Decision-Based Design: Integrating Consumer Preferences into Engineering Design

358 Pages·2013·5.483 MB·English

by  Wei Chen, Christopher Hoyle, Henk Jan Wassenaar (auth.)

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
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(cid:2)Springer-VerlagLondon2013
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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