, a separation of the whole graph into subgraphs is allowed at the borders of individual modules. However, interdependencies within the components of the same module and also interdependencies between different modules are still possible. Still, the network partitioning along the modules reduces the required data for LCA and LCC. It depends on the number of modules within the structure network and the number of components per module to which extend the data demand is reduced. Reduction by network adaption to include a correction factor The application of the network size reduction strategy that provides a correction factor for each path through the structure network to a real product system is more

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complex. The implementation of a correction factor is built on the idea that all interdependency influences are summarized into one overarching node that represents the entire network path it is part of. The product to be analyzed has to behave in this way as well in order to transfer this form of modelling to the life cycle performance of a product system. This means that all interactions and interdependencies between the components of a module or the modules of a product system are centralized at one point to be quantified and included to the network structure. A solution for the transfer of the correction factor to a product system is the separation of the product’s production and end of life stage from the use stage of the product. Figure 4.13 depicts this separation. The foundation to the incorporation of the correction factor is that the interdependencies that are caused by the changed product configurations of module or component alternatives all interact during the use stage. This means that all changes to e.g. the weight or energy provision of the product system (e.g. a vehicle) are effective during the use stage. During this use stage, all interactions are active at the same time to provide the desired functionality of the product. Before entering the use stage, it is not possible to assess the influence that a reduced weight has on e.g. the choice of the engine or other modules. Therefore, only the use stage is suitable to detect and quantify the occurring interdependencies of all modules. The separation between the production on one side and the use stage on the other side is performed since in order to enter the use stage, the entire product system has to be designed and manufactured completely. This means that for every component and module category a choice has been made. Changes to this product configuration are not possible anymore and the individual use stage of this defined configuration begins with all information regarding this product system configuration being available (e.g. the total weight or the air resistance coefficient). This information helps to assess the financial and environmental impacts of the use stage specifically for each individual product configuration. The separated use stage in figure 4.13 has a unique subnode for every possible product configuration, just as in figure 4.12 every possible path through the network has an own node N which provides the correction factor. This allows the adaption of the use stage module in the LCA and LCC assessment as the correction factor. The correction factor serves as a corrective dimension that balances out the deviation of the added static module results from the true values that belong to the specific combination. For a product system this means that within the network for the production and end of life stage, the information of the individual modules is added up and matched with the corresponding use stage. In this way, the interdependencies do not have to be considered during the production and end of life stage, but only during the use stage. Incorporating the interdependencies into the use stage requires a limitation of these interdependencies to the use stage only.

Electr. E2

Electr. E3

Loc. L2

Loc. L3

Mach. M3

Mach. M2

Mach. M1

Comp. CB3

Comp. CA3

Comp. CA3

Comp. CC3

Comp. CB2 Comp. CB3

Comp. CA2 Comp. CA3

Comp. CC3

Comp. CC2

Comp. CC1

Comp. CA2 Comp. CA3

Comp. CC2 Comp. CC3 Comp. CB3

Comp. CB2

Comp. CB1

Comp. CC3

Comp. CC2

Comp. CC1

module category B

Comp. CA1

Comp. CC1

module category A

Comp. CB1

Comp. CA1

Comp. CB3

Comp. CB2

Comp. CB1

module B2: lightweight car body

Comp. CA2

Comp. CC2

module A2: electric powertrain

Comp. CB2

Comp. CA2

Comp. CA1

Comp. CC1

Figure 4.13 Separation of use stage from product system network

production category

Electr. E1

Loc. L1

production stage P1

Comp. CB1

Comp. CA1

module B1: conv. car body

network I

module A1: combustion engine

modular vehicle

modular product system structure network

Electr. E2 Electr. E3

Loc. L2 Loc. L3

Mach. M3

Mach. M2

Mach. M1

end of life category

Electr. E1

Loc. L1

end of life stage EoL1

+

use phase category

use 26,244

…

use 4

use 3

use 2

use 1

network II

4.3 Modelling the Product System’s Structure in a Network 117

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The selection of an alternative module must not influence the production and end of life stage of another module. Referring to an automotive product system, this means that changing the material of the car body from steel to aluminum influences the use stage of the final vehicle as the weight is reduced. However, the production and end of life treatment of another module, e.g. the engine, is affected by the change in the car body. Therefore, both alternatives are combined with the same engine. Consequently, the financial and environmental impacts of the engine do not change in Network I of figure 4.13. The impact of the changed weight only occurs in the specific use stage for both possible engine and car body combinations (engine + steel, engine + aluminum). By adding more module categories to the product system, the direct interdependencies between car body and engine are now not relevant anymore. The relevant interactions of all modules (e.g. engine, car body, tire and further modules) together define the interactions of all modules of the product system and are represented by the excluded use stage. For a product system that consists of ten modules, the interdependencies between the first and the second module are not relevant. Only the interdependencies that occur when all modules are combined at the same time need to be considered. The change of one module of the product system does not need to be evaluated regarding the new interdependencies between the new module and every other module individually, but only for the whole product system in total. A split up of modules into subnodes for every module and for every possible interdependency with every other module is not required. The only required information regarding the interdependencies is needed for the combined interdependencies of all modules. This information can be combined into one node—the node that contains the correction factor, which is the use stage for vehicles. As the required module information for Network I in figure 4.13 are now all static and not interaction-specific, the information for Network I can be provided with regular LCA and LCC assessments as described in Section 2.2. The dynamic information of the individual influence of each unique product configuration therefore needs to be provided by the data of the use stage in the network. The financial and environmental burdens of the use stage of a vehicle depend on the consumed energy, fuels, and other operating materials during the time of operation. For the calculation of the individual consumption of the required energy and material input for the use stage, the physical properties of each configuration are the defining parameters. Considering the weight, geometry and energy efficiency of the individual modules, each product configuration has individual specifications that influence its energy and material consumption for the use stage of a certain product system. The individual energy and material consumption rates are multiplied by fixed factors regarding financial costs and environmental impacts. For example, the

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demand of electric energy of the product in the entire use stage is multiplied by the price for one kWh of electric energy and an environmental impact factor. Following this approach, each individual product system’s use stage is assessed in terms of its financial and environmental impact by matching its individual material and energy demand with fixed factors per consumed unit. An important difference that occurs when transferring the idea of the correction node from the network modelling perspective to a product system network affects the number of required nodes and input data. While Network C III in figure 4.14 requires 22 nodes to model the network to allow the tracking of the individual paths, the subnodes for each module all contain the same static information. As the interdependencies are all bundled into the use stage, the module configuration does not need a split up for modelling interactions, only for pathfinding. Regarding the input data for LCA and LCC, e.g. the nodes of module C1–1 to module C1–4 all contain the same information. Therefore, the number of nodes does not represent the number of required input data. Figure 4.14 visualizes the difference between the node demand (left) and the data demand (right). For Network C IV, the data demand is calculated by the number of possible combinations added with the number of nodes to model the static product structure network. In Network C IV, the possible number of combinations (eight) and the number of nodes (six) result in 14 required inputs. For larger networks, the difference between possible combinations and the data demand decreases rapidly. Already for slightly larger networks, this difference becomes relatively small. For modelling a network with ten module categories and three options per category considering the interdependencies, the 59, 049 possible combinations require 59,079 inputs or 0.05% more effort than calculating all options directly. For even larger network this difference becomes insignificantly small. Applying the two introduced network reduction strategies to a product system’s structure network shows that the reduced number of required nodes are converted into a reduced need for input data regarding the LCA and LCC of the individual components, modules, and the entire product system. As stated in Section 4.3.1, implementing the interdependencies between all components and modules into the network of figure 4.9 results in a network that is too large to model, to provide input data for and too complex to optimize it. However, by applying the introduced network reduction strategies, it becomes possible to model this network. Figure 4.15 shows the application of the reduction strategies to the network in figure 4.9. Based on the first reduction strategy (network partitioning) and its transfer to the modularity of product systems, the interdependencies between components of different modules can be neglected. This allows a focus on interdependencies on a component level only within each individual module, but not between different

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Network C III

Network C IV Node C1-1

N1

N1

Node C1-2

N2

N2

Node B1-1

Node C1-3

N3

Node A1

Node B1-2

Node C1-4

Node A2

Node B2-1 Node B2-2

N3

N4

Node A1

Node B1

Node C1

N4

Node C2-1

N5

Node A2

Node B2

Node C2

N5

Node C2-2

N6

N6

Node C2-3

N7

N7

Node C2-4

N8

N8

Figure 4.14 Difference between node demand and data demand

modules. This reduces the interdependency possibilities enormously. The interdependencies between the components within a single module on the other hand are modelled in line with the network reduction strategy of introducing the correction factor. Interactions are also possible on the architecture hierarchy level of modules. These interdependencies are modelled in the same way following the principle of the excluded use stage information. This results in a unique node of the additional node category for each possible path. Following both reduction strategies, it is possible to represent the structure network including possible interdependencies in a modular way enabling the application of modular LCA and LCC and an optimization based on the graph theory. The number of required nodes to model the network of figure 4.9 lies with 290 nodes far below the number of possible combinations (26,244). The required input data with 106 data sets of each LCA and LCC is even lower. This number is derived as each module has nine static LCAs or LCCs for every component and additionally eight individual use stages for the component combinations. These 17 module specific data need to be calculated for all six available modules (A, B1 , B2 , C1 , C2 , D). Finally, the four different use stage configurations for the product system have to be added.

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modular product system structure network modular vehicle module B1: conv. car body

module A1: combuson engine

producon stage P1 Loc. L1

Electr. E1

Mach. M1

Loc. L2

Electr. E2

Mach. M2

Loc. L3

Electr. E3

Mach. M3

Comp. CA1

Comp. CB1

Comp. CC1

Comp. CA1

Comp. CB1

Comp. CC1

Comp. CA2

Comp. CB2

Comp. CC2

Comp. CA2

Comp. CB2

Comp. CC2

Comp. CA3

Comp. CB3

Comp. CC3

Comp. CA3

Comp. CB3

Comp. CC3

module B2: lightweight car body

module A2: electric powertrain Comp. CA1

Comp. CB1

Comp. CC1

Comp. CA1

Comp. CB1

Comp. CC1

Comp. CA2

Comp. CB2

Comp. CC2

Comp. CA2

Comp. CB2

Comp. CC2

Comp. CA3

Comp. CB3

Comp. CC3

Comp. CA3

Comp. CB3

Comp. CC3

producon category

module category A

product system structure network

Comp. C1-1

N1

Comp. C1-2

N2

Comp. B1-1

Comp. C1-3

N3

Comp. A1

Comp. B1-2

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. B2-2

Module A1

P1

Comp. C1-1 Comp. C1-2 Comp. C1-3

N3

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. C2-1

N5

Comp. B2-2

Comp. A3

Comp. B3

Comp. C2-2

Mach. M1

Electr. E2

Mach. M2

Loc. L3

Electr. E3

Mach. M3

end of life category

Comp. B3

Module B1

EoL1

Comp. C1-1

N1

Comp. C1-2

N2

Comp. B1-1

Comp. C1-3

N3

Comp. A1

Comp. B1-2

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

Comp. B2-2

Comp. C2-4

N8

Comp. C3

-

Comp. A3

Comp. C1-1

N1

EoL1

Comp. B3

Comp. C3

-

Comp. C1-1

N1

Comp. C1-2

N2

Comp. C1-2

N2

Comp. B1-1

Comp. C1-3

N3

Comp. B1-1

Comp. C1-3

N3

Comp. B1-1

Comp. C1-3

N3

Comp. A1

Comp. C1-4

N4

Comp. A1

Comp. B1-2

Comp. C1-4

N4

Comp. A1

Comp. B1-2

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. C2-1

N5

Comp. A2

Comp. B2-1

Comp. A2

Comp. B2-1

Comp. C2-2

N6

Comp. B2-2

N2

Comp. B1-1 Comp. B1-2

N1

Electr. E1

Loc. L2

Comp. B1-2

N1

Comp. A1

Comp. C1-1

Loc. L1

module category B

Module B1

Comp. A3

end of life stage EoL1

Comp. A3

Comp. B3

Module A2

Comp. C2-3

N7

Comp. C2-4

N8

Comp. C3

-

Comp. C1-1

N1

Comp. C1-2

N6 N7

Comp. B1-1

Comp. C1-3

N3

N8

Comp. A1

Comp. B1-2

Comp. C1-4

N4

Comp. C3

-

Comp. A2

Comp. B2-1

Comp. A3

Comp. B3

Comp. A3

Comp. B3

Module B2

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

Comp. C3

-

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

-

Comp. A3

Comp. C1-1

N1

EoL1

Comp. C1-3

N3

Comp. B1-2

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. A3

Comp. B3

Module B2

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. B2-1 Comp. B2-2

Comp. A3

Comp. B3

Comp. B2-1 Comp. B2-2

Comp. B3

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

Comp. C3

-

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

Comp. C3

-

Comp. C1-1

N1

Comp. C1-2 Comp. C1-3

N3

Comp. B1-2

Comp. C1-4

N4

Comp. A2

Comp. B2-1

Comp. A3

Comp. B3

N3

N2

Comp. B1-1 Comp. A1

Comp. B2-2

N2

N2

Comp. A2

N2

Comp. A2

N1

N3

EoL1

N3

-

Comp. C1-1

N4

N1

N4

N8

Comp. C1-3

Comp. C1-1

Comp. C1-3

Comp. C2-4

Comp. C3

Comp. C1-2

Comp. A3

Comp. C1-4

N7

Comp. C1-4

-

Comp. B1-1

N5 N6

Comp. C2-3

Comp. B1-1

N8

Comp. B1-2

Comp. C2-1 Comp. C2-2

Comp. B1-2

Comp. C2-4

Comp. A1

N2

Comp. A1

Comp. C3

Comp. C1-2

Comp. B3

N2

Comp. B1-1 Comp. A1

Comp. B2-2

Comp. B2-2

Comp. C3

Comp. C1-2

N2

Comp. C2-3 Comp. C2-4

Comp. B2-2

Comp. B2-2

Comp. C1-2

N1

Comp. C2-1

N5

Comp. C2-2

N6

Comp. C2-3

N7

Comp. C2-4

N8

Comp. C3

-

Figure 4.15 Applying reduction strategies to a system structure network

N4

122

4.4

4

Concept for the Optimization of Eco-effectiveness of Product Systems

Data Management of the Input Data

The target of the data management is to provide the relevant input data for the optimization algorithm. It is required to transfer the product system’s structure network into a clearly defined data structure where each node and arc of the network is described and weighted according its financial and environmental impact. Therefore, this subchapter describes the steps two and three of the framework (see figure 4.1). Furthermore, the individual selections and additional input parameters selected from the user via the user interface (see figure 4.2) need to be integrated into the input data. Finally, an interface for entering the optimization results from the algorithm has to be defined to analyze the results. Every available relation between the nodes of the structure network has to be documented to define the network structure for the optimization algorithm. Therefore, the network structure of the product system is transferred into a data set where the relevant information for every node and path are entered. The required information includes “from node”, “to node”, “financial costs”, “GWP” and “upper boundary” to define the network. The upper boundary information defines the capacity of each arc and therefore the availability of each module for a product fleet. The given example for the data structure in Table 4.1 is based on the product unit network of figure 4.8 with the premise that thousand product units shall be build. A maximum capacity of a thousand is required for a module to be included into every product unit of the product fleet. An upper boundary of the availability below this value limits the installation rate of this specific module. Every node category requires a sum of the upper boundaries of at least a thousand to enable a network flow of thousand product units from the beginning to the end to ensure that a total of thousand product units can be produced. A lower sum of the upper boundaries for a node category results in a limited flow through the network as not enough modules are available for each product. Consequently, the network structure and architecture hierarchy levels of the modules and components are clearly defined. The financial and environmental costs are fixed for each path. The length of this data table is defined by the number of node connections as every arrow has to be defined. Furthermore, the user can define a value for the requested minimum size of product variants for the calculated results. For a fleet of thousand product units, the extreme scenario for a solution are a thousand individual product configurations. This implies that several measures or modules are potentially applied only once. A minimum threshold can be defined that forces the algorithm to choose solutions where a product configuration appears at least as often as this limit

4.4 Data Management of the Input Data

123

Table 4.1 Data structure table for an exemplary network structure From node

To node

Financial costs

GWP

Upper boundary

A

B1

e13

120 kg CO2 eq.

1000

A

B2

e12

130 kg CO2 eq.

1000

B1

C1

e11

1,500 kg CO2 eq.

330

B2

C1

e23

220 kg CO2 eq.

330

B1

C2

e22

230 kg CO2 eq.

270

B2

C2

e21

2,500 kg CO2 eq.

270

B1

C3

e33

320 kg CO2 eq.

500

B2

C3

e32

330 kg CO2 eq.

500

C1

D1

e31

3,500 kg CO2 eq.

250

C2

D1

e43

420 kg CO2 eq.

250

C3

D1

e42

430 kg CO2 eq.

250

C1

D2

e41

420 kg CO2 eq.

750

C2

D2

e52

530 kg CO2 eq.

750

C3

D2

e51

5,500 kgCO2 eq.

750

value is required to reduce the effort of producing modules for only one single utilization. For a fleet of thousand product units and a limit value of 20, a maximum of 50 different product configurations is allowed, to fulfill the constraint of having at least 20 units of each configuration. This leads to a simplification of the transferability to a real-world problem, as the investment costs for modules with a single implementation otherwise are too high. This threshold value is also defined by the user and is provided to the algorithm via the input table. The data management table also provides a prepared area for the results of the optimization. The algorithm and calculation software writes the results of the calculated eco-effective pathway through the network into the data table where it is interpreted by the data management system. The entered pathway through the network is transferred into the ideal set of module and measure combination, and the total and average environmental and financial impacts per product system unit and fleet are expressed.

124

4.5

4

Concept for the Optimization of Eco-effectiveness of Product Systems

Adaption of Shortest Path Algorithms to the Problem Statement

This subchapter describes step four of the framework that has been introduced in figure 4.1. At first, graph-theory based algorithms to identify shortest paths through networks are selected to optimize the eco-effectiveness of a product system. Such an algorithm needs to be adapted to this purpose to meet all requirements of the introduced problem statement of optimizing the product system configuration to meet the goals of eco-effectiveness. The algorithm takes its data input from the introduced data structure table for the relations between the available nodes and the further information that is selected by the user’s preferences. The algorithm design is based on the principle of the minimum cost flow problem, considering the given arc weightings, the defined network flow, and the capacity limitations per arc. Programming the algorithm is divided into two separate aspects: model programming and data provision. Figure 4.16 shows a simple flow chart that describes the general logic of the optimization model. As the required input data is provided from the input data storage that is connected to the user interface, the main model is divided into three segments, namely: (1) initialization of the model, (2) definition of the main function and (3) definition of the boundary constraints. During the initialization of the model, the general layout of the optimization algorithm is defined. In this part of the algorithm code, the structure of the model and the role of the individual parameters and values are set. This is done by defining the number of nodes that built the network, the design of the arc structure that connects the nodes and the key variables that are relevant for the model, e.g. the limitations for each measure or the objective values. In the second segment, the main or objective functions are set up. Here it is defined which values serve as fixed target values (total costs or total emissions) and which parameter is the value that is minimized. In the third segment, the constraints or boundary conditions are defined that make sure that the optimization model behaves in the way the user is expecting. Among constraints that keep the optimization model within its own boundaries (e.g. ingoing flow into each node equals outgoing flow), conditions are also defined that keep the algorithm within the limitations of each measure. The details of each segment are described in the following paragraphs. Segment I: Model programming Programming the optimization model starts with the definition of the required variables and figures. The total amount of nodes within the network is defined as an

4.5 Adaption of Shortest Path Algorithms to the Problem Statement

125

input data e.g. number of nodes, measures, emissions, costs, limits, …

input data storage start

segment I: Initialization of model e.g. number of nodes, definition of ranges per measure, definition of limitation, definition of arc design and network flow, definition of target values

segment II: Definition of main function for target optimization e.g. minimalization of total costs or minimalization of total emissions

segment III: Consideration of constraints and boundary conditions e.g. compliance to limitation of measures, consideration of upper limit for total emissions or total costs

end output data storage

Output data e.g. ideal flow, total emissions, total costs, …

Figure 4.16 Flow chart of the general logic of the optimization model

126

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Concept for the Optimization of Eco-effectiveness of Product Systems

integer while the order of the nodes is defined as the range of available nodes. The options for the optimization objective are defined as float variables and contain the maximum budget, the maximum emission level along the entire life cycle and the maximum emission level of e.g. the use stage of the product system. A financial penalty is introduced that is activated when the emission limit is exceeded to make sure that the algorithm will select the solution that stays within the given boundaries or to model existing legislative regulations. For the integration of a minimum size of configured product types to ensure a minimum amount of product units that are built in the same way, a “BigM” variable is defined. This variable is complemented by a variable for the minimum configuration size and upper limit for the number of possible variations. These definitions need to be defined within the program code. Figure 4.17 shows an example of how the definitions of the key variables are translated into the syntax of the optimization model.

// number and range of all nodes int NumNodes = ...; range Nodes = 1..NumNodes;

// limit of target values float LimitMoney = ...; float LimitEmissions = ...; float LimitEmissionsUseStage = ...; // value of emission penalty float EmissionPenalty = ...; // variables for MinConfigurations + types of car bodies float BigM = ...; float MinConfiguration = ...; float MaxCarBodyTypes = ...;

Figure 4.17 Definition of key variables and arc design

In the same way, variables for each node have to be defined. As shown in figure 4.14 (left), each node, even though it represents the same data, has multiple subnodes to be able to track each selected path individually. For each defined node, a range is defined that covers these nodes of a module or measure. For the use case of optimizing multiple product units, availability limits of the defined nodes become relevant. For each measure, and thus the respective node range, a limit value is defined that defines the upper boundary for the corresponding arcs.

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127

The translation of the input data into a bundled information of each arc is done by the definition of the general arc design. At first, the supply and demand (“SupDem”) of each node regarding the network flow needs to be defined. This SupDem defines the individual flow for each node of the network. For the first node, this SupDem defines the number of product units flowing into the network. The SupDem of the last node has the same value with a negative sign. All nodes between the first and last node have a SupDem of zero. This ensures that the amount of product units that enter a node are the same amount that leaves the node. The tuple design for the arcs of the network contains the information “fromnode”, “tonode”, “costmoney”, “costemissions”, and “upper boundary”. This information is directly taken from the data structure table. The flow through every node has to meet the limits of its capacity. Those definitions need to be defined within the program code as well. Figure 4.18 shows an example of how these parts of the program are translated into the syntax of the optimization model.

// definition of supply and demand int SupDem[Nodes] = ...; // definition of arc architecture and source of arcs tuple arc { key int fromnode; key int tonode; float costmoney; float costemissions; int ub; } {arc} Arcs = ...; {arc} ArcsUseStage = ...; // flow in arc needs to stay below upper boundary dvar int Flow[a in Arcs] in 0 .. a.ub; // index variable defined for each arc as binary variable dvar boolean index[a in Arcs]; // calculation of SupDem subject to { forall (i in Nodes) ctNodeFlow: sum ( in Arcs) Flow[] - sum ( in Arcs) Flow[] == SupDem[i];

Figure 4.18 Definition of arc design

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Concept for the Optimization of Eco-effectiveness of Product Systems

Segment II: Definition of main function for target optimization After the variables and the arc design are defined, the equations for the optimization objectives have to be defined. The objective of the optimization is either the minimization of total costs, the minimization of total emissions or possibly the additional limitations of the emissions of a single life cycle stage, e.g. the use stage. The total costs and emissions are calculated by multiplying the individual costs and emissions of the respective arc with the flow that is assigned to this arc. The optimization objectives are either the minimization of the total emission or the total costs that consist of the costs of the network flow and the penalty for exceeded emissions. Again, these definitions need to be defined within the program code as well. Figure 4.19 shows an example of how these optimization objectives are translated into the syntax of the optimization model.

// total cost of ownership defined as the sum of all arcs dexpr float TotalFlowCostMoney = sum (a in Arcs) a.costmoney * Flow[a]; // total emissions defined as the sum of all arcs dexpr float TotalFlowCostEmissions = sum (a in Arcs) a.costemissions * Flow[a]; // total emissions in use stage defined as the sum of all arcs dexpr float TotalFlowCostEmissionsUseStage = sum (a in ArcsUseStage) a.costemissions * Flow[a]; // objective function of minimization of target values Minimize totalFlowCostMoney + EmissionPenalty * exceededemissions;

Figure 4.19 Definition of optimization objectives

Segment III: Consideration of constraints and boundary conditions Additional equations for the constraints are required to make sure that the identified solutions are in line with the defined limitations and boundary conditions. These constraints define the solution space for the algorithm and set the borders for the solution identification. Regarding the optimization objectives, constraints are required to limit the possible results to the threshold values defined by the user. The first three constraints in figure 4.20 force the algorithm to find solutions that stay below the user-defined limit values for emissions or costs. The algorithm either has to choose arcs with lower costs and/or emissions or assign a lower flow through the more expensive nodes to meet its limitations to stay below the limit values.

4.5 Adaption of Shortest Path Algorithms to the Problem Statement

129

The constraint of providing configuration solutions that appear at least in a minimum (and user-defined) amount, is based on two different equations. The first equation ensures that the flow of a node is at least as large as the minimum configuration variable demands and that the binary index variable of that node has to be activated. The second equation defines that for enabling a flow within a node, the index variable has to be activated. This is done by the “BigM” variable which simply represents a large value. The index variable needs to be activated to make sure the equation of the flow is being smaller than the BigM variable. The variable for module limitation controls the limitation of an arc’s availability within the network flow. This variable determines that the sum of all flows within the range of the same module is not allowed to be above the user-set capacity limit of this specific module. Those definitions also need to be defined within the program code. Figure 4.20 shows an example of how these constraints are translated into the syntax of the optimization model.

// total emissions need to be below defined limit ctTotalFlowCostEmissions: sum (a in Arcs) a.costemissions * Flow[a]