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Decision-Based Design Wei Chen Christopher Hoyle • Henk Jan Wassenaar Decision-Based Design Integrating Consumer Preferences into Engineering Design 123 Wei Chen HenkJan Wassenaar Department of Mechanical Engineering Zilliant Inc. Northwestern University Capitalof TexasHighway 3815 Sheridan Road2145 Austin, TX78704 Evanston, IL 60208-3111 USA USA Christopher Hoyle Mechanical, Industrial &Manufacturing Engineering OregonState University 204Rogers Hall Corvallis, OR97331-6001 USA ISBN 978-1-4471-4035-1 ISBN 978-1-4471-4036-8 (eBook) DOI 10.1007/978-1-4471-4036-8 SpringerLondonHeidelbergNewYorkDordrecht LibraryofCongressControlNumber:2012936756 Winbugs(cid:2)1996-2008BUGS (cid:2)Springer-VerlagLondon2013 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionor informationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purposeofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthe work. Duplication of this publication or parts thereof is permitted only under the provisions of theCopyrightLawofthePublisher’slocation,initscurrentversion,andpermissionforusemustalways beobtainedfromSpringer.PermissionsforusemaybeobtainedthroughRightsLinkattheCopyright ClearanceCenter.ViolationsareliabletoprosecutionundertherespectiveCopyrightLaw. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexempt fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. While the advice and information in this book are believed to be true and accurate at the date of publication,neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityfor anyerrorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,with respecttothematerialcontainedherein. Printedonacid-freepaper SpringerispartofSpringerScience+BusinessMedia(www.springer.com) Preface Since the late 1990s, there has been a growing recognition in the engineering design research community that decisions are a fundamental construct in engi- neering design [3]. This position and its premise naturally leads to the study of how engineering designers should make choices during the design process, rep- resenting the foundation of an emerging perspective on design theory called decision-based design (DBD). As defined in Chen et al. [1]: Decision-Based Design (DBD) is an approach to engineering design that recognizes the substantial role that decisions play in design and in other engineering activities, largely characterized by ambiguity, uncertainty, risk, andtradeoffs. Through the rigorous appli- cation of mathematical principles, DBD seeks to improve the degree to which these activitiesareperformedandtaughtasrational,thatis,self-consistent,processes. DBD provides a framework [4] within which the design research community can conceive, articulate, verify, and promote theories of design beyond the tra- ditional problem-solving view. To support the scholarly dialogs on the develop- mentoftheDBDtheory,theU.S.NationalScienceFoundation(NSF)supporteda series of workshops under the ‘‘Open Workshop on DBD.’’ During the period of 1996–2005, the open workshop has engaged design theory researchers via elec- tronic and Internet related technologies as well as 18 face-to-face meetings in scholarly and collegial dialog to establish a rigorous and common foundation for DBD [1]. Thesynergybuilt through the workshop led tomany conference papers and journal publications, special editions of journals dedicated to DBD [2], suc- cessfulresearchworkshops,andabookentitled‘‘DecisionMakinginEngineering Design’’bytheNSFworkshoporganizers(eds.LewisK,ChenWandSchmidtL, [5]). As a result, the international design engineering technical conferences (IDETC) sponsored by the American Society of Mechanical Engineers (ASME) have established multiple technical sessions on DBD on various issues within DBD and research topics that grew out of the DBD paradigm. One example of such topic that attracts growing interest is on ‘‘Design for Market Systems.’’ By integrating quantitative engineering and economic models, the focus of ‘‘design for market systems’’ is to develop the ability to understand, predict, and account v vi Preface for the market implications of design decisions, namely, to predict expected marketresponsesbasedoncustomerchoicebehaviorasaresultofproductdesign decisions[7].Asdemonstratedinmanyworksinthisarea,aswellasinthisbook, modeling customer preferences and choice behavior is becoming an integral part of DBD. This book is, to a large extent, a collection of research results on DBD developed by the Integrated Design Automation Laboratory (IDEAL) led by ProfessorWeiChenatNorthwesternUniversityduringthepastdecade.Whilethe fundamental principles of decision analysis are generally applicable to decision making in engineering design, as we will illustrate in this book, making design decisions in a rigorous way is not trivial. This is due to the fact that engineering design is a complex decision-making process that involves multiple parties in an enterpriseandrequirestradeoffsofinterestsamongdifferentgroups.Themethods presented in this book represent our view of how DBD can be implemented in a rigorous way for engineering design. In particular, our book offers a more com- plete coverage of modeling and integrating customer preference into engineering design. Buildinguponthefundamentalprinciplesofdecisiontheory,thisbookpresents a single-criterion approach to enterprise-driven DBD as a rigorous framework for integrating engineering design and business decision making. This book begins withintroductionstothefundamentalsofdecisiontheory,economicanalysis,and econometrics modeling, together with an examination of the limitations of some existing design selection methods. The core portion of the book describes the entire process and the associated analytical techniques for integrating customer preference modeling into the enterprise-driven DBD framework to bridge the gap betweenmarketanalysisandengineeringdecisionmaking.Tofacilitatetheuseof discrete choice analysis [6] as a fundamental technique for customer choice modelinginproductdesign,methodsforattributeidentification,optimaldesignof human appraisal experiments, data collection, data analysis, and demand model estimationarepresentedandillustratedusingengineeringdesigncasestudies.The book also presents the state-of-the-art research methods that address current product design challenges, including hierarchical choice modeling to support complex systems design, latent variable modeling, choice modeling for usage- contextbaseddesign,enterprise-drivenapproachtoproductfamilydesign,aswell as multilevel optimization for DBD. The content and format of this text has been designed to benefit a number of different audiences, including: • Graduate students at all levels in engineering or product design disciplines; • Instructors teaching design courses; • Researchers in product design/development; and • Design practitioners in the field facing the challenge of designing customer goods for a diverse population. Preface vii Each of the primary chapters contains example problem(s) which illustrate the techniques presented in the chapter as well as additional resources for computer implementation.Exampleproblemscanbepracticedbythereaderusingthefreely available open source software as recommended at the end of chapters in the ‘‘Additional Resources forComputational Implementation’’section.Chapters1–7 ofthisbookcoverthefundamentaltechniques,andwouldbethemostappropriate for students or design practitioners. Chapters 8–12 cover advanced topics that would be of interest to graduate students and researchers working in the area of productdesign,customerchoicemodeling,orcomplexsystemdesign.Chapter13 provides a summary of the DBD approach and also discusses potential future research directions. This book is a collection of materials developed in the dissertations of several former and current doctoral students in the IDEAL at Northwestern University. Whilethemajorityofthechaptershavebeendevelopedbasedonthedissertations of Christopher Hoyle (PhD 2009—now Oregon State University) and Henk Jan Wassenaar (PhD 2003—now Zilliant, Inc.), we would also like to thank the con- tributionsfromLinHe(PhD2012),DeepakKumar(PhD2007—nowGoogle),and HarrisonKim(Postdoc2004—nowUniversityofIllinoisatUrbanaChampaign)to thematerialspresentedinChaps.10–12,respectively.Inunderstandingtheroleof decisiontheoryinengineeringdesign,wehavebenefitedfromtherichinteractions with Dr. George Hazelrigg (NSF) and Professor Donald Saari (University of California at Irvine). The development of real engineering applications based on real market research data was made possible through close interactions with our industrialcollaborators,includingDrs.NanxinWang,GinaGomez-Levi,andAgus Sudjianto from Ford Motor Company in developing the vehicle packaging design and engine design case studies; Drs. Jie Cheng and Jie Du from J.D. Power & Associatesincollectingandanalyzingvehiclemarketsurveydata;andDr.Guenter Conzelman from Argonne National Laboratory (ANL) in developing the hybrid vehicle usage context-based choice modeling. Furthermore, at Northwestern University, our work has benefited greatly from the interdisciplinary research collaborationswithProfessorFrankKoppelman,whoisanexpertintransportation engineering and discrete choice analysis, and Professor Bruce Ankenman, an expert in design of experiments and statistical analysis. Our work on the DBD approach to address product design challenges (Chaps. 8–12) could not have reached its current depth without close collaborations with several faculty mem- bersintheengineeringdesigncommunity,inparticular,ProfessorBernardYannou (Ecole Centrale Paris) on usage context-based design (Chap. 10), Professor Tim Simpson (Penn State) on product family design (Chap. 11), and Professor Panos Papalambros (University of Michigan) on multi-level DBD (Chap. 12). Pursuit of many of the topics covered in this book received financial support from multiple sources. Theoretical developments of the Decision-Based Design approach and the associated demand modeling techniques received support from the U.S. NSF, including DMI-9896300 (2002–2005) (Program manager: George Hazelrigg), DMI-0503781 (2005–2006) (Program manager: Delcie Durham) and CMMI-0700585 (2007–2010) (Program managers: Judy Vance and Christina viii Preface Bloebaum). International collaboration with Ecole Centrale Paris, France was supported in the form of summer visits by Wei Chen and Christopher Hoyle as a supplement to CMMI-0700585. The collaborations with Ford Motor Company were supported by the Ford University Program (URP) (2003–2006; 2006–2009). The choice modeling of alternative fuel vehicles in collaboration with ANL was supported by initiative for sustainability and energy at northwestern (ISEN). Finally, we would like topoint out that the views presentedin this book are from the authors but not their sponsors. References 1. Chen W, Lewis K, Schmidt L (2006) The open workshop on decision-based design. In: Lewis K, Chen W, Schmidt L (eds) Decision making in engineering design. ASME Press, NewYork,pp5–11 2. ChenW,LewisKE,SchmidtL(2000)Decisionbaseddesign:anemergingdesignperspec- tive.Engineeringvaluationandcostanalysis,specialeditionondecisionbaseddesign:Status andPromise3(2/3):57–66 3. HazelriggGA(1996)Systemsengineering:anapproachtoinformation-baseddesign.Prentice Hall,UpperSaddleRiver 4. Hazelrigg GA (1998) A framework for decision-based engineering design. J Mech Des 120(4):653–658 5. LewisK,ChenW,SchmidtL(2006)Decisionmakinginengineeringdesign.ASMEPress, NewYork 6. McFadden D (1974) Conditional logit analysis of qualitative choice behavior. In: Zarembka P (ed) Frontiers in econometrics. Academic Press, New York, pp 105–142 7. Michalek JJ (2008) Design for market systems: integrating social, economic, and physical sciencestoengineerproductsuccess.MechanicalEngineeringMagazineNov.7 Contents Part I Theory 1 Decision-Based Design: An Approach for Enterprise-Driven Engineering Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1 Motivation for Enterprise-Driven Decision-Based Design . . . . 3 1.2 Decision-Based Design: An Overview. . . . . . . . . . . . . . . . . . 5 1.3 Organization of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . 7 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Decision Theory in Engineering Design. . . . . . . . . . . . . . . . . . . . 13 2.1 Fundamentals of Decision Theory . . . . . . . . . . . . . . . . . . . . 14 2.2 Desirable Properties of a Design Selection Method and Related Economic Principles . . . . . . . . . . . . . . . . . . . . . 18 2.3 Paradox of Multi-Criteria Alternative Selection Methods for Engineering Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3.1 Paradox of Multiple Decision Makers or Multicriteria Alternative Selection Processes . . . . . . . . . . . . . . . . 21 2.3.2 Problems with Normalization, Scaling, and Translation . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.3 Problems with Assigning Weights . . . . . . . . . . . . . . 24 2.3.4 Problems with Multi-Attribute Ranking. . . . . . . . . . . 24 2.3.5 Problems with Multi-Attribute Utility Function . . . . . 25 2.3.6 Limitations of the Multi-Attribute Approach in Dealing with Uncertainty. . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.4 Limitations of Existing Design Approaches for Alternative Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.5 An Enterprise-Driven Design Approach to Modeling Designer Preference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 ix x Contents 3 Fundamentals of Analytical Techniques for Modeling Consumer Preferences and Choices. . . . . . . . . . . . . . . . . . . . . . . 35 3.1 Modeling Heterogeneous Customer Preference: State of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Discrete Choice Analysis for Choice Modeling . . . . . . . . . . . 38 3.2.1 Basic Concepts of Discrete Choice Analysis . . . . . . . 38 3.2.2 Multinomial Logit (MNL) . . . . . . . . . . . . . . . . . . . 42 3.2.3 Nested Logit (NL) . . . . . . . . . . . . . . . . . . . . . . . . . 44 3.2.4 Mixed Logit (MXL). . . . . . . . . . . . . . . . . . . . . . . . 46 3.2.5 Importance of Modeling Heterogeneous Customer Preferences . . . . . . . . . . . . . . . . . . . . . . . 47 3.3 Ordered Logit for Modeling Rating Responses. . . . . . . . . . . . 48 3.4 Computational Techniques for Estimation of DCA and OL Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.4.1 Maximum Likelihood Estimation. . . . . . . . . . . . . . . 50 3.4.2 Hierarchical Bayes Estimation. . . . . . . . . . . . . . . . . 51 3.5 Guidelines for Demand Estimation Process in Product Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.5.1 Attributes and Choice Set Identification . . . . . . . . . . 52 3.5.2 Data Collection, Stated Choice Versus Revealed Choice . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.5.3 Data Collection, Survey Respondent Sampling. . . . . . 53 3.5.4 Identification of Market Segments . . . . . . . . . . . . . . 55 3.5.5 Fitting the Choice Model. . . . . . . . . . . . . . . . . . . . . 56 3.5.6 Demand Estimation Using the Choice Model. . . . . . . 57 3.5.7 Dynamic Demand Modeling in Product Life Cycle. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.5.8 Choice Model Selection and Validation . . . . . . . . . . 60 3.6 Case Study: Walk-through of a Typical MNL Model Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 3.6.1 Constructing the Choice Set. . . . . . . . . . . . . . . . . . . 63 3.6.2 Walk-Through of a Typical MNL Model Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . 64 3.7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.8 Additional Resources for Computational Implementation . . . . 73 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 4 Decision-Based Design Framework . . . . . . . . . . . . . . . . . . . . . . . 79 4.1 Decision-Based Design Framework and Taxonomy . . . . . . . . 80 4.2 Integration of Discrete Choice Analysis Into DBD for Demand Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 4.2.1 Economic Principles for Demand Analysis . . . . . . . . 82 4.2.2 Capability of Discrete Choice Analysis Approach in Avoiding Arrow’s Impossibility. . . . . . . . . . . . . . 82

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