About the Authors

Wei Chen is the Wilson-Cook Chair Professor inEngineering Design at Northwestern University. She is aProfessor in the Department of Mechanical Engineering,with courtesy appointment in the Department ofIndustrial Engineering and Management, and is afaculty fellow of the Segal Design Institute. She is alsothe Director of the interdisciplinary doctoral cluster inPredictive Science and Engineering Design (PSED) atNorthwestern. Wei Chen received her PhD in

Mechanical Engineering from the Georgia Institute of Technology in 1995, herMS from the University of Houston in 1992, and her BS from Shanghai Jiao TongUniversity, China in 1988. Before joining Northwestern University in 2003, sheserved at Clemson University (1995–1998) and the University of Illinois atChicago (1998–2003). Directing the Integrated Design Automation Laboratory(IDEAL), her current research involves issues such as consumer choice modelingand enterprise-driven Decision-Based Design (DBD), simulation-based designunder uncertainty, model validation, stochastic multiscale analysis and design,robust shape and topology optimization, and multidisciplinary optimization. Topromote DBD as a new design paradigm that employs the classical decision theoryand rigorous mathematical principles to preference modeling and design decision-making, during 1996–2004, Chen co-organized the National Science Foundation(NSF) sponsored Open Workshop on Decision-Based Design, a series of 18 face-to-face meetings and website workshops (http://dbd.eng.buffalo.edu). Chen wasthe recipient of the 1996 NSF Faculty Early Career Award and the 1998 AmericanSociety of Mechanical Engineers (ASME) Pi Tau Sigma Gold Medal achievementaward. She was also the recipient of the 2005 Intelligent Optimal Design Prizeand the 2006 SAE Ralph R. Teetor Educational award. Chen is a fellow ofAmerican Society of Mechanical Engineers (ASME) and an associate fellow ofAmerican Institute of Aeronautics and Astronautics (AIAA). She is an electedmember of the ASME Design Engineering Division Executive Committee and an

W. Chen et al., Decision-Based Design,DOI: 10.1007/978-1-4471-4036-8, � Springer-Verlag London 2013

347

elected Advisory Board member of the Design society. Author of the book‘‘Decision Making in Engineering Design’’ (co-edited with Lewis, K. and Schmidt,L.) and over 190 technical papers, Chen is an Associate Editor of the ASMEJournal of Mechanical Design and serves as the review editor of Structural andMultidisciplinary Optimization. In the past, she was an Associate Editor of theJournal of Engineering Optimization (07–09).

web: http://ideal.mech.northwestern.edu/Email: weichen@northwestern.edu

Christopher Hoyle is currently an Assistant Professorand the Arthur E. Hitsman Faculty Scholar in the area ofDesign in the Mechanical, Industrial, and ManufacturingDepartment at Oregon State University. His current researchinterests are focused upon decision making in engineeringdesign, with emphasis on the early design phase whenuncertainty is high and the potential design space is large.His research contributions are to the field of DBD,specifically in linking consumer preferences and

enterprise-level objectives with the engineering design process. His areas ofexpertise are uncertainty propagation methodologies, Bayesian statistics andmodeling, stochastic consumer choice modeling, optimization and designautomation. Hoyle received his PhD from Northwestern University inMechanical Engineering in 2009, his Master’s degree in MechanicalEngineering from Purdue University in 1994, and his BS degree in GeneralEngineering from University of Illinois at Urbana-Champaign in 1988. He servedas an Adjunct Professor of Mechanical Engineering at Illinois Institute ofTechnology in 2009 and was a Summer Intern at the National Aeronautics andSpace Administration (NASA) Ames Research Center in 2006. He was previouslya Design Engineer and an Engineering Manager at Motorola, Inc. for 10 yearsbefore enrolling in the PhD program at Northwestern University.

web: http://web.engr.oregonstate.edu/*hoylec/Email: chris.hoyle@oregonstate.edu

Henk Jan Wassenaar is a Senior Scientist at Zilliant,Inc. specializing in developing pricing models forbusiness to business markets for Fortune 500companies. He was the first IDEAL laboratory studentin the field of Decision-Based Design under the directionof Dr. Chen. His research contribution includes the firstuse of discrete choice analysis to estimate the demand,later expanding it to include latent variables. His paper,

348 About the Authors

‘‘An approach to decision-based design with discrete choice analysis for demandmodeling,’’ is widely regarded as one of the cornerstones of the field. Hepreviously worked at J. D. Power and Associates conducting analysis of theautomotive industry including an approach to incentives and demand modeling.Wassenaar received his PhD from University of Illinois at Chicago in IndustrialEngineering and Operations Research in 2003 and his Master’s degree inMechanical Engineering from Delft University of Technology in 2000.

Email: wassenaer@yahoo.com

About the Authors 349

Index

AAcross-respondent to within-respondent

variance, 147Adaptive conjoint analysis, 141Aggregate approaches, 36Akaike information

criterion (AIC), 163, 172Algdesign, 159Aliasing, 136All-in-one (AIO) approach, 321All-in-one problem, 295Alternative (option), 23–26, 32Alternative fuel vehicle, 273Alternative Specific Constant (ASC), 42, 60,

67, 68Alternative specific variable (ASV), 42, 69Analysis of rating style, 177Analysis of variation, 175Analytic hierarchy process (AHP), 23Analytical target cascading, 319, 321,

322, 325Anova, 200Antecedent states, 264Application context, 259Arrow’s general (im)possibility theorem, 7,

18, 25, 28, 82, 83Attribute, 26, 29Attribute interrelationship, 111, 113, 119Attributes hierarchy, 93Augmented, 168Automotive occupant package, 144Automotive Performance, Execution

and Layout (APEAL) score, 247Axiomatic design, 115Axioms of rational behavior, 15

BBass technology diffusion model, 60Bayes law, 195Bayesian choice modeling framework

for multiple time-periods, 230Bayesian designs, 147Bayesian estimation techniques, 242Bayesian information criterion (BIC), 62, 98,

100Bayesian network, 164, 165, 186, 195, 200Bayesm package, 232Beachhead approach, 291Behavioral interpretations, 60Benchmarking, 209BHH2 package for R, 136Bias and scale usage heterogeneity, 177Binary probit choice model, 41Binary string, 295, 297Binomial choice, 238Block, 131, 139, 142–144, 146–151, 151,

153, 158Blocked and split-plot design of experiments,

139–143, 145, 149, 151, 152,154, 158, 168

Borda count voting, 25Bottom-up system design, 329

CC4.5 decision tree, 186, 200Candidate set, 145, 149, 150, 153, 157Cannibalism, 101Capacitative output, 122Cardinal, 50Chi-square test, 60, 67, 70

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C (cont.)Choice, 132–135, 137, 141–144, 151, 157, 40,

48, 58, 67, 68, 71Choice behavior, 256, 257, 261, 265, 341, 342Choice experiment, 262, 265, 269, 270Choice probability, 40, 41, 43, 45–48, 51, 56,

64, 71, 273, 280Choice probability function, 43, 45Choice set, 35, 36, 38–40, 43, 45, 48, 53, 60,

112, 116, 141–144, 151Choice set identification, 52Choice share, 37, 39, 48, 55, 58, 59Choice share/segment prediction tests, 225Choice-based conjoint, 74Classified, 164, 183, 187, 192Cluster analysis, 165, 186Coding, 134, 135, 137Collinear, collinearity, 62, 97, 98Combine data sources, 207, 215Comfort, 250Commonality, 287–289, 291, 304, 306,

308, 315Commonality decision variables, 296, 297Complete linkage hierarchical clustering, 180Complex systems, 79, 94, 104Computational implementation, 73, 158, 200,

231, 253, 343Conditionally independent, 171, 172, 195Condorcet’s voting paradox, 21Confirmation of effect of modeling

heterogeneity, 225Confirmatory analysis, 246Confounding, 136–138Conjoint analysis, 73Constants only model, 60Consumer preference heterogeneity, 5, 38, 212Correlation matrix, 172Correlations, 198Cost, 113–116, 118, 121, 123–125, 127Cost estimation uncertainty, 123Cost modeling, 83, 84, 87, 294, 304, 306, 307,

311, 342Covariates, 153Critical level relationship, 183Cronbach’s alpha, 179Cross validation, 60, 61, 100, 126, 278Customer choice utility, 38, 42, 43, 47Customer context, 259Customer desired product attributes, 6, 13, 30,

31, 35, 39, 40, 46, 47, 52, 53, 56, 57,59, 62, 63, 69, 70, 79–84, 86–90,93–97, 104, 255, 261, 266, 270,274–277, 287, 291–293, 319, 328,330, 343

Customer heterogeneity, 5, 38, 276, 278Customer profile attributes, 255

Performance-relatedPreference-related

Customer satisfaction survey, 265, 274Cut points, 49, 50

DD, A, G and V optimality, 139, 142, 145,

146, 150, 153, 155Data collection, 207Data enrichment, 53, 207Data mining, 164, 185, 200Deal package, 200Decision, 13–16Decision analysis, 6, 15, 30Decision-making processes, 3, 4, 6, 14, 31, 36Decision theory, 13–15Decision tree, 164, 165, 186, 192, 200Decision-based design (DBD), 3–8, 10,

339, 341Decomposition, 321, 322, 325Demand, 35–39, 41, 43–45, 47, 52, 54–62, 64,

71, 73Demand model, 30, 32Demand model estimation, 328, 331Demand model uncertainty, 343Design alternative, 133–137, 139, 141,

143, 151Design concept, 109, 110, 113–116, 118, 121,

125–128Design features, 113–116, 121Design for market systems, 5Design of experiments, 132, 135, 142,

144, 255Design options X, 13, 30, 35, 80–82, 84–88,

92–95, 104Design resolution, 136Design structure matrix, 27Design variable uncertainty, 123Design variables, 6, 113, 115, 121, 122Dictatorship, 19Differentiating attributes, 292, 301Diminishing marginal utility, 20Disaggregate approaches, 36Disconnected feasible performance

domains, 325Discrete choice analysis (DCA), 35–38, 71, 74DoE.base package, 138, 159D-optimality, 139, 142, 146, 153, 155Dummy variables, 56Dynamic demand modeling, 58Dynamic model, 343

352 Index

EEffect, 133–136, 138, 143, 144, 146Egress, 168Engineering analysis, 83, 84, 86, 87Engineering attributes, 6Engineering design, 3Engineering design attributes, 80Engineering performance, 255, 261

Performance of service results, 255Performance of service

delivery/transformations, 261Non-engineering attributes, 255, 262

Engineering product development, 320,322–325, 327, 328, 336

Engineering-centric, 4Enterprise-level utility optimization, 326Enterprise planning, 319Enterprise product planning, 319, 320, 323,

325, 328Enterprise risk attitudes, 124Enterprise-driven decision-based design, 3Enterprise-driven design, 320Enterprise-level product planning, 320, 323,

324, 336Environment context, 259Equally likely model, 60, 64Error in the system-level model, 212Exchangeable structureExchanges, 150Exit interviews, 52Exogenous variables, 13, 79, 80, 89Expectation, 14, 15Expected enterprise utility, 31Expected utility, 14, 16, 17, 81, 83, 85, 91,

102, 324, 326, 327, 333, 334Expected utility theorem, 16Experimental units, 139Exploratory analysis, 245Extended design points, 140Extreme value, 241

FF value, 175Fac.design, 159Factor, 133–141, 149, 157Factor analysis, 179, 245Factor combinations to

exclude, 149, 167Family of universal motors, 297Favorable properties of an alternative selection

method, 18Fisher, 132, 137, 175Fisher information matrix, 131, 145, 146, 158

Focus groups, 52Forces of demand, 339Forecasting future market, 59Form of the parameters, 116Fractional factorial experiment, 136–139, 154F-test, 175Full factorial experiment, 135, 137, 145,

149, 153Functional decomposition, 115Fusion, 207, 214, 230

GGam, 200Gelman-rubin r statistic, 222, 224, 231Generalized customer

desired attributes, 93Generating relation, 136, 137Geometric brownian motion path, 60Geometric constraint, 334Gibbs method, 249Gibbs sampling, 210Global sensitivity analysis, 126Goodness of fit, 60, 64, 331Goodness-of-fit measures, 225Gumbel distribution, 43

HHalo-effect, 247Hclust function, 200Heterogeneity of the consumers, 4, 5, 9, 37,

38, 40, 42, 47Heterogeneous consumer preference, 3, 5, 6,

14, 36–38, 42, 46–48Hierarchical approach, 322Hierarchical bayes estimation, 50, 51Hierarchical bayes methods, 249, 253Hierarchical bayesian, 177Hierarchical choice modeling, 205–207,

209, 216, 231Hierarchical decomposition, 322Horizontal leveraging strategy, 290House of quality, 28Human appraisal, 132–134, 137, 140, 142,

144, 145, 147, 152, 154, 158Human appraisal survey, 265, 266Human attribute ‘‘bins’’, 153Human attribute ranges, 153Hybrid electric vehicles, 256, 273–275

IIBHCM convergence, 222IBHCM estimation, 220

Index 353

I (cont.)IBHCM model fit and prediction tests, 224Identification of latent variable models, 242Include or exclude specific factor

combinations, 143Independence of irrelevant

alternatives (IIA), 19, 42Independent and identically

distributed, 43, 215Index of dissimilarity, 172Indicators, 171, 172, 174, 179, 246Indifferent, 17Information, 208, 211, 214, 216, 219, 230Information gain, 192, 194Ingress, 168, 172, 181, 194, 200Innovation, 340, 342Instrument variable techniques, 212Integrated bayesian heriarachical choice model

(IBHCM), 206, 209, 211, 219, 222,230, 253

Integrated latent variable discretechoice modeling, 239

Interaction between supply and demand, 82Interaction effects, 135, 138–140, 144Interior, 168Inverse gamma distribution, 178Iterated principle factor, 199

JJustification bias, 241, 247

KKaiser-guttman rule, 245Kano method, 99, 100

Basic attributes, 99, 100Excitive attributes, 99Must-be attributes, 99

Kmeans, 200K-means cluster analysis, 180

LLatent class, 171, 173–175, 179, 184, 187, 199Latent class analysis (LCA), 37, 55, 164, 165,

171, 172, 174, 186, 194Latent factor, 175, 179, 199Latent variable approach, 165Latent variable modeling, 237, 238Law of diminishing returns, 82Least squares method, 187Levels, 133–135, 137, 138, 141, 144, 151,

153, 157

Likelihood function, 239, 242–244Likelihood ratio chi-squared test, 171Limitations of the multi-attribute approach, 26Linear relationship, 183Linking models, 207Linking performance with usage contextLMA approach, 141Local design variables, 322Location parameter, 43Log likelihood estimates, 60Log likelihood ratio, 67Logit model, 38, 41, 46, 47, 49, 51, 55–58Lottery, 16, 18, 26Lottery assessment

MMachine learning, 164, 185, 196, 200Manifold absolute pressure (MAP)

sensor, 110, 116, 128Manufacturing cost, 84, 91, 291, 301,

304, 307, 310Manufacturing process attributes, 114Mapping of top-level customer desires, 95Marginal utility, 20Market contextMarket research, 83, 86Market segmentation, 55Market segmentation grid, 290–293, 296, 304Market segmentation grid (enhanced), 291Market segments, 43, 55, 63Market share, 250, 287, 289, 294, 295, 301,

303, 307, 309, 311, 312Market share prediction, 100Matlab statistical toolbox, 136Market-driven product family design, 289, 291Marketing-centric, 4Markov chain monte carlo methods, 51Maximum likelihood estimation, 43, 50,

171, 187Maximum likelihood technique, 241Measurement model, 239–241, 252Mechanism design, 341Mgcv package, 200Minimum deviation optimization, 322, 325Minitab, 136, 137Mitigate error propagated, 205–208, 230Mixed logit, 35, 36, 46MNL, 42, 43Model fit statistic q0

2, 155, 156Model fusion, 53Model selection, 60Model updating, 51, 59, 205, 206, 214, 216,

219, 230

354 Index

Model validation, 224Multi-attribute ranking, 24Multi-attribute utility function, 25Multidisciplinary design

optimization, 320, 321Multilevel design, 325Multi-level optimization, 323Multinomial analysis, 39Multinomial covariance matrices, 148Multinomial logit, 42, 89, 98, 330, 337Multiple-usage case, 258, 267, 273Multiplicative function form, 25Multivariate statistical analysis, 164

NNested block experiments, 151Nested logit, 43, 44, 73Nested logit demand model, 292Nests, 44, 46Net present value, 81, 85, 91Net present value of profit, 81, 85, 91Net revenue model, 30NL tree representations, 296Noise variables, 17Non-hierarchical decomposition, 321Non-parametric regression, 195Normalization, 20, 22, 23NP-hard optimization, 307

OOa.design package, 159Object, 257Observable independent variables, 40Open workshop on decision-based design, 5Opportunity cost, 19Optblock, 159Optfederov, 159Optimal, 132, 133, 138, 140, 142, 144, 145,

147, 149, 151, 152, 154, 157, 159Optimal ‘‘commonality’’, 287, 288, 295Optimal design of experiments, 133, 157Optimal experimental design for

choice experiments, 151Optimal product line positioning, 287, 288,

295, 311Optimization, 83, 85, 91Optimization loop, 81Ordered logit (OL), 8, 36, 48–50Ordered logit cutpoint, 50, 147Ordered logit model, 49, 50Ordinal, 48–50, 55Ordinal constraint, 196

Ordinary least squares (OLS), 146Organization design, 341Orthogonal, 135, 137, 138, 159Orthogonal array experiment, 137Outcomes, 14, 16, 18, 19, 25, 32Overdetermined models, 242

PPairwise comparisons, 21Paradox of multi-criteria, 20–22, 27Paradox of multiple decision makers, 21Parent to the child attributes, 197Pareto optimality, 19Partial sum of squares, 175Perceived customer attribute, 236Performance, 247, 250Person-situation segmentation, 258Physical surroundings, 264Physics-based model, 266, 270, 276Piezoresistive sensing, 121Platform index, 296Platform leveraging strategy, 291, 296,

310, 311Plausibility, 62Plug-in hybrid electric vehicle, 255, 274poLCA package, 200Pooled utility function, 215Population segment, 207Positive view of economists, 19, 30Posterior distribution, 210, 212–214, 216, 219,

222, 223, 249Power law relationship, 144, 183Power saw design, 62Power tools, 301, 303, 310Prediction variance, 146, 149, 150, 159Predictive modeling, 164, 165, 185Preference functions, 21Preference reversal, 23, 25Preferences over time, 343Preferred alternative, 133Preprocessing, 163–165, 194, 199Price, 112, 113, 124Principle component analysis, 240Prior distribution, 214, 216, 219, 242,

244, 250Probability of significance, 67Probit, 40Product attribute function deployment

(PAFD), 110–120, 122–128, 144Product choice set, 262Product design context, 259Product design selection procedure, 83Product design variables, 262

Index 355

P (cont.)Product family, 287–291, 294–296, 298–301,

303, 306–311, 315Product family design, 288, 301Product family design optimization, 295, 308Product line positioning, 288, 289, 294–297,

303, 307, 308, 311Product performance functions, 294Product platform, 288, 289, 299, 301, 311Profit-based optimization, 308, 310, 311Programmable vehicle model, 163, 166Proportional substitution, 42, 44Pseudo R-square, 60, 67, 70Psychological factors, 235, 237, 238, 253Psychometric tests, 18Psychometrics, 144Pugh matrix, 28, 29PVM experiments, 152

QQualitative, 205–207, 209, 210, 212, 213, 230Quality function deployment (QFD), 13, 26,

27, 32Quantifiable attributes, 94Quantifying the uncertainty, 343

RR2winbugs, 232Random block effect, 143, 146, 158Random block effects model, 146Random coefficients, 46, 47Random disturbance, 138, 236, 240, 241, 243Random effects ordered logit model

estimation, 154Random heterogeneity, 37, 38, 46, 48, 50, 165,

188, 200Random sampling, 54Random taste variation, 38, 46, 59Random utility, 40, 46Random-effects, 50Random-effects ordered logit, 165, 186, 188Random-effects ordered logit model, 50Randomized, 134, 139, 140Range, 134, 135, 152Rank-ordering, 142Rating, 132, 133, 140, 143, 144, 146–149, 153,

154, 157Rating category, 147, 148Rating style, 140, 143, 164, 175, 179, 182, 194Ratings bias, 177Redundancy, 198Relaxation of IIA, 48

Requirements traceability matrix, 27Research opportunities, 340, 342Respondent-level error, 211, 213Response, 132, 133, 140–144, 148, 153, 158Response surface, 143, 144, 153Restricted-unrestricted relationship, 60, 61Revealed preference, 35, 52, 94, 119, 256,

265, 281Revenue-based optimization, 308, 311Risk attitude, 14, 16, 17, 26, 31Risk attitude of the decision-maker, 81, 85Risk-averse, 16, 20Risk-neutral, 16Risk-prone, 16R-project, 220

SScale factor, 215Scale parameter, 43, 45, 46, 293, 294, 303Scale usage, 175, 177–180Scree plot analysis, 245Search algorithm, 320, 325Selection criterion, 6, 7, 81, 85, 89, 91Semi-baysian designs, 147Significance of rejection, 61Single criterion approach, 6, 30Single-objective approach, 7Single-usage case, 273Situational variable, 284Smoothing spline regression, 164, 182, 187Social influence, 341Social surroundings, 264Society of automotive engineers (SAE), 153Sociodemographic attributes, 6SPSS, 136‘‘splitting’’ attribute value gain, 192STATA, 56, 64, 67–69, 250STATA output, 67–69Stated preference, 52, 284Stated preference (or stated choice)

methods, 132Stated preference survey, 120Statistical efficiency, 133, 135, 138, 151Statistical goodness of fit, 60, 64, 67Stats package, 200Stratified random sampling, 54Structural model, 240Style variable, 180–182Subjective attributes, 166Substitution in use, 284Subsystem-level engineering problem, 329Sum of squares, 175, 181Supervised bayesian network, 195

356 Index

Suppression, 198Survey-specific error component, 215Suspension system design, 328, 332Sustainable design, 342System hierarchy, 93Systematic heterogeneity, 37, 165, 200, 328System-level analysis, 328

TTaguchi method, 137Target market population, 54Target setting, 109, 123, 128Targets, 322–329, 332, 336, 337Task definition, 264Taxonomy, 80, 257, 259, 285Taxonomy of customer attributes, 37, 38Technical difficulty, 111, 113, 115, 118Technical test measures, 115Technical importance, 111, 115, 118, 126Temporal perspective, 264Text mining, 52, 343Tolerance variables, 322Top-down system design, 329Total cost, 88, 101Total demand, 41Total market share, 309Total market size, 59Total product cost, 80, 84, 87, 89, 94Transformation, 23

Translation, 22, 23Transitivity, 15, 21TRIZ, 115Try-it-out survey, 265, 266, 282Two-stage least squares regression, 212, 213

UUncertainty, 16, 26Underdetermined models, 242Universal motor design, 86, 87Universal motor product family, 306, 312Unobserved errors, 49Unrestricted domain, 19Unsupervised bayesian network, 197Usage context, 255, 256, 259, 260, 267, 274Usage context attributes, 255, 259

performance-related, 255preference-related, 255

Usage context identification, 267, 274

Usage context scenario, 255, 260Usage context-based design, 255, 256,

259, 262Usage importance index, 261, 273Use situation, 256, 257, 259User fatigue (or fatigue of human

respondents), 144Utility, 20, 26Utility function, 7, 8, 14, 16, 17, 26, 28, 32Utility of the certainty equivalent, 17Utopia target, 325, 326, 333, 337

VValidation, 60, 126, 342Value, 16Variance at the observation level, 146Variance at the respondent level, 146Variance–covariance, 220Vehicle dependability index (VDI), 248, 331Vehicle occupant package design

optimization, 228Vehicle occupant packaging design, 208Vehicle quality survey (VQS), 237Vertical leveraging strategy, 291Violation of arrow’s IIA property, 22Voice of the customer, 27Voting, 24

WWalk-through example, 62, 64Weight/scaling constant, 15Weights, 23, 24Weka 3 code, 200What if’’ scenarios, 100What-if-scenario analysis, 281Whole plot, 139, 140, 145, 149, 153Whole-plot factors, 139, 140, 145, 149,

153, 158Winbugs, 220, 232, 249, 253Within-block covariance matrices, 148Within-segment competition, 303Working correlation matrix, 131, 148

ZZero-model, 64, 67, 70

Index 357