Cost Engineering and Pricing in Autonomous Manufacturing Systems 1789734703, 9781789734706

This book provides extensive insights and analysis into pricing models for autonomous manufacturing. Taking a cost engin

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Cost Engineering and Pricing in Autonomous Manufacturing Systems
 1789734703, 9781789734706

Table of contents :
Cost Engineering and Pricing in Autonomous Manufacturing Systems
Cost Engineering and Pricing in Autonomous Manufacturing Systems
List of Figures
List of Tables
Chapter 1: Introduction
1.1. Autonomous Manufacturing System
1.2. Costing and Pricing
1.2.1. Opportunity Cost
1.2.2. Opportunity Costs and Market Prices
1.2.3. Price
Chapter 2: Concepts of Costing in Automation
2.1. Overview
2.2. Introduction and Related Works
2.3. Model Development
2.3.1. Key Dimensions for Managing Automation Supply Complexity
2.3.2. Reference Automation Agent Architecture Model
2.4. New Paradigm: The Use of Automation Resources
2.4.1. Economic Aspects of the Automation Life Cycle
2.4.2. Maximum Benefit of the Product Life Cycle
2.5. Data Integration Model
2.5.1. Costs and Benefits of IS
2.5.2. Balancing Benefits against Implementation Costs
2.6. Discussions and Concluding Remarks
Chapter 3: Concepts of Pricing in Automation
3.1. Overview
3.2. Introduction and Related Works
3.3. Model Development
3.3.1. Automation Energy Pricing Model
3.3.2. Concession Pricing Model
3.3.3. Representative Automation Pricing Methods
3.4. Discussions and Concluding Remarks
Chapter 4: Cost Parameters and Costing Models in Autonomous Manufacturing
4.1. Overview
4.2. Introduction and Related Works
4.3. Cost Accounting Concept
4.3.1. Documenting Cost Accounting Policies
4.4. Cost Object
4.5. Manufacturing Costs
4.6. Costing Model Development
4.7. Application Study
4.8. Discussions and Concluding Remarks
Chapter 5: Cost Engineering in Autonomous Manufacturing
5.1. Overview
5.2. Introduction and Related Works
5.3. Cost Engineering
5.4. Cost Minimization/Profit Maximization
5.4.1. Short-run Cost Minimization
5.4.2. Long-run Cost Minimization
5.4.3. Application Study
5.4.4. Cost Functions
5.5. Cost of Quality
5.5.1. Application of CoQ in Autonomous System
5.6. Discussions and Concluding Remarks
Chapter 6: Cost and Price in Autonomous Manufacturing
6.1. Overview
6.2. Introduction and Related Works
6.3. Model Development
6.3.1. Time-varying Pricing
6.3.2. Production Function
6.3.3. Electricity Cost Function
6.3.4. Labor Cost Function
6.4. Manufacturing Profit Maximization
6.5. Example
6.6. Discussions and Concluding Remarks
Chapter 7: Pricing Models in Autonomous Manufacturing
7.1. Overview
7.2. Introduction and Related Works
7.3. Model Development and Analysis
7.4. Discussions and Concluding Remarks
Chapter 8: Price Optimization in Autonomous Manufacturing
8.1. Overview
8.2. Introduction and Related Works
8.3. Smart Manufacturing
8.4. Pricing in Manufacturing
8.4.1. Profitable Selling
8.4.2. Cost System for Advanced Manufacturing Systems
8.5. Estimating Rwsc
Estimating Risc
8.6. Application of the Cost Model
8.7. Discussions and Concluding Remarks
References and Further Reading

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Department of Industrial Engineering, School of Engineering, Damghan University, Iran


Faculty of Industrial Engineering, Iran University of Science and Technology, Iran

United Kingdom – North America – Japan – India – Malaysia – China

Emerald Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2019 Copyright © 2019 Emerald Publishing Limited Reprints and permissions service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-78973-470-6 (Print) ISBN: 978-1-78973-469-0 (Online) ISBN: 978-1-78973-471-3 (Epub)


List of Figures


List of Tables




Acknowledgments Chapter 1  Introduction 1.1.  Autonomous Manufacturing System 1.2.  Costing and Pricing 1.2.1. Opportunity Cost 1.2.2. Opportunity Costs and Market Prices 1.2.3. Price Chapter 2  Concepts of Costing in Automation 2.1. Overview 2.2.  Introduction and Related Works 2.3.  Model Development 2.3.1. Key Dimensions for Managing Automation Supply Complexity 2.3.2. Reference Automation Agent Architecture Model 2.4.  New Paradigm: The Use of Automation Resources 2.4.1. Economic Aspects of the Automation Life Cycle 2.4.2. Maximum Benefit of the Product Life Cycle 2.5.  Data Integration Model 2.5.1. Costs and Benefits of IS 2.5.2. Balancing Benefits against Implementation Costs 2.6.  Discussions and Concluding Remarks

xv 1 1 3 4 6 6 9 9 10 12 12 14 17 17 19 20 21 21 22

vi   Contents

Chapter 3  Concepts of Pricing in Automation 3.1. Overview 3.2.  Introduction and Related Works 3.3.  Model Development 3.3.1. Automation Energy Pricing Model 3.3.2. Concession Pricing Model 3.3.3. Representative Automation Pricing Methods 3.4.  Discussions and Concluding Remarks Chapter 4 Cost Parameters and Costing Models in Autonomous Manufacturing 4.1. Overview 4.2.  Introduction and Related Works 4.3.  Cost Accounting Concept 4.3.1. Documenting Cost Accounting Policies 4.4.  Cost Object 4.5.  Manufacturing Costs 4.6.  Costing Model Development 4.7.  Application Study 4.8.  Discussions and Concluding Remarks Chapter 5  Cost Engineering in Autonomous Manufacturing 5.1. Overview 5.2.  Introduction and Related Works 5.3.  Cost Engineering 5.4.  Cost-Minimization/Profit Maximization 5.4.1. Short-run Cost Minimization 5.4.2. Long-run Cost Minimization 5.4.3. Application Study 5.4.4. Cost Functions 5.5.  Cost of Quality 5.5.1. Application of CoQ in Autonomous System 5.6.  Discussions and Concluding Remarks

25 25 25 29 30 32 36 41 43 43 43 45 46 47 51 53 55 62 65 65 65 72 72 74 75 75 77 81 83 87

Contents    vii

Chapter 6  Cost and Price in Autonomous Manufacturing 6.1. Overview 6.2.  Introduction and Related Works 6.3.  Model Development 6.3.1. Time-varying Pricing 6.3.2. Production Function 6.3.3. Electricity Cost Function 6.3.4. Labor Cost Function 6.4.  Manufacturing Profit Maximization 6.5. Example 6.6.  Discussions and Concluding Remarks Chapter 7  Pricing Models in Autonomous Manufacturing 7.1. Overview 7.2.  Introduction and Related Works 7.3.  Model Development and Analysis 7.4.  Discussions and Concluding Remarks Chapter 8  Price Optimization in Autonomous Manufacturing 8.1. Overview 8.2.  Introduction and Related Works 8.3.  Smart Manufacturing 8.4.  Pricing in Manufacturing 8.4.1. Profitable Selling 8.4.2. Cost System for Advanced Manufacturing Systems 8.5.  Estimating RWSC 8.6.  Application of the Cost Model 8.7.  Discussions and Concluding Remarks

89 89 89 93 94 97 99 101 102 103 105 107 107 107 110 114 117 117 117 121 123 125 126 127 134 135

References and Further Reading




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List of Figures

Fig. 2.1. Impact of Automation Supply Complexity on Focal Company. 13 Fig. 2.2. The Switch of the Responsibilities. 15 Fig. 2.3. The Product Life Cycle. 18 Fig. 2.4. The Course of the Cost and Benefit in the Product Life Cycle. 19 Fig. 3.1. Comprehensive Structure for Pricing in Automation Projects. 32 Fig. 3.2. Concession Pricing Parameters of Automation Projects. 34 Fig. 3.3. Causal Loop Diagram for Concession Pricing of Automation Projects. 35 Fig. 3.4. Equivalent Marginal Cost Pricing (EMCP) Model in Automation Pricing. 39 Fig. 3.5. EMCP Model in Automation Pricing. 40 Fig. 4.1. Dimensions for the Analysis to the Cost Object. 48 Fig. 4.2. Dimensions for the Analysis Related to the Computations Challenge. 50 Fig. 5.1. Combination of Robot and Resources for Cost Minimization. 74 Fig. 5.2. Combination of Robot and Resources for Profit Maximization. 74 Fig. 5.3. Short-run Cost Minimization with One Fixed Input. 76 Fig. 5.4. Cost Function Behavior. 78 Fig. 5.5. Lundvall–Juran Curve Depicting Relationship between Conformance (Prevention) and Nonconformance (Appraisal + Failure) Costs and the Tradeoff Point (EQL).82 Fig. 5.6. Type I and Type II Errors. 84 Fig. 5.7. Representation of a Double-stage Acceptance Sampling Flow Diagram. 85 Fig. 5.8. Schematic Representation of Double-stage Accepting Sampling. 86 Fig. 6.1. Decomposition of the Profit (a) and the Total Cost (b). 94 Fig. 6.2. Diagram of a Typical Manufacturing System. 97 Fig. 6.3. Results of Example 1. 103 Fig. 7.1. The Price–Demand Curve (One Period). 111 Fig. 7.2. The Price–Demand Curve (Two Periods). 111 Fig. 7.3. The Price–Demand Curve (Three Periods). 112 Fig. 8.1. Six Pillars of Smart Manufacturing. 122 Fig. 8.2. A Cost System Supporting Analysis of Advanced Manufacturing Systems. 127

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List of Tables

Table 2.1. Shares of Costs, Revenue, and Benefits of a Manufacturing Cell as an Example (in US$). 20 Table 3.1. The Summary of Pricing Literature. 30 Table 3.2. Price Adjustment Coefficient of Reference Cases. 36 Table 3.3. Energy Pricing Mechanisms in Some Countries. 38 Table 4.1. Sample Worksheet for Mold Manufacturing Time Calculation. 57 Table 4.2. Sample Worksheet for Assembly Cost Calculation. 61 Table 4.3. Parameters Used for High-cost Manufacturing Environment And Low-cost Manufacturing Environment (Hourly Rates). 62 Table 5.1. Keywords and Combinations. 69 Table 5.2. AB – Application Area from Costing Method. 70 Table 5.3. AC – Level of Integration Between Costing Methods and Production Process. 71 Table 5.4. AD – Advantages Resulting from the Application of Costing Methods. 71 Table 5.5. AE – Difficulties in the Deployment and Utilization of Costing Methods. 73 Table 6.1. Typical Pricing Profiles in New York, USA.95 Table 6.2. Typical TOU and CPP Pricing Profiles in California, USA.96 Table 8.1. Computer System Functions for Automated Manufacturing.120 Table 8.2. Five Cases Considered for Failure Cost Estimation. 131

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Automation will substantially disrupt markets throughout the economy in the coming decade, ranging from construction to financial services. By understanding how technological changes will impact these markets, businesses can take advantage of the situation. Most importantly, buyers should be aware that those falling wages costs will help slow price growth in these markets, potentially providing the flexibility to delay purchasing decisions. Due to high tendency in employing high-tech machines and devices in industry and with respect to extensive consideration in automation, it is significant to investigate specific problems and challenges related to autonomous systems. However, such expensive systems require large amount of economic investment. Thus, identifying cost factors, analyzing them, and developing engineering paradigms for control and optimization need to be studied. Engineering design impacts whole-life cost of products produced. Understanding true cost of a product and the cost drivers during the design stage could guide the design process to obtain more competitive solutions. Cost engineering is concerned with cost estimation, cost control, business planning and management, profitability analysis, cost risk analysis and project management, planning, and scheduling. There are many different approaches and methods for estimating or assessing costs, all of which have advantages and disadvantages under particular circumstances. Cost estimating helps companies with decision making, cost management, and budgeting with respect to product development. It is the start of the cost management process. Cost estimates during the early stages of product development are crucial. Also, to have more productive system and to obtain profit, appropriate pricing models should be developed to handle the operational costs in autonomous manufacturing systems. Price is one of the most flexible elements of the marketing mix, which interferes directly and in a short term over the profitability and cost effectiveness of a company. In fact, businesses can combat the destructive pricing environments that result from increased competition and globalization by implementing a more strategic pricing approach. This method provides businesses with the ability to maximize profit by providing visibility to pricing sensitivity – allowing you to maximize price in every transaction. Therefore, both academicians and practitioners can find the book helpful. Graduate students can use the book as a course textbook or as further reading source. Industrial practitioners can learn significant concepts and applied models to be employed in real cases investigations and implementations.

xiv   Preface Therefore, this book encompasses variety of topics in cost analysis for autonomous systems and pricing models. Different topics such as scheduling costing, agent-based costing, cost parameters of an advanced manufacturing system and operations planning with respect to cost management and cost minimization are considered in the book. Also, due to high competitive market and profit aspects, pricing concepts and models for autonomous manufacturing systems are developed. The models are novel and adapted based on autonomous manufacturing systems. Some of the distinct properties of the book are listed as follows: ⦁⦁ A pioneer book in cost engineering for autonomous systems. ⦁⦁ Introducing cost parameters, elements, and optimization models. ⦁⦁ Pricing models adapted for autonomous manufacturing.

This book covers several general and technical concepts involved in optimal decision making for manufacturing systems and also the use of autonomous systems as industrial automation for both researchers and executive managers. The book can be employed as a course book in graduate studies of industrial and systems engineering, operations management, logistics, etc. Structure of the book and the materials in each chapter are further explained here. In Chapter 1, an overview of the book and significance of the concepts considered in the book are given. In Chapter 2, the basics of costing and different cost models are explained within a scheduling problem in advanced manufacturing system. In Chapter 3, pricing models are discussed in detail and a case is investigated. Analytical studies on the performance of the pricing models in different conditions are also included. In Chapter 4, various cost parameters in manufacturing systems and costing models are reported and detailed in a case problem where specific data are extracted and a costing model is implemented. The impact of each cost parameter is also analyzed. In Chapter 5, cost minimization is discussed with respect to engineering paradigm in product design and manufacturing planning. In Chapter 6, cost/price interaction for profit modeling is handled. Profit maximization is a common goal of manufacturing needing to consider both cost and price at the same time. In Chapter 7, pricing model for advanced systems is detailed and implemented for a specific system. In Chapter 8, price optimization with respect to costs is modeled for an advanced manufacturing system. The model considers a comprehensive set of parameters and provides a generic framework for other systems.


We would like to express our gratitude to the many people who saw us through this book; to all those who provided support, talked things over, read, wrote, offered comments, allowed us to quote their remarks and assisted in the editing, proofreading, and design. We would like to thank Iran National Elites Foundation and Damghan University for enabling us to publish this book. Above all, we want to thank our families, who supported and encouraged us in spite of all the time it took us away from them. It was a long and difficult journey for them. Our specific thanks to Iman Dadashpour and Ahmadreza Rostami for their warm and effective cooperation in preparing the materials of the book in different stages. Last and not least, We beg forgiveness of all those who have been with us over the course of the years and whose names we have failed to mention. Hamed Fazlollahtabar Mohammad Saidi-Mehrabad

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Chapter 1

Introduction 1.1. Autonomous Manufacturing System Intelligent machining systems facilities adaption to the changes in manufacturing environment, such as orders and changes happening in the system. In machining system, machine tools play an important role in manufacturing products with high quality, low cost, and high productivity. This chapter presents a new concept of intelligent machining system, namely Autonomous Manufacturing System (AMS). In traditional manufacturing systems, the workers play an important role, with their knowledge and experience. They adapt flexibly to changes in the manufacturing environment. With every new problem, their knowledge is updated through learning. Abilities in processing information and cognitive abilities enable workers to play an important role in monitoring, control, and production planning. Those workers with the ability to solve problems and high cognitive capacity enable adaption with changing manufacturing environment. However, the system manipulated by workers with a high price is only suitable for small production. In the era of computer-integrated manufacturing, workers are replaced by automatic control systems and robots, hence the cognitive abilities of workers in solving problems such as perception, learning, and reasoning to make a decision also are removed. The limitations of the automatic control system fail to adapt to the changes due to the system operating under preset programs. Hence, the system needs to reset and restart when an error occurs. To overcome these shortcomings, a combination of both advanced automatic control system and the cognitive abilities of human, cognitive sciences and artificial intelligence as well as the biology-inspired technologies have been applied to the manufacturing systems, which make the production system to become more intelligent and more flexible. Industries today seek the reduction and elimination of waste through continuous improvement projects that enable increased productivity within the production process, while preserving quality and serving the customer within each other (Gracanin, Buchmeister, & Lalic, 2014). These operational improvements proposed to maximize efficiency and effectiveness throughout the production system, reducing the non-value-added activities, costs, and eventually increase net income (Ruiz-de-Arbulo-Lopez, Fortuny-Santos, & Cuatrecasas-Arbós, 2013).

Cost Engineering and Pricing in Autonomous Manufacturing Systems, 1–7 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78973-469-020191001

2    Cost Engineering and Pricing in Autonomous Manufacturing Systems These perspectives makes evident that the increasing global competition among companies have adopted new production approaches such as Lean Manufacturing in order to make them more competitive (Ruiz-de-Arbulo-Lopez et al., 2013). Some industries have been through physical and cultural transformation processes by adopting the Lean concept (Abuthakeer, Mohanram, & Kumar, 2010). Briefly, Lean Manufacturing is a model that seeks to increase productivity by reducing or eliminating waste through activities that do not add value in the production processes (Ohno, 1997; Shingo & Dillon, 1988; Womack, Jones, & Roos, 1991). The adoption of Lean by companies implies the need for improvement in the accounting system. The lean organizations see the traditional accounting systems as unfavorable for eliminating waste. After all, the traditional costing system is not conceptually prepared to operate efficiently in the lean production model (Malta & Cunha, 2011; Pike, Tayles, & Mansor, 2011). In fact, even in normal companies that has a wide range of products the traditional approach to cost when applied has a distortion in the cost information (Gunasekaran & Sarhadi, 1998; Kaplan & Copper, 1998). Given this paradigm, Lean Accounting emerges as a way to adapt or change the traditional costing methods in order to support businesses and lean industrial processes (Gracanin et al., 2014; Wang & Yuan, 2009). Producing quality and reliable products at a realistic cost has always been a fundamental objective for manufacturers. In recent years, customer expectations for quality at low cost have only intensified. As manufacturers strive to achieve these goals, they eventually reach a point where tradeoffs must be made between increasing quality and lowering costs. For a specific product type, the product size and the resulting weight also affect the incremental manufacturing cost of cordless products. The weight is dependent on both the size and the type of material used for the window covering. For example, faux wood blinds have higher weight than identically sized vinyl blinds. Increasing the size and the weight may result in design modifications. In some cases, these modifications may be as simple as adding additional cordless modules to the product or changing the sizes of components, whereas in other cases, the required design changes may make the design concept unusable. For example, the use of constant force springs with friction is appropriate for products where the change in weight over the travel of the bottom rail is small. However, in large faux wood blinds where the change in weight is high, the design may not be feasible. Additionally, any customization of the product (e.g., changes in width and length, choice of fabric, etc.) requires that the cordless technology be designed to work for the entire range of customization, thereby increasing the cost. Reducing the energy consumption of machine tools can significantly improve the environmental performance of manufacturing systems. To achieve this, monitoring of energy consumption patterns in the systems is required. It is vital in these studies to correlate energy usage with the operations being performed in the manufacturing system. However, this can be challenging due to complexity of manufacturing systems and the vast number of data sources. Manufacturing and the processes involved consume substantial amounts of energy and other resources and, as a result, have a measurable impact on the environment. Reducing the energy consumption of machine tools can significantly

Introduction    3 improve the environmental performance of manufacturing processes and systems. Furthermore, given that machining processes are used in manufacturing the tooling for many consumer products, improving the energy efficiency of machining-based manufacturing systems could yield significant reduction in the environmental impact of consumer products. The problem of optimal manufacturing systems design was explored by many researchers during the last ten years. It is rather frequent to find in literature the description of methodology of design of rigid transfer lines or traditional manufacturing shop-floors.

1.2. Costing and Pricing There is a cost associated with higher quality products. For cordless technologies, the same concept can be manufactured at different qualities by using different materials (e.g., steel gears instead of plastic gears), different tolerances and surface finish, and using sophisticated transmission systems for smoother operation. Higher quality increases the life and reliability of the product, thereby reducing warranty costs for the manufacturers. The cost is dependent on whether the parts are directly manufactured by the same firm or purchased from suppliers. Similarly, the cost is also dependent on whether the parts are manufactured within the United States or overseas. The Manufacturing Cost Levelization Model is an analytical method for estimating all of the manufacturing costs necessary to produce a given product and compute a levelized cost per-unit of that manufactured product. Levelized cost is the minimum per-unit price ($/unit of product) necessary to recover all of the costs associated with manufacturing the product over an assumed financial cycle and manufacturing facility lifetime. Manufacturing costs typically includes: a) manufacturing facility capital investments; b) raw material and energy purchases; c) fixed and variable operations and maintenance costs including labor; d) financing costs, and e) taxes. Engineering economic methods are used to project each of these costs into cash flows over the life-time of the manufacturing facility. Because taxes are a function of product sales revenue, the levelized cost is the per-unit sales prices where the net present value (NPV) of all costs (including taxes) equals the NPV of all sales revenues. Thus, the levelized cost is the NPV of all of the cost cash flows divided by the NPV of the product units produced. It can be thought of as the minimum per-unit product sales price that pays all expenses including raw materials, labor wages, debt service for both loans (debt) and owner’s investments (equity), and taxes – but no profit above just these cash flow expense items. The model is designed to reflect a notional manufacturing facility specific to the production of a technology or product. Although the model can be utilized to estimate the production cost of any manufactured product, it is specifically designed to help guide technology R&D research. Estimating the production cost implications of research at an early stage can help researchers develop processes and designs that minimize the eventual manufacturing costs and increase the likelihood of successful technology deployment. Utilizing the model does require the

4    Cost Engineering and Pricing in Autonomous Manufacturing Systems development of the core components of any specific manufacturing processes: the manufacturing equipment, raw materials cost, labor costs, etc. Several methods are embedded in the model to help a technology researcher produce ballpark estimates quickly. However, the accuracy of these core components determines the accuracy of the levelized cost estimate and therefore the model should be utilized over the course of R&D allowing it to evolve alongside the R&D process and guide research focus toward the most significant cost drivers. Managers are most experienced with cost presented as monetary expenses in an income statement. Politicians and policy analysts are more familiar with costs as an expense item in a budget statement. Consumers think of costs as their monthly bills and other expenses. But economists use a broader concept of cost. To an economist, cost is the value of sacrificed opportunities. What is the cost to you of devoting 20 hours every week to studying microeconomics? It is the value of whatever you would have done instead with those 20 hours (leisure activities, perhaps). What is the cost to an airline of using one of its planes in scheduled passenger service? In addition to the money the airline spends on items such as fuel, flight-crew salaries, maintenance, airport fees, and food and drinks for passengers, the cost of flying the plane also includes the income the airline sacrifices by not renting out its jet to other parties (e.g., another airline) that would be willing to lease it. What is the cost to repair an expressway in Chicago? Besides the money paid to hire construction workers, purchase materials, and rent equipment, it would also include the value of the time that drivers sacrifice as they sit immobilized in traffic jams. Viewed this way, costs are not necessarily synonymous with monetary outlays. When the airline flies the planes that it owns, it does pay for the fuel, flightcrew salaries, maintenance, and so forth. However, it does not spend money for the use of the airplane itself (i.e., it does not need to lease it from someone else). Still, in most cases, the airline incurs a cost when it uses the plane because it sacrifices the opportunity to lease that airplane to others who could use it. Because not all costs involve direct monetary outlays, economists distinguish between explicit costs and implicit costs. Explicit costs involve a direct monetary outlay, whereas implicit costs do not. For example, an airline’s expenditures on fuel and salaries are explicit costs, whereas the income it forgoes by not leasing its jets is an implicit cost. The sum total of the explicit costs and the implicit costs represents what the airline sacrifices when it makes the decision to fly one of its planes on a particular route.

1.2.1. Opportunity Cost The economist’s notion that cost is the value of sacrificed opportunities is based on the concept of opportunity cost. To understand opportunity cost, consider a decision-maker, such as a business firm, that must choose among a set of mutually exclusive alternatives, each of which entails a particular monetary payoff. The opportunity cost of a particular alternative is the payoff associated with the best of the alternatives that are not chosen.

Introduction    5 The opportunity cost of an alternative includes all of the explicit and implicit costs associated with that alternative. To illustrate, suppose that you own and manage your own business and that you are contemplating whether you should continue to operate over the next year or go out of business. If you remain in business, you will need to spend $100,000 to hire the services of workers and $80,000 to purchase supplies; if you go out of business, you will not need to incur these expenses. In addition, the business will require 80 hours of your time every week. Your best alternative to managing your own business is to work the same number of hours in a corporation for an income of $75,000 per year. In this example, the opportunity cost of continuing in business over the next year is $255,000. This amount includes an explicit cost of $180,000 – the required cash outlays for labor and materials; it also includes an implicit cost of $75,000 – the income that you forgo by continuing to manage your own firm as opposed to choosing your best available alternative. The concept of opportunity cost is forward looking, in that it measures the value that the decision-maker sacrifices at the time the decision is made and beyond. To illustrate this point, consider an automobile firm that has an inventory of sheet steel that it purchased for $1,000,000. It is planning to use the sheet steel to manufacture automobiles. As an alternative, it can resell the steel to other firms. Suppose that the price of sheet steel has gone up since the firm made its purchase, so if it resells its steel the firm would get $1,200,000. The opportunity cost of using the steel to produce automobiles is thus $1,200,000. In this illustration, opportunity cost differs from the original expense incurred by the firm. After reading this last example, students sometimes ask, “Why isn’t the opportunity cost of the steel $200,000: the difference between the market value of the steel ($1,200,000) and its original cost ($1,000,000)?” After all, the firm has already spent $1,000,000 to buy the steel. Why the opportunity doesn’t cost the amount above and beyond that original cost ($200,000 in this example)? The way to answer this question is to remember that the notion of opportunity cost is forward looking, not backward looking. To assess opportunity cost we ask: “What does the decision-maker give up at the time the decision is being made?” In this case, when the automobile company uses the steel to produce cars, it gives up more than just $200,000. It forecloses the opportunity to receive a payment of $1,200,000 from reselling the steel. The opportunity cost of $1,200,000 measures the full amount the firm sacrifices at the moment it makes the decision to use the steel to produce cars rather than to resell it in the open market. Opportunity costs depend on the decision being made. The forward-looking nature of opportunity costs implies that opportunity costs can change as time passes and circumstances change. To illustrate this point, let us return to our example of the automobile firm that purchased $1,000,000 worth of sheet steel. When the firm first confronted the decision to “buy the steel” or “don’t buy the steel,” the relevant opportunity cost was the purchase price of $1,000,000. This is because the firm would save $1,000,000 if it did not buy the steel. But – moving ahead in time – once the firm purchases the steel and the market price of steel changes, the firm faces a different decision: “use the steel to produce cars” or “resell it in the open market.” The opportunity cost of using the steel is

6    Cost Engineering and Pricing in Autonomous Manufacturing Systems the $1,200,000 payment that the firm sacrifices by not selling the steel in the open market. Same steel, same firm, but different opportunity cost! The opportunity costs differ because there are different opportunity costs for different decisions under different circumstances.

1.2.2. Opportunity Costs and Market Prices Note that the unifying feature of this example is that the relevant opportunity cost was, in both cases, the current market price of the sheet steel. This is no coincidence. From the firm’s perspective, the opportunity cost of using the productive services of an input is the current market price of the input. The opportunity cost of using the services of an input is what the firm’s owners would save or gain by not using those services. A firm can “not use” the services of an input in two ways. It can refrain from buying those services in the first place, in which case the firm saves an amount equal to the market price of the input. Or it can resell unused services of the input in the open market, in which case it gains an amount equal to the market price of the input. In both cases, the opportunity cost of the input services is the current market price of those services.

1.2.3. Price Price is one of the most flexible elements of the marketing mix, which interferes directly and in a short term over the profitability and cost effectiveness of a company (Simon, Bilstein, & Luby, 2008). Despite the importance a price has on the performance of businesses, it seems that this element has not received proper attention from many academics and marketing professionals (Avlonitis & Indounas, 2006). Typically, in marketing, the main focus is placed on the development of new products, distribution channels, and communication strategies, and according to Lancioni (2005) this could lead to precipitated pricing decisions without properly evaluating market and cost factors. Thus, pricing is treated as the simplest strategy within marketing, perhaps because many companies determine their prices based on intuition and the manager’s market experience (Simon, 1992). In addition, only few managers strategically think about pricing while proactively administrating their prices in order to create favorable conditions that lead to profits (Nagle & Holden, 2003). Considering this, Liozu and Hinterhuber (2012) highlight the need for more research regarding the pricing preferences and practices because, according to the authors, less than 2% of all published articles in marketing journals are focused on pricing. Strategic pricing requires a stronger relationship between marketing and the other sectors of a company. In order to enhance companies’ economic and financial performance, the pricing policies should be defined by their internal capacities and on the basic systematical understanding of needs and wishes of their customers, in addition to market conditions such as economic conditions and degree of competition (Besanko, Dranove, Shanley, & Schaefer, 2012; De Toni

Introduction    7 & Mazzon, 2013b). In this context, this study’s objective is to propose and test a theoretical model that indicates the impacts of pricing policies on company’s profit. In this regard, the theoretical assumptions consider as pricing policies the definitions that comprise the pricing strategies and the price levels used by companies in their respective markets. Pricing strategies are based on Nagle and Holden (2003) studies, namely valuebased, competition-based, and cost-based pricing strategies; whereas the pricing levels are classified as high and low prices (Urdan & Osaku, 2005). Besides identifying the direct effects of these elements over profitability, this research also analyzed the impacts of moderating effects considering some independent variables on the business profitability (dependent variable). According to Monroe (2003), price decisions are one of the most important decisions of management because it affects profitability and the companies’ return along with their market competitiveness. Thus, the task of developing and defining prices is complex and challenging because the managers involved in this process must understand how their customers perceive the prices, how to develop the perceived value, what are the intrinsic and relevant costs to comply with this necessity, as well as consider the pricing objectives of the company and their competitive position in the market (De Toni & Mazzon, 2013a, 2013b; Hinterhuber & Liozu, 2014; Monroe, 2003).

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Chapter 2

Concepts of Costing in Automation 2.1. Overview The system of computing cost of production or of running a business is by allocating expenditure to various stages of production or to different operations of a firm. Costing may involve only the assignment of variable costs, which are those costs that vary with some form of activity (such as sales or the number of employees). This type of costing is called direct costing. For example, the cost of materials varies with the number of units produced, and so is a variable cost. Costing can also include the assignment of fixed costs, which are those costs that stay the same, irrespective of the level of activity. This type of costing is called absorption costing. Examples of fixed costs are rent, insurance, and property taxes. Costing is used for two purposes: ⦁⦁ Internal reporting. Management uses costing to learn about the cost of opera-

tions so that it can work on refining operations to improve profitability. This information can also be used as the basis for developing product prices. ⦁⦁ External reporting. The various accounting frameworks require that costs be allocated to the inventory recorded in a company’s balance sheet at the end of a reporting period. This calls for the use of a cost allocation system, consistently applied. Within the areas of both internal and external reporting, costing is most heavily utilized in the area of assigning costs to products. This can be done with job costing, which requires the detailed assignment of individual costs to production jobs (which are small product batches). Another alternative is to use process costing, where costs are aggregated and charged to a large number of uniform products, such as are found on a production line. An efficiency improvement on either concept is to use standard costing, where costs are estimated in advance and then assigned to products, followed by variance analysis to determine the differences between actual and standard costs. In the contemporary dynamic globalized world economy, manufacturing organizations are faced with stiff cut-throat competition. The global competition characterized by the rapid technological innovations and ever-changing market

Cost Engineering and Pricing in Autonomous Manufacturing Systems, 9–23 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78973-469-020191002

10    Cost Engineering and Pricing in Autonomous Manufacturing Systems demands is putting enormous pressure on manufacturing organizations across the globe. The contemporary manufacturing organizations endeavor to achieve world-class performance through continuous improvement in the production systems and development of world-class products and services to satisfy the peculiar and rapidly changing customer requirements.

2.2. Introduction and Related Works The manufacturing sector across the globe has witnessed drastic changes in the later part of the twentieth century. These changes have left their unmistakable mark on the different facets of manufacturing organizations. The challenges of stiff competition and the drive for profits are forcing the organizations to implement various productivity improvement efforts to meet the challenges posed by ever-changing market demands. The demands on the efficiency and quality of capital goods, for example, machine tools and manufacturing systems, are further increasing, whether it may be the technically defined fields realizing robust processes or the economical field considering the operating costs. The producer of a manufacturing system will have an increasing responsibility observing environmental regulations and restrictions during the life cycle stages while manufacturing, usage and disposal (Alting, 1996; Jansen & Krause, 1995). Considering environmental aspects of the production phase, the machining processes are certainly not crucial, even though there are numbers of potential approaches to increase the intensive usage of resources and to decrease the effect on the environment by means of using appropriate materials. There is another decisive motive taking the whole product life cycle of a manufacturing system into consideration. Modern machine tools are very complex systems of high performance. Their use is sensitively influenced by controlling the systems and subsystems. According to this, the systems should be economically used at the limit of performance and precision. Long time ago, the technical evolution of machine tools reached an area of high quality and a long working life, that is rather determined by technical outdated functionality than by attrition. Besides, the manufacturing system can be used much more efficiently using the knowledge and know-how of the manufacturer by means of modern communication networks using teleservice and teleoperations. Therefore, the producer of a product or a manufacturing system remains responsible for a longer period, and therefore has another opportunity to explore new business areas to make further profit. A common language for communicating about business events is a prerequisite for coordinating diverse and far-flung units of organizations. In the realm of computer information systems (IS), one form this common language can take is data integration, or data elements with standard definitions and codes. Researchers studying the impact of information technology on organizations often assume that the common language provided by data integration exists, or that it will be developed because of the benefits of increased communication within (or across) organizations (e.g., Huber, 1990; Malone, Yates, & Benjamin, 1987).

Concepts of Costing in Automation    11 However, there is evidence that this common language of logically compatible data does not exist in a great many large organizations today. Within a single company there are often different identifiers for key business entities, such as customer or product, different schemes for aggregating key indicators, such as sales or expenses, or different ways of calculating key concepts, such as profit or return on investments. These inconsistencies cause major problems when firms ask questions that span multiple systems or multiple subunits, thwarting their ability to make coordinated, organization-wide responses to today’s business problems. The scheduling of products, parts, assemblies, or subassemblies to different shop floor resources is a well-known and complex problem. It has been typically formulated as an optimization problem. It is also known to be NP hard consisting in the selection of the best possible schedule out of n!m where n is the number of tasks (or jobs) and m the number of available machines. The complexity of determining effective schedules is subsequently aggravated when (Shen, Wang, & Hao, 2006) operators and tools are included in the process, optimization occurs both for planning and scheduling, unpredictable conditions impact the system (failures, breakdowns, system changes, and production surges). This has promoted the subformulation of the scheduling problem to meet distinct objectives and several performance indicators have been chosen as the optimization target. When the main objective is to improve the system’s balance, typical problem formulations include maximization of line utilization, minimization of number of stations given the cycle time, minimization of the cycle time given the number of stations, and a compromise between the number of stations and cycle time (Boysen, Fliedner, Scholl, 2008). When the optimization objective is more focused on performance than make span (Pach, Berger, Bonte, & Trentesaux, 2014; Zhao, Zhu, Ren, & Yang, 2006), minimization of tardiness (Kianfar, Fatemi Ghomi, & Jadid, 2012; Tharumarajah, 1998; Zhang & Wu, 2008), throughput, energy efficiency (Pach et al., 2014), work in progress (Tamani, Boukezzoula, & Habchi, 2011), activity-based costing, etc., are commonly used to characterize the performance of the scheduling algorithms. Scheduling has therefore been approached from different perspectives, with different objectives. The conventional approaches are based on enumerative or heuristic algorithms and are normally able to produce near optimal solutions. Known techniques and algorithms include genetic algorithms (Lin, Lee, & Ho, 2013; Zhao et al., 2006), ant colony optimization (Blum & Samples, 2004; Chen, Lo, Wu, & Lin 2008), particle swarm optimization (Sha & Hsu, 2006; Zhao et al., 2006; Zhang & Wu, 2008), fuzzy control (Tamani et al., 2011; Zhao et al., 2006) and neural networks (Rovithakis, Perrakis, & Christodoulou, 2001). The focal company may induce working relationships among suppliers, while some autonomous relationships may emerge among the suppliers (Choi, Dooley, & Rungtusanatham, 2001). The overall relationship arrangement between the focal company and the automation supply is depicted pictorially in Fig. 2.1. The arrows indicate the direction of influence (e.g., coordination and control), and the lines connecting suppliers indicate the relationships among the

12    Cost Engineering and Pricing in Autonomous Manufacturing Systems suppliers, whether induced by the focal company or emerged autonomously (e.g., joint product development, participation in common supplier associations, buyer–supplier relationships between suppliers, etc.). With the recent trend of increasing levels of outsourcing, orchestrating activities with suppliers in the automation supply from the perspective of a focal company has become a top strategic issue. In general, the more its inputs of production the focal company decides to buy instead of make, the more dependent it is on the automation supply. More specifically, the higher the percentage of purchased items and services to the total cost of goods sold, the higher the significance of automation supply management to the company’s bottom line such as return on investment or shareholder value. Researchers and practitioners have responded to this problem by developing ways to increase data integration. For example, network and relational data structures that could accommodate the varied needs of many users in a single integrated data structure have been described (Bonczek, Holsapple, & Whinston, 1978; Date, 1981). The entity relationship model has been proposed as a means of conceptually modeling all of a corporation’s data, providing the basis for integrated computer systems (Chen, 1976; McCarthy, 1982). Other researchers have recognized that organizations do not start from a clean slate, and existing systems typically are not integrated. Thus, theoretical approaches for integrating existing, disparate database schemas have been developed (Batini, Lenzerini, & Navathe, 1986). Still others have focused on developing practical methods for immediate use in actual organizations, including information engineering methodologies that would provide an information architecture by which organizations could transform their systems from nonintegrated to integrated forms (Finkelstein, 1990; IBM, 1981; Martin, 1982, 1986). In general, the presumption has been that greater data integration is universally desirable and leads to greater benefits for organizations.

2.3. Model Development 2.3.1. Key Dimensions for Managing Automation Supply Complexity There are three dimensions that managers should consider when managing the complexity of an automation supply: (1) the number of suppliers in the automation supply, (2) the degree of differentiation of these suppliers, and (3) the level of interrelationships among the suppliers. Having arrived at these three dimensions, drawing from various literature sources including organization design, organization development, and supply chain management, it is striking to note that these dimensions in fact correspond to the variables captured in the “NK” model of complex system proposed by Kauffman. He approached complexity from a more mathematical and technical perspective. The three variables in the NK model that correspond with our three dimensions include: “N” sites that exist within a complex system, “A” alternative states that these sites can be in, and “K” number of functional couplings

Concepts of Costing in Automation    13 among the sites. These three variables overlap quite precisely with our dimensions in that N, A, and K point, respectively, to the number of suppliers, the degree of differentiation, and the level of interrelationships. Furthermore, in his description of the complexity of social systems, La Porte also proposed similar dimensions from a sociological perspective. They are the number of social components in a social system, the role/position differentiation of these components, and the degree of interdependence of the functional operations of these components. Therefore, we feel confident that we have captured three key dimensions of complexity. We further refine these terms below. Based on our review of the literature, there are four key areas of managerial focus when it comes to managing an automation supply. They are transaction costs, supply risk, supplier responsiveness (Carbone, 1999), and supplier innovation (Miles & Huberman, 1984). Managing transaction costs focuses on minimizing the costs incurred at the interface between a focal company and its suppliers; managing supply risk refers to minimizing potential negative events that might occur in procuring the goods and services from the suppliers; supplier responsiveness addresses the timeliness of the movement of goods and services such as on-time delivery and suppliers’ ability to meet changing requirements; and managing supplier innovation entails tapping into suppliers’ creativity for product and process improvements. We propose the following relationships between automation supply complexity and each of these four constructs, as shown in Fig. 2.1. A positive relationship is depicted with a plus sign, a negative relationship is shown with a minus sign, and a quadratic relationship is symbolized by a U-shape sign. These relationships depict a general trend between automation supply complexity and each of the four managerial focuses as dependent measures. However, the focal company can emphasize any one of the three dimensions of automation supply complexity and can set up as the strategic thrust any one of the four dependent measures. In each of the four sections that follow, we will introduce the general proposition and, whenever possible, follow that with more specific and detailed corollaries involving each of the automation supply complexity dimensions and the four dependent measures.

Fig. 2.1:  Impact of Automation Supply Complexity on Focal Company.

14    Cost Engineering and Pricing in Autonomous Manufacturing Systems 2.3.2. Reference Automation Agent Architecture Model The agent architecture presented in this section is derived from the IDEAS reference architecture. It shares with this architecture some concepts and their implementation. Among the concepts are the notion of skill as the basic representation of the stations’ functionalities; the deployment agent as a software construct that has the ability to handle agent serialization, deserialization, and the subsequent deployment across compatible controllers; and the product agent (PA) as the toplevel entity that takes decisions on the locations and associated costs where its process plan is to be executed. The deployment agent is a pure technological construct. It is used in the agent deployment procedure and in the automation of the tests. Although some names of the transport-related agents are similar, its internal behavior and interaction dynamics have been substantially modified with respect to the work reported. In this context, the proposed architecture focuses entirely on the interactions between the PAs and the transport elements. In respect to the work detailed, the present architecture is more generically defined, featuring no specific technological couplings, and hence is able to tackle a wider family of systems. The link with the IDEAS architecture is worth a mention since the present implementation has retained the generic technological elements that have been demonstrated under the IDEAS industrial test cases and that render it applicable in real world scenarios and not only in simulation. The proposed agent architecture (Fig. 2.2) is supported by a heterarchical agentbased model composed of four main agents: Transport Entity Agent (TEA), Routing Entity Agent (REA), Skill Management Entity Agent (SMEA), and the PA. All these agents are formally specializations of the Transport Element Agent abstract class that encapsulates all the generic variables and functions related to the management of products in runtime. The architecture is additionally constituted by three interfaces and one abstract class that enable (1) the development of customized transport cost metric algorithms (Cost Metric Algorithm Interface, CMAI); (2) the development of customized path computation algorithms (Path Computation Algorithm Interface, PCAI); (3) the generic interconnections of the agents with the physical world and subsequent control in the form of the Low Level Control Integration Interface, and the Low Level Control Integration Library Interface (partially inherited from the IDEAS architecture following the principles described in Rockart, 1979). The main interactions are restricted to three pairs of agents. The first set of interactions occurs between the PAs and the REAs and their specializations, between the REAs and the TEAs and in between the REAs the PAs are the top-level decision-makers in the architecture and they interact with the REAs in order to obtain the transport cost associated with their displacement to a location where the next step of their process plan will execute. The stepwise negotiation concerning each skill on the PA’s process plan is an architectural design decision that seeks to limit the allocation of a high number of resources. This allows the transport elements more flexibility in re-routing when changes are

Concepts of Costing in Automation    15

Fig. 2.2:  The Switch of the Responsibilities. introduced in the system. It also introduces some decision myopia since the stepwise negotiation does not necessarily drive the PA through the optimal path for its entire process plan. However, the present architecture compromises between PA level decision myopia and the self-organizing effect of transport elements when handling PAs with weaker allocation commitments to the available resources. The PAs keep track of their current location by memorizing the last transport element they have interacted with. The transport elements keep track of all the PAs in the system. The entire transport procedure is transparent to the PA. In this context, the PA interacts with the routing elements and their specialized classes only when entering the system, choosing the next execution location, or reacting to the failure of the SMEA where they attempt to execute. Fig. 2.2 provides an overview of the main interactions between the agents when disturbances do not occur. In the portrayed scenario, the PA is entering the system. Hence, it interacts with the REA that will trigger the transport to its first execution location. The communication starts through the request of the transport cost to all the locations in the system where Skill x can be executed. The REA replies with the corresponding list. If the skill is not available, the PA is not allowed to enter the system or, if it is already in the system, it is routed to the destination where it can be removed. The PA will subsequently evaluate the costs and choose one of the locations. It will normally choose the lowest cost solution unless its process plan prescribed the execution of Skill x in a specific station. Once a destination is selected the PA requires the corresponding transport to the REA. The REA will then forward the PA to the TEA listed in its routing tables as the one conducting to the destination along the shortest path. This procedure is used to progressively process the PA

16    Cost Engineering and Pricing in Autonomous Manufacturing Systems until it reaches its destination. When the PA arrives at its destination, the receiving SMEA, which is a specialization of the REA, recognizes that the PA wishes to use the resources therein and issues a confirmation that the PA has arrived at the required destination. The PA will subsequently handle the execution, mediated by SMEA, and upon conclusion will retrigger the procedure of selecting the next execution location. If the final destination of a PA disappears from the system, the transport procedure must be interrupted. If during the transport process a REA deems the target destination of a PA unreachable it temporarily stops the routing procedure and messages the PA with a set of possible alternatives for the execution of Skill x. In Fig. 2.2, a station, which by inheritance is also a REA, receives new link state information that reveals that the desired resource is no longer present. The PA will then reassess its process plan and decide if it can take one of the alternatives or if it should be removed from the system. If the cost of the potential alternatives is the same then the PA chooses a random location. This is particularly important during the setup stage of the system where several stations may be available at the same cost and the load must be balanced. The interactions between the REAs are to a certain extent governed by the TEAs. In effect, each TEA continuously computes its transport cost (+computeCost (capacity: int, contents: Item [0..*], timeStamp : long) : long) using the user-defined class that implements the CMAI. The CMAI also features a function (+updateCosts() : boolean) that instructs the TEA whether it should send its cost update to the associated REA. Only the REA that precedes a TEA receives its traversing cost information. In this context, it is possible to control the nervousness of the overall system by regulating the output of the update costs function. The interaction between the TEA and the REA is therefore a simple request–reply communication triggered by the TEA. The same is valid for the interactions between REAs. Whenever a REA receives information from a TEA it recomputes the routing tables using the function (+updateRoutes (systemInfo: hashMap): treeMap) in the user-defined class that implements the PCAI and forwards the neighborhood changes to all the other REAs in the system. These generic interactions and basic behaviors constitute the generic core of the architecture that renders it applicable to a wide range of systems. However, to extract concrete performance figures and evaluate the architecture the user-defined algorithms need to be instantiated. Information about recently plugged resources and the associated skills are broadcasted every time a station is plugged. On the instantiation of the user-defined algorithms, since the user-defined algorithms make use of the internal variables of the different transport elements, it is worth detailing their main purpose. These variables include the maximum capacity of the transport element (-capacity : int), a flag representing the filling level (-full : boolean), an identifier detailing the next item to be dispatched by the transport element (-nextToDispactch : Item), a flag stating the status of the item being dispatched (-dispatched : boolean), and the destination of that item (-nextDestination : Transport Element Agent), the current contents of the transport element (-content- s : Item [0..*]), a flag stating the preparedness of the next item to be dispatched (-readyToDispatch : boolean), the set of neighboring transport elements that are able to dispatch items to the current transport element (-inputNeighs : Transport Element Agent [0..*]),

Concepts of Costing in Automation    17 the set of neighboring transport elements to where the current transport element can dispatch items to (-outputNeigh- s : Transport Element Agent [0..*]), an internal behavior that aggregates all the monitoring and control behaviors of the agent (-controlLoop : Behaviour), which is overridden by the specialized classes, and finally a flag that tracks the status of the hardware execution commanded by the transport element (-executionSta- tus : boolean). The transport element class also features a set of abstract functions that handle: dispatching (+dispatchItem(item : Ite- m)) and reception of items (+acceptItem(item : Item)) and the runtime management of the neighbors (+addInputNeigh(neigh : Transport Element Agent), +addOutputNeigh(neigh : Trans- port Element Agent), +removeInputNeigh(neigh : Transport Ele- ment Agent), +removeOutputNeigh(neigh : Transport Element Agent) ). As mentioned earlier, the CMAI is a central algorithm in controlling the nervousness of the system. In the present context, the cost computation function (+computeCost(capacity : int, contents : Item [0..*], timeStamp : long ): long) is calculated as follows:

Cost =

∑ TTi   If at least one carrier exceeds the MTT If 0 carriers exceed the MTT N

where TTi is the transport time of a carrier i whose transport time exceeds the minimum transport time MTT and N is the total number of carriers exceeding the MTT value.

2.4. New Paradigm: The Use of Automation Resources 2.4.1. Economic Aspects of the Automation Life Cycle In the case of capital goods, there is an increase in producer’s responsibility for the caused and determined activities of the product life cycle after the purchase and installation phase. In Fig. 2.2, this evolutionary process is schematically depicted. Due to this, the accompanying follow-up costs, for example, caused by usage, service, and disposal activities, are increasing, but also new and economically successful business areas can be explored. New cost accounting methods are needed to assess the share of costs and revenues. The traditional accounting methods are not qualified to cover these new demands in order to optimize the cumulative benefit of a production system considering the whole life cycle. The following fact proves this thesis: business management cost accounting methods traditionally strive to optimize resources like asset, staff, material, and systems, but less concentrate on accounting the benefit of the products. This usually is carried out by order of magnitude estimates, just to show the advantage of an investment. Intending to assess the increased expenditures during service usage and disposal, it has to be based on a complete new accounting method. The traditional methods of cost accounting are not able to assess the quality of the manufactured product sufficiently. Based on own experiences of the usage of flexible manufacturing systems during 15 years of operation, systems of a higher quality achieved a more effective manufacturing time,

18    Cost Engineering and Pricing in Autonomous Manufacturing Systems twice as long than the span of a lower quality product. Single CNC machine tools used in the aviation industry for milling of titanium components during tree-shift operation reached up to 100.000 hours of operation. Machine tools of a similar structure but of lower quality were not up to nearly the same load after only 40.000 hours of operation. The relationship between quality and working life is principally known but not considered by the traditional assessment of costs and revenues. According to this example, it is obvious that some evident aspects are not taken into account if traditional resource-models are applied (Redberg et al., 1996; Zust, Caduff, & Frei, 1996). An increased budget during the design and construction phase leads to a higher quality of the product and results in a prolonged working life and a higher benefit of the product. Considering the product life cycle as a whole and striving the total benefit as the target of the optimization, not only the life time but also the costs of operation and other costs can be assessed more effectively. Within this, all ecological factors can be considered, too. The optimization of the cost and revenues of a manufacturing system is based on its life cycle as outlined in Fig. 2.3. The main stages are the “production,” “usage (market),” and “disposal/deproduction.” The single processes in the different life cycle stages are linked by the product components or their functionality. As an example, the functions and the features of the manufacturing system have a substantial influence on the expenditures for maintenance and repair, so that the total effectiveness of the manufacturing system is determined, too (Westkamper & Friedel, 1997). Hence, cost reductions in the different life cycle stages are achieved through a product conception directed toward the requirements of the use phase. The same interdependence applies to subsequent expenditures arising during the utilization and disposal or deproduction phase (Dowie, Simon, & Fogg, 1995; Jansen & Krause, 1995). For example, the shares of different materials used in the manufacturing system have a substantial influence on the allocation of cost and revenues in the life cycle of the product. In the future, the additional responsibility for a system’s disposal will mean to calculate up to 5% of the replacement value of the system for recycling and/or waste removal.

Fig. 2.3:  The Product Life Cycle.

Concepts of Costing in Automation    19 In future times, the designer and manufacturer of manufacturing systems will have an increasing responsibility in developing systems and devices appropriate or adequate to the demands of the whole life cycle. The product life cycle offers many opportunities to reduce costs or even to make profit (Zust et al., 1996).

2.4.2. Maximum Benefit of the Product Life Cycle The growing possibilities given by the modern information and communication technology (Jansen & Krause, 1995) result in the support of monitoring highly sophisticated manufacturing systems during their whole life cycle at every place on earth. Using these technologies and possibilities in the life cycles of the products, it leads to the challenge of operating the products much more effectively than we do today. The crucial question is how to use and operate the products with the maximum efficiency and economy regarding the whole life cycle. The most important distinctive categories for the respective areas of cost and benefit in the life cycle of a manufacturing system are production, service/ usage and deproduction. There are different influences causing the increase or the decrease of the costs or the benefit as outlined in Fig. 2.4. Observing the constraints and regulations of environmentally friendly and quality-orientated manufacturing processes leads to increasing expenditures (Alting, 1996; Zust et al., 1996) during the production phase, whereas low costs of manufacturing (e.g., wages and energy) result in decreased life cycle costs. To reach a higher benefit out of the product or system during the service and usage phase, it leads to increasing expenditures, whereas lower cost for operation results in decreased expenditures (Carannate, Haigh, & Morris, 1996). The same relation exists for the reuse and recycling phase. The share of reusable parts and components of the product is the cost-inducing factor while decreased expenditures for the processes of the recycling and deposition result in lower life cycle costs (Dowie et al., 1995; Westkamper & Friedel, 1997). The share of costs, revenues, and benefits of a manufacturing system is depicted in Table 2.1. The sales/

Fig. 2.4:  The Course of the Cost and Benefit in the Product Life Cycle.

20    Cost Engineering and Pricing in Autonomous Manufacturing Systems Table 2.1:  Shares of Costs, Revenue, and Benefits of a Manufacturing Cell as an Example (in US$).   Manufacture Operator Recycle

Costs 450,000 2,187,000 12,344

Revenues 500,000 3,310,696 13,500

Benefit 50,000 1,123,696 1,156

distribution costs are part of the distributional pre-sale cost, and consist of spare parts for installation (manufacturer) and training costs (operator). The costs of the manufacturer mostly cover manufacturing costs including all activities of product development, design, relevant overhead cost, and special production costs. Spare and expendable parts are regarded as after sales cost of the operator to be exchanged as a consequence of warranty contracts or other agreements between the manufacturer and the buyer or user of the system. Operating cost for maintenance and repair may arise from repairs carried out by the manufacturer and may be resulting, for example, from misoperations of the user or from maintenance work based on contracts between the manufacturer and customer (Carannate et al., 1996; Westkamper & Friedel, 1997). Accompanying payments (cost) and deposits (revenue) during the operation and utilization phase depends very much on the complexity of the product and the maintenance strategy. In the worst case, operation and utilization costs may amount to three or four times the expenditures of product acquisition. The maintenance costs of plants typically may amount to 4-14’/0 of total production costs and often are greater than the plant profit (Carannate et al., 1996). The prevailing maintenance strategy has a crucial impact on cost and revenue during the utilization phase. For example, alternatives for maintaining complex systems are setting up local maintenance branches or establishing maintenance areas to enable onsite maintenance. Two other maintenance variants relate to (partly) transportable systems: first, through local branches for interim maintenance, where maintenance works include options of upgrading, and second, through local branches of depot maintenance, exclusively intended to upgrade at the manufacturer’s site. The revenue of the manufacturer is determined by the purchase price, whereas the revenue of the operator is determined by the added value of the manufacturing process. The recycling costs mainly determine the revenues of the recycler.

2.5. Data Integration Model The model to be developed here depends upon a very rational view of automation organization and organizational design. It assumes that benefits and costs will be summed at the level of the automation organization and that everyone in the organization shares the goal of maximizing overall benefits minus costs. In spite of these caveats, the rational model provides a powerful conceptual framework for thinking about the issue of data integration.

Concepts of Costing in Automation    21 2.5.1. Costs and Benefits of IS The idea that a careful conceptualization of costs and benefits can lead to useful insights about design choices for IS is not new. As early as 1959, Marschak conceptualized the differences in IS designs as different ways of selecting and partitioning the available data from the environment. IS can be seen as having costs (design and implementation costs to the provider of the system) and benefits (improved decision-making and more informed actions for the user of the information), which can vary quite independently. Organizations face an array of possible IS, each with certain implementation costs and different expected decision-making benefits. Rational organizations should choose a design that maximizes the benefits minus the costs (Emery, 1982; Marschak, 1959; Mendelson & Saharia, 1986). To look at data integration in this light, a clear conceptualization is needed of how integration affects both the benefits and the costs to users and implementers. Organizational information processing theory (Daft & Lengel, 1986; Galbraith, 1973; Tushman & Nadler, 1978) gives us a basis for such a conceptualization.

2.5.2. Balancing Benefits against Implementation Costs Finally, both Galbraith (1973) and Tushman and Nadler (1978) emphasize the importance of balancing greater effectiveness of more complex information-processing mechanisms against their greater costs: [T]he basic design problem is to balance the costs of information processing capacity against the needs of the subunit’s work-too much capacity will be redundant and costly; too little capacity will not get the job done. (Tushman & Nadler, 1978, p. 619) While organizational information processing theory does not give much detailed help in conceptualizing the impact of data integration on the costs of design and implementation of systems, we can resort to arguments on design difficulty in general (Simon, 1981) and the design and implementation of IS in particular (Banker & Kemerer, 1989; Brooks, 1975; Martin, 1982). These suggest that data integration could have a positive impact on reducing costs by reducing redundant design efforts. However, because multiple subunits would be involved, data integration could also have a negative impact on costs by increasing the size and complexity of the design problem or increasing the difficulty in getting agreement from all concerned parties. If we consider only those situations where uncertainty dominates and IS in general are very appropriate choices, this suggests that the impact of data integration on the costs and benefits of IS will come primarily through three potential factors: (1) increased ability to share information to address subunit interdependence; (2) reduced ability to meet unique subunit information requirements; and (3) changes in the costs of IS design and implementation.

22    Cost Engineering and Pricing in Autonomous Manufacturing Systems

2.6. Discussions and Concluding Remarks With the usage of products and especially manufacturing systems with switched operation demands, we expect a linked switch of paradigms. The future producer of manufacturing systems pursues the expansion of his responsibilities and business areas. The producer becomes user and recycler of his own products. The evaluation of the resulting benefit calls for a new method to account costs and revenues in the life cycle of the products. By means of the method “Life Cycle Costing” the costs of production, installation, usage and disposal are analyzed, so a minimum of the total cost and a maximum of benefit are achieved. This optimization process supports the adaptation of the life cycle processes by observing the demands and constraints of the life cycle management. The life cycle cost accounting has to prove the thesis, that longevity of products including the permanent upgrading of the operating system is ecologically and economically useful. The structures of costs and revenues of the life cycle justify the operation of new innovative products or system concepts, possibly new operational and maintenance concepts, and/or new financing models and cooperation forms. Indeed, the understanding of the complexity of a system is a first step to the understanding of the behavior of that system (Choi et al., 2001). In this chapter, we tried to understand how varying levels of automation supply complexity affect transaction costs, supply risk, supplier responsiveness, and supplier innovation, and we articulated them in both linear and nonlinear relationships. We would submit that by considering the complexity of automation supply, we were able to paint a more complete picture of how to manage an automation supply. In particular, nonlinear relationships captured in our propositions offer a novel approach to considering the automation supply management and may play a key role in extending future studies. Researchers should take care to define the automation supply to include only suppliers who are being actively managed by the focal firm. The automation supply is the unit of analysis. For example, “number of suppliers” could be measured in terms of whether the focal firm has purchased a product or service from the supplier over the past 12 months. Further, a scale addressing “level of differentiation” among suppliers could tap many dimensions including geographical location, culture, supplier size, unionized or not, technical sophistication, industry, and so on. “Interrelationships among suppliers” may require surveys and case studies directly with the focal firm’s suppliers. The existence of supplier associations, annual supplier awards, trade associations, and similar activities may provide measures of interconnectedness among suppliers with valid and reliable measures of these constructs. Researchers will come to a better understanding of automation supply complexity and its effects on transaction costs, supply risk, supply responsiveness, and innovation. Such efforts should provide insights for industry managers as well. Although automation supply complexity is itself a complex topic, we believe that focusing solely on transaction costs is short-sighted automation supply management practice. The present chapter introduced a heterarchical architecture featuring local scheduling/routing of the automation order already under production. The rational for adopting a more heterarchical model is avoiding the long decision times of hybrid and

Concepts of Costing in Automation    23 traditional approaches in systems that are prone to frequent changes. The results suggest that the proposed architecture can cope well with systems under extreme dynamic conditions such as runtime topological changes. The results also show that the architecture and the particular instantiation considered are sensitive to mechatronic constraints. Some are directly related to the structure of the system while others have a more technological background. It is therefore rather difficult to benchmark the emerging architectures and the proposed work is not an exception. In this context, the proposed test cases were designed to expose the adaptation capabilities of the proposed architecture. As detailed, this is an indication that a pre-selection mechanism should be considered. The development of such a mechanism is currently being considered as a mean to further improve the dynamics of the automation system. Widespread data integration is an expensive proposition. While much of the literature focuses on some attractive benefits, we have tried to balance the view by looking at both positive and negative impacts. The arguments for this new perspective have been primarily theoretical. Though anecdotal evidence was presented to bolster the plausibility of the theoretical propositions, future empirical work will be needed to validate them. The major implication of this analysis is that in general it will not be cost-effective to integrate all of an organization’s data. If this is true, organizations will need help in their efforts to “partially integrate” to achieve the most important benefits and avoid the most burdensome costs. For the MIS field to provide this help, we need to “unbundle” our concept of data integration. It is neither an all-or-nothing choice, nor is it the preferred approach. As researchers and practitioners, we need to change our thinking so that we can provide guidance to organizations on implementing “partial integration.” Additional conceptual work and empirical research is needed to suggest which of these or other partial integration approaches will be most effective, and why. A second and equally pressing need is for methods that can help an organization determine which data should be integrated and which should not. Existing automation information engineering methodologies have overlooked this question because they assumed that all data should be integrated, though some other IS planning approaches, such as critical success factors (Rockart, 1979), may be applicable for the partial integration problem. The choice presumably depends heavily on the strategic direction of the organization. Firms that attempt to integrate an in appropriate subset of the data, or on a wider scope and in more detail than is appropriate given their organizational situation, will probably face stiff resistance to either the large cost involved or the loss of local flexibility of heterogeneous subunits. Though this chapter has pointed out some negative aspects to data integration, many organizations today are striving for a more global consciousness of their businesses and a more global response to customers and markets. Huber (1984) suggests that managers in post-industrial organizations will need to access more information about more aspects of the business, from even more parts of the organization, and in shorter time spans. This implies both greater interdependence and greater reliance on computer-based information. These types of changes in the organizational climate will probably shift the balance toward the need for greater (but not total) data integration in many firms, heightening the practical and academic importance of this area of research.

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Chapter 3

Concepts of Pricing in Automation 3.1. Overview Pricing is the process whereby a business sets the price at which it will sell its products and services, and may be part of the business’s marketing plan. In setting prices, the business will take into account the price at which it could acquire the goods, the manufacturing cost, the market place, competition, market condition, brand, and quality of product. Pricing is a fundamental aspect of financial modeling and is one of the four Ps of the marketing mix. (The other three aspects are product, promotion, and place.) Price is the only revenue-generating element among the four Ps, the rest being cost centers. However, the other Ps of marketing will contribute to decreasing price elasticity and so enable price increases to drive greater revenue and profits. Pricing can be a manual or automatic process of applying prices to purchase and sales orders, based on factors such as a fixed amount, quantity break, promotion or sales campaign, specific vendor quote, price prevailing on entry, shipment or invoice date, combination of multiple orders or lines, and many others. Automated systems require more setup and maintenance but may prevent pricing errors. The needs of the consumer can be converted into demand only if the consumer has the willingness and capacity to buy the product. Thus, pricing is the most important concept in the field of marketing and it is used as a tactical decision in response to comparing market situations.

3.2. Introduction and Related Works The objectives of pricing should consider the following: ⦁⦁ the financial goals of the company (i.e., profitability); ⦁⦁ the fit with marketplace realities (will customers buy at that price?); and ⦁⦁ the extent to which the price supports a product’s market positioning and be

consistent with the other variables in the marketing mix.

Price is influenced by the type of distribution channel used, the type of promotions used, and the quality of the product. Where manufacturing is expensive, distribution is exclusive, and the product is supported by extensive advertising

Cost Engineering and Pricing in Autonomous Manufacturing Systems, 25–42 Copyright © 2019 by Emerald Publishing Limited All rights of reproduction in any form reserved doi:10.1108/978-1-78973-469-020191003

26    Cost Engineering and Pricing in Autonomous Manufacturing Systems and promotional campaigns, then prices are likely to be higher. Price can act as a substitute for product quality, effective promotions, or an energetic selling effort by distributors in certain markets. From the marketer’s point of view, an efficient price is a price that is very close to the maximum that customers are prepared to pay. In economic terms, it is a price that shifts most of the consumer economic surplus to the producer. A good pricing strategy would be the one which could balance between the price floor (the price below which the organization ends up in losses) and the price ceiling (the price by which the organization experiences a no-demand situation). Also, competition in manufacturing industry calls for transformation from traditional cost reduction in mass production to value proposition in customeroriented product development. The driving force is the development of technologies to enable faster and seamless information sharing between manufacturers within supply chains as well as customers, government, and other stakeholders. Information flow becomes a critical element of supply chain in addition to materials and energy flows. With the support of information infrastructure, manufacturers will be able to become more agile in acquiring information, internally (e.g., cost of production and inventory) or externally (e.g., customer satisfaction and market trends). With such information, more intelligent and timely decisions can be made to ensure success in global competition. Marketers develop an overall pricing strategy that is consistent with the organization’s mission and values. This pricing strategy typically becomes part of the company’s overall long-term strategic plan. The strategy is designed to provide broad guidance to price-setters and ensures that the pricing strategy is consistent with other elements of the marketing plan. While the actual price of goods or services may vary in response to different conditions, the broad approach to pricing (i.e., the pricing strategy) remains a constant for the planning outlook period which is typically 3–5 years, but in some industries may be a longer period of 7–10 years. Broadly, there are six approaches to pricing strategy mentioned in the marketing literature: Operations-oriented pricing: The objective is to optimize productive capacity, to achieve operational efficiencies, or to match supply and demand through varying prices. In some cases, prices might be set to de-market. Revenue-oriented pricing: (also known as profit-oriented pricing or cost-based pricing) The marketer seeks to maximize the profits (i.e., the surplus income over costs) or simply to cover costs and break even. For example, dynamic pricing (also known as yield management) is a form of revenue-oriented pricing. Customer-oriented pricing: The objective is to maximize the number of customers, encourage cross-selling opportunities, or to recognize different levels in the customer’s ability to pay. Value-based pricing: (also known as image-based pricing) Occurs where the company uses prices to signal market value or associates price with the desired value position in the mind of the buyer. The aim of value-based pricing is to reinforce the overall positioning strategy, for example, premium pricing posture to pursue or maintain a luxury image.

Concepts of Pricing in Automation    27 Relationship-oriented pricing: The marketer sets prices in order to build or maintain relationships with existing or potential customers. Socially oriented pricing: The objective is to encourage or discourage specific social attitudes and behaviors, for example, high tariffs on tobacco to discourage smoking. Consumers can have different perceptions on premium pricing, and this factor makes it important for the marketer to understand consumer behavior. According to Vigneron and Johnson’s figure on “Prestige-Seeking Consumer Behaviors,” consumers can be categorized into four groups; these are Hedonist & Perfectionist, snob, bandwagon, and veblenian. These categories rank from level of self-consciousness, to importance of price as an indicator of prestige. The veblen effect explains how this group of consumers makes purchase decisions based on conspicuous value, as they tend to purchase publicly consumed luxury products. This shows they are likely to make the purchase to show power, status, and wealth. Consumers who fall under the “snob effect” can be described as individuals who search for perceived unique value, and will purchase exclusive products in order to be the first or very few who have it. They will also avoid purchasing products consumed by a general mass of people, as it is perceived that items in limited supply hold a higher value than items that do not. The bandwagon effect explains that consumers who fit into this category make purchasing decisions to fit into a social group, and gain a perceived social value out of purchasing popular products within said social group at premium prices. Research shows that people will often conform to what the majority of the group they are a member of thinks when it comes to the attitude of a product. Paying a premium price for a product can act as a way of gaining acceptance, due to the pressure placed on them by their peers. The Hedonic effect can be described as a certain group of people whose purchasing decisions are not affected by the status and exclusivity gained by purchasing a product at a premium, nor susceptible to the fear of being left out and peer pressure. Consumers who fit into this category base their purchasing decisions on a perceived emotional value, and gain intangible benefits such as sensory pleasure, aesthetic beauty, and excitement. Consumers of this type have a higher interest on their own well-being. The last category on Vigneron and Johnson’s figure of “Prestige-Seeking Consumer Behaviors” is the perfectionism effect. Prestige brands are expected to show high quality, and it is this reassurance of the highest quality that can actually enhance the value of the product. According to this effect, those that fit into this group value the prestige’s brands to have a superior quality and higher performance than other similar brands. Research has indicated that consumers perceive quality of a product to be relational to its price. Consumers often believe a high price of a product indicates a higher level of quality. Even though it is suggested that high prices seem to make certain products more desirable, consumers who fall in this category have their own perception of quality and make decisions based upon their own judgment. They may also use the premium price as an indicator of the product’s level of quality. Concession pricing belongs to the category of semi-structured decision-­ making; it faces many complicated and changeable decision settings. Some

28    Cost Engineering and Pricing in Autonomous Manufacturing Systems parameters can be quantified, such as the engineering project’s total investment cost, operation cost, loan interest, etc., while a considerable number of deterministic parameters cannot be described in quantifiable terms, such as the degree of government support, price competitiveness, etc. (Yu, 2006). Using a mathematical model to determine concession pricing problems will often neglect much of important decision-making information that cannot be quantified (Yu, 2006). Therefore, price adjustment is necessarily integrated into the pricing process. Moreover, parameters of (public–private partnership) PPP concession pricing involve a complex structure arising from (1) multiple interdependent components affecting concession price internally through cause and effect feedback loops and (2) the risk factors that may influence pricing parameters dramatically. To capture the complexity and dynamism of parameters in a holistic manner, the determination of concession price is divided into two parts in this study: (1) calculate the basic price (BP) of an automation project based on quantifiable parameters by using system dynamics (SD) model; (2) make consideration of the effect of risk factors on quantifiable pricing parameters and adjust the basic concession price to form final price (FP). BP determination occupies a rather important position in price decision-making. It is the bottleneck of the concession pricing and also the focus of this study. Premium pricing (also called prestige pricing) is the strategy of consistently pricing at, or near, the high end of the possible price range to help attract statusconscious consumers. The high pricing of a premium product is used to enhance and reinforce a product’s luxury image. Examples of companies that partake in premium pricing in the marketplace include Rolex and Bentley. As well as brand, product attributes such as eco-labeling and provenance (e.g., “certified organic” and “product of Australia”) may add value for consumers and attract premium pricing. A component of such premiums may reflect the increased cost of production. People will buy a premium priced product because of the following: ⦁⦁ They believe the high price is an indication of good quality. ⦁⦁ They believe it to be a sign of self-worth – “They are worth it”; it authenticates

the buyer’s success and status; it is a signal to others that the owner is a member of an exclusive group. ⦁⦁ They require flawless performance in this application – The cost of product malfunction is too high to buy anything but the best – for example, a heart pacemaker. The old association of luxury only being for the kings and queens of the world is almost nonexistent in today’s world. People have generally become wealthier; therefore the mass marketing phenomenon of luxury has simply become a part of everyday life, and no longer reserved for the elite. Since consumers have a larger source of disposable income, they now have the power to purchase products that meet their aspirational needs. This phenomenon enables premium pricing opportunities for marketers in luxury markets. Luxurification in society can be seen when middle-class members of society are willing to pay premium prices

Concepts of Pricing in Automation    29 for a service or product of the highest quality when compared with similar goods. Examples of this can be seen with items such as clothing and electronics. Charging a premium price for a product also makes it more inaccessible and helps it gain an exclusive appeal. Luxury brands such as Louis Vuitton and Gucci are more than just clothing and become more of a status symbol. Prestige goods are usually sold by companies that have a monopoly on the market and hold competitive advantage. Due to a firm having great market power they are able to charge at a premium for goods, and are able to spend a larger sum on promotion and advertising. According to Han, Nunes and Dreze (2015) figure on “signal preference and taxonomy based on wealth and need for status” two social groups known as “Parvenus” and “Poseurs” are individuals generally more selfconscious, and base purchases on a need to reach a higher status or gain a social prestige value. Further market research shows the role of possessions in consumer’s lives and how people make assumptions about others solely based on their possessions. People associate high-priced items with success. Marketers understand this concept, and price items at a premium to create the illusion of exclusivity and high quality. Consumers are likely to purchase a product at a higher price than a similar product as they crave the status, and feeling of superiority as being part of a minority that can in fact afford the said product. A price premium can also be charged to consumers when purchasing ecolabeled products. Market-based incentives are given in order to encourage people to practice their business in an eco-friendly way in regard to the environment. Associations such as the Marine Stewardship Council (MSC)’s fishery certification programmer and seafood eco-label reward those who practice sustainable fishing. Pressure from environmental groups has caused the implementation of associations such as these, rather than consumers demanding it. The value consumer’s gain from purchasing environmentally conscious products may create a premium price over noneco-labeled products. This means that producers have some sort of incentive for supplying goods worthy of eco-labeling standard. Usually more costs are incurred when practicing sustainable business, and charging at a premium is a way businesses can recover extra costs. The summary of the related literature is shown in Table 3.1.

3.3. Model Development In this section, we provide a mathematical representation of the residential load control problem in real-time pricing (RTP) environments inclining block rate (IBR). We consider the general wholesale electricity market scenario, where each retailer/utility serves a number of end users. The RTP information, reflecting the wholesale prices, is informed by the retailer to the users over a digital communication infrastructure, for example, a local area network (LAN). In this scenario, our focus is to formulate the energy consumption scheduling problem in each household as an optimization problem that aims to achieve a trade-off between minimizing the electricity payment and minimizing the waiting time for the operation of each household appliance in response to the real-time prices announced by the retailer company.

30    Cost Engineering and Pricing in Autonomous Manufacturing Systems Table 3.1:  The Summary of Pricing Literature. No.







Concession period determination


2007 2007

Concession price and period Concession price


Concession period


Ng, Xie, Skitmore, and Cheung Subprasom and Chen Shen, Bao, Wu, and Lu Huang and Chou



Shen and Wu


Minimum revenue guarantee Concession period


Cheng and Tiong



Ye and Tiong


9 10

Shen, Li, and Li Ngee, Tiong, and Alum

2002 1997

3 4

Tariff design in BOT water supply projects Concession period design Concession period Concession pricing

Methods Critical path method and Monte Carlo simulation technique Fuzzy simulation Genetic algorithm and case study Bargaining–game theory Real option approach Net present value (NPV) and Monte Carlo simulation NPV and risk analysis (risk allocation) NPV and Monte Carlo simulation NPV Multi-linear regression model

3.3.1. Automation Energy Pricing Model Consider a manufacturing unit that participates in a real-time pricing program. Let x denote the set of devices in this unit, which may include robots and other automation devices. For each device, we define an energy consumption scheduling vector as follows:

xa = [ xa1 ,..., xaH ](3.1)

where H ≥ 1 is the scheduling horizon that indicates the number of hours ahead which are taken into account for decision-making in energy consumption scheduling. For example, H = 24 or H = 48. For each upcoming hour of the day h ∈ Η  {1,..., H }, a real-valued scalar xah ≥ 0 denotes the corresponding 1-h energy consumption that is scheduled for an autonomous device a ∈ Α. On the other hand, let Ea denote the total energy needed for the operation of autonomous device a ∈ Α . For example, in the case of a robot, in total Ea = 16 kWh is needed to charge the battery for a 40-mi driving range (Ipakchi & Albuyeh, 2009). Next,

Concepts of Pricing in Automation    31 assume that for each device a ∈ Α , the decision maker indicates ασ , βσ ∈ H as the beginning and end of a time interval in which the energy consumption for autonomous device a is valid to be scheduled, respectively. Clearly, we always have ασ < βσ. For example, after loading a robot with the products to be moved to the next processing station, the decision maker may select ασ = 2 PM and βσ = 6PM for scheduling the energy consumption for the robot as he expects the products to be ready to use by the next station. Given the predetermined parameters Ea , ασ , and βσ , in order to provide the needed energy for each autonomous device a ∈ Α in times within the interval [ ασ , βσ ] , it is required that β∂


h a

h = α∂

= Ea 0 (3.2)

Furthermore, to constraint (3.2), it is expected that xa = 0 for any h < ασ and h > βσ as no operation (thus energy consumption) is needed outside the timeframe [ ασ , βσ ] for autonomous device a. We note that the time length βσ − ασ needs to be larger than or equal to the time duration required to finish the normal operation of device a. All autonomous devices have certain maximum power levels denoted by γ amax , for each a ∈ Α . For example, a robot may be charged only up to γ amax = 33 kW (Ipakchi & Albuyeh, 2009). Some devices may also have minimum stand-by power levels γ amin , for each a ∈ Α . Therefore, the following lower and upper bound constraints are needed on the choices of the energy scheduling vector X a for each device a ∈ Α : γ amin ≤ xah ≤ γ amax , ∀h ∈ [ ασ , βσ ].(3.3)

Finally, we note that there is usually a limit on the total energy consumption at each manufacturing unit at each hour. This limit, denoted by E max , can be set by the utility to impose the following set of constraints on energy scheduling:


h a

≤ E max , ∀h ∈ H .(3.4)


Together, constraints (3.2)–(3.4) determine all valid choices for the energy consumption scheduling vectors. Therefore, we can define a feasible scheduling set X for all possible energy consumption scheduling vectors as X =x|



h a

= Ea , ∀a ∈ A,

h = ασ

γ amin ≤ xah ≤ γ amax , ∀a ∈ A, h ∈ [ ασ , βσ ], xah = 0,


h a

≤ E max ,


∀a ∈ A, h ∈ H \ [ ασ , βσ ], ∀h ∈ H }

α∈ A

where x = ( xa , ∀a ∈ A) denotes the vector of energy consumption scheduling variables for all devices. An energy schedule X is valid only if x ∈ X . Clearly, the proper choice of x would depend on the electricity prices. The real-time prices are provided by the utility company via a LAN. The user announces his needs

32    Cost Engineering and Pricing in Autonomous Manufacturing Systems by selecting parameters Ea , ασ , βσ , γ amin and γ amax for each device a ∈ Α . Then, the energy scheduler, with some help form the price predictor if needed, determines the optimal choice of energy consumption scheduling vector x. The resulting energy consumption schedule is then applied to all autonomous manufacturing devices in form of on/off commands with specified power levels over a wired or wireless manufacturing network among the devices and the smart meter.

3.3.2. Concession Pricing Model In another case, the pricing methodology employed in designing an objective and reliable concession pricing model for automation project is based on a comprehensive literature review for data collection and SD and case-based reasoning (CBR) as quantitative tools for data analysis. Fig. 3.1 shows the flow of the overall research framework, which consists of the following steps. (1) Identify the pricing parameters and risk factors of an automation project, and divide them into two categories. One is pricing parameters, which are

Fig. 3.1:  Comprehensive Structure for Pricing in Automation Projects.

Concepts of Pricing in Automation    33 quantifiable variables that have been clearly defined in project’s feasibility study report. The second category comprises uncertain risk factors, which are unquantifiable. (2) Calculate the BP of automation project based on quantifiable pricing parameters using SD. (3) Verify the reliability and accuracy of the proposed SD-based concession pricing model through an automation project as the case study. (4) Consider the effect of uncertainty (risk factors) on concession pricing; past experiences of similar cases are used to determine the price adjustment coefficient utilizing CBR technique. (5) Finally, the FP of target automation projects can be determined based on the results of steps 2–4. Concession Pricing Parameters and Risk Factors. Zhang (2009) considered that a functional pricing mechanism should (1) clearly define the cost and revenue structure that is necessary for the concessionaire to maintain the project at a required level of service in the operation period and to cover the initial construction investment and (2) assess the impacts of main factors that affect the cost and revenue structure. Automation project concession price determination is usually conducted after the project feasibility study on the basis of taking an overall consideration of all parameters that affect the price. Parameters directly related to concession pricing involve two categories: one is the cash outflow (cost) parameters and, the other is cash inflow (benefit) parameters. The concession price can be determined by cash inflow parameters and cash outflow parameters jointly through NPV calculation as shown in Fig. 3.2. This section aims to briefly describe the pricing parameters of automation projects. The real pricing data and information extracted from automation project feasibility report are used to formulate the pro forma cash flow. The total configuration investment and operation cost have been calculated by their subparameters, respectively, which constitute the cash outflow. SD is used to systematically consider price parameters (variables) of automation projects for concession pricing determination. With reference to findings from previous research, the overall structure of concession pricing variables using causal loop is constructed with the use of Vensim PLE32 software as shown in Fig. 3.3 (Shen et al., 2002; Shen & Wu, 2005). It is beneficial to visualize how interrelated variables affect one another (Ogunlana, Li, & Sukhera, 2003). The influence diagram depicts a succession of causation so that the cause and effect relationship can be traced by following the direction of the arrows (Love, Mandal, Smith, & Li, 2000). This indicates whether there is an increasing or decreasing relationship between two variables, which was denoted by a polarity, that is, positive “+” or negative “−” on the arrow. Any change of pricing parameters will have an effect on concession price. Fig. 3.2 shows that total cost of automation projects is jointly determined by configuration investment cost and operation cost; the configuration investment cost can have positive influence operation cost and cause higher total cost through the feedback loop. Higher configuration

34    Cost Engineering and Pricing in Autonomous Manufacturing Systems

Fig. 3.2:  Concession Pricing Parameters of Automation Projects. investment will induce an increase in the length of the automation process, therefore resulting in a higher operation cost. To compensate for the increase in total cost, the concession price will need to be amplified. Meanwhile, there is a negative feedback loop among the concession price, annual automation depreciation and concession period. An increase in depreciation or concession period will affect the concession price adversely. In this study, the concession period is assumed to be fixed. Other critical parameters including loan, tax rate, government subsidy, and income are also included in this model. Based on this, the total net benefit and NPV can be determined by the annual total cost and annul total income at the discount rate of minimum attractive rate of return (MARR). Such circular cause and effect relationships provide the foundation for building quantitative concession pricing model via SD. The NPV of an automation project refers to the sum of the present values of a stream of cash flow. If NPV is above or equal to 0, the project is deemed as acceptable as it can achieve the target MARR, and vice versa (Huang, Xu, Tan, & Li, 2004). Automation concession price can be divided into two parts. Part one is the BP, which can be calculated through SD model based on actual data extracted from feasibility study report of an automation project. Part two is the adjustment price (AP), which is determined based on the value of risk impact on concession price. The combination of both makes up the FP of the service of automation projects, which can better guarantee the rationality and validity of the concession price.

Concepts of Pricing in Automation    35

Fig. 3.3:  Causal Loop Diagram for Concession Pricing of Automation Projects. To facilitate the determination of AP, the ratio between automation project’s AP and BP is defined as price adjustment coefficient and denoted by λ: Final Price = Basic Price +Adjustment price

Final price = (1+λ) Basic Price

Then, the problem of sophisticated price adjustment of automation project is transformed into the determination of the price adjustment coefficient. It will not only make the settlement of the BP adjustment simpler and faster, but can also greatly improve the speed and quality of the pricing process (Yu, 2006). Provided that two most similar reference cases are identified from the database, their BP can be calculated by SD model through inputting the relevant data into the computerized pricing model, and then the price adjustment coefficient can be determined by FP and BP jointly as shown in Table 3.2. Based on this, the concession adjustment coefficient of target automation project can be determined by the formula (3.7). Project risk similarity (PRS) represents the degree of risk similarity between reference case and target case:

36    Cost Engineering and Pricing in Autonomous Manufacturing Systems PRS2 − PRS1 0 − PRS1 (3.6) = λ2 − λ1 λ − λ1

λ = λ1 − PRS1

λ2 − λ1 (3.7) PRS2 − PRS1

And the final concession price of the automation project can therefore be determined: λ2 − λ1 Finalprice = Basicprice * (1+ λ1 − PRS1 )(3.8) PRS2 − PRS1

3.3.3. Representative Automation Pricing Methods Autonomous devices mainly use electricity to work out. Heat spread by the electrical autonomous devices affects automation production. However, the effect of market forces on automation production is weaker than on electricity production because the heating network is smaller in scale and automation heating schemes are often owned by a single entity. There are two basic types of electricity markets worldwide; regulated and deregulated. In regulated markets, there is no competition, and the automation electrical energy price is government regulated. By contrast, in deregulated markets, electrical energy competes freely with other options and the price is derived in the market. It is difficult to determine which approach is best for the market. However, it is certain that the market cannot be liberalized fully or regulated fully; instead, it has gradually become the consensus that there should be free competition on the basis of control. Product pricing is one of the key elements of automation market reform. There are currently two representative methods in automation product pricing. The first is the cost-plus method, which is mainly used in regulated markets. The other is the marginal-cost pricing method, which is often utilized in deregulated markets. In the cost-plus method, all costs associated with the product are added to the tax charged and a specific profit margin to determine the FP. Cost-plus pricing offers a number of advantages to sellers, buyers, and regulators, such as simplicity, flexibility, and ease of administration. It is used widely in countries such as China and those of Eastern Europe, as shown in Table 3.2, in which

Table 3.2:  Price Adjustment Coefficient of Reference Cases.  


Case 1

Determined by SD model

Case 2

Determined by SD model

FP Actual price of automation product or services Actual price of automation product or services

Price Adjustment Coefficient λ1=FP/BP−1 λ2= FP/BP−1

Concepts of Pricing in Automation    37 the markets are regulated (Lukoseviciu, 2008). However, the cost-plus method is usually based on the historical data of real plants and does not cover all of the costs. Moreover, because the profit allowed is typically derived from total costs, there is an incentive to inflate costs and thereby increase profits (Poputoaia & Bouzarovski, 2010). Companies that are efficient and manage to reduce their costs are punished with lower profits (Korppoo & Korobova, 2012; Meyer & Kalkum, 2008). Therefore, the cost-plus method offers no incentives for suppliers to lower costs or find faster, cheaper, and more efficient ways of producing automation products. The cost-plus method may promote investment to a certain extent but cannot enhance the efficiency of the market simultaneously. The marginal-cost method is often used in deregulated markets (Difs & Trygg, 2009; Rolfsman & Gustafsson, 2003; Sjödin & Henning, 2004). Marginal cost is the cost of the last unit produced, which, in this case, is the cost of a one-unit increase in automation electrical energy consumption. In addition, there is shortrun marginal cost (SRMC) and long-run marginal cost (LRMC). Investment is fixed for SRMC and variable for LRMC. SRMC and LRMC will be equal if the suppliers’ installation mix is optimal (Rolfsman & Gustafsson, 2003). Optimal prices should equal the SRMC of electrical energy generation, from a societal perspective. These prices reflect the scarcity of resources in society and are the best means for optimal resource allocation (Sjödin & Henning, 2004). However, marginal-cost pricing is based on ideal market theory. In reality, because of various constraints, marginal-cost pricing is difficult to achieve and its effects are difficult to guarantee, particularly in natural monopoly markets. The energy market is a typical natural monopoly market; however, marginal-cost pricing of automation products may be approximately achieved through unregulated market bidding. For example, in Sweden and Finland, as shown in Table 3.3, companies are assumed to work in a businesslike manner and are consequently free to set prices (Ericsson & Svenningsson, 2009; Hansson, 2009; Kostama, 2011). The lack of control in market bidding may lead to unintended consequences that result from emphasizing market efficiency and neglecting investment guidance. Thus, the opening of the electricity markets in Sweden has led to a lower interest in investment and maintenance. Furthermore, the 2001 California electricity crisis may also be a lesson for the energy market because there are many similarities between the electricity and energy markets. In one single automation project, products from different units can be replaced by one another in production sequence. The electricity value equivalent (EVE) pricing method may be applied to automation product pricing directly in one single company. From the perspective of optimal market allocation, automation products from different automation companies should be put into the same platform to compete under the same conditions. Although the products of one automation company may not be replaced by the products of another automation company in reality, its production and its FP may be effectively controlled by the guidance of quoted cost and return on investment cost. For simplicity, we assume that a large company consists of three independent automation enterprises called RA , RB and RC – with the same characteristics. The only difference among them is quoted cost. First, the load duration curves (LDCs)

38    Cost Engineering and Pricing in Autonomous Manufacturing Systems Table 3.3:  Energy Pricing Mechanisms in Some Countries. Country China

Market (Method) Regulation (Cost-plus)

Specific (1) Price= costs + taxes + profits. (2) Reasonable losses in transmission may be counted into costs.


Regulation (Cost-plus)


Regulation (Cost-plus)


Deregulation (Competition)


Deregulation (Competition)

(3) The rate of return shall be no higher than 3% or their turn on equity shall be 2 to 3 percentage points higher than the interest rate of long-term (five years or more) treasury bonds (1) The price is based on the cost reported to the regulator. (2) Price regulation is in fact “political” and income for the supplied heat covers only half of real expenses (1) Prices do not cover all of the costs. (2) Justified return WACC on RAB temporarily decreased to 5%. (3) Prices are revised twice a year (1) Municipal energy companies should be operated in a business-like fashion. (2) The price of district heating varies significantly between different counties and different firms (1) Prices set by DH companies based on costs and competition on local heating markets. (2) Prices vary much between different companies depending on the actual operating costs

of the entire company and each single enterprise are drawn, as shown in Fig. 3.4; the upper curve represents the entire company’s load requirement for the next period and the lower curves represent each single enterprise. Apparently, the actual load duration time for each single enterprise, which is 24 h, cannot be replaced. Next, the average quoted cost of each enterprise is calculated based on the quoted cost that each automation producer provides. Assuming that the sequence of costs from low to high is RA < RB