We present an integrated design and marketing approach to facilitate the generation of an optimal robust set of product design alternatives to carry forward to the prototyping stage. The approach considers variability in both (i) engineering design domain, and (ii) customer preferences in marketing domain. In the design domain, the approach evaluates performance and robustness of a design alternative due to variations in its uncontrollable parameters. In the marketing domain, in addition to considering competitive product offerings, the approach considers designs that are robust in customer preferences with respect to: (1) the variations in the design domain, and (2) the inherent variations in the estimates of preferences given the fit of the preference model to the sampled data. Our overall goal is to obtain design alternatives that are multi-objectively robust and optimal, i.e., (1) are optimal for nominal values of parameters, and (2) are within a known acceptable range in their multi-objective performance, and (3) maintain feasibility even when they are subject to applications and environments that are different from nominal or standard laboratory conditions. We illustrate the highlights of our approach with the design of a corded power tool example.
1.
Griffin
, A.
, and Hauser
, J. R.
, 1992, “Patterns of Communications among Marketing, Engineering, and Manufacturing—A Comparison between Two New Product Teams
,” Manage. Sci.
0025-1909, 38
(3
), pp. 360
–373
.2.
McAllister
, C. D.
, and Simpson
, T. W.
, 2003, “Multidisciplinary Robust Design Optimization of an Internal Combustion Engine
,” ASME J. Mech. Des.
1050-0472, 125
(1
), pp. 124
–130
.3.
Fuhita
, K.
, and Ishii
, K.
, 1997, “Task Structuring Toward Computational Approaches to Product Variety Design
,” Proceedings of ASME DETC ’97
, 23rd Design Automation Conference
, DETC97/DAC3766, Sep. 14th—17th, Sacramento, CA.4.
Li
, H.
, and Azarm
, S.
, 2000, “Product Design Selection under Uncertainty and with Competitive Advantage
,” ASME J. Mech. Des.
1050-0472, 122
(4
), pp. 411
–418
.5.
Urban
, G. L.
, and Hauser
, J. R.
, 1980, Design and Marketing of New Products
, Prentice-Hall
, Englewood Cliffs
, NJ.6.
Michalek
, J. J.
, Feinberg
, F. M.
, and Papalambros
, P. Y.
, 2005, “Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading
,” Journal of Product Innovation Management
, 22
(1
), pp. 42
–62
.7.
Wassenaar
, H. J.
, and Chen
, W.
, 2003, “An Approach to Decision Based Design with Discrete Choice Analysis for Demand Modeling
,” ASME J. Mech. Des.
1050-0472, 125
(3
), pp. 90
–497
.8.
Wassenaar
, H. J.
, Chen
, W.
, Cheng
, J.
, and Sudjianto
, A.
, 2005, “Enhancing Discrete Choice Modeling for Decision-Based Design
,” ASME J. Mech. Des.
1050-0472, 127
(4
), pp. 514
–523
.9.
Luo
, L.
, Kannan
, P. K.
, Besharati
, B.
, and Azarm
, S.
, 2005, “Design of Robust New Products under Variability: Marketing Meets Design
,” Journal of Product Innovation Management
, 22
(2
), pp. 177
–192
.10.
McFadden
, D.
, 1986, “The Choice Theory Approach to Market Research
,” Aviat. Week Space Technol.
0005-2175, 5
(4
), pp. 275
–297
.11.
Taguchi
, G.
, Elsayed
, E.
, and Hsiang
, T.
, 1989, Quality Engineering in Production Systems
, McGraw Hill
, New York
.12.
Parkinson
, A.
, Sorensen
, C.
, and Pourhassan
, N.
, 1993, “A General Approach for robust Optimal Design
,” ASME J. Mech. Des.
1050-0472, 115
(1
), pp. 74
–80
.13.
Sundaresan
, S.
, Ishii
, K.
, and Houser
, D. R.
, 1992, “Design Optimization for Robustness Using Performance Simulation Programs
,” Eng. Optimiz.
0305-215X, 20
(1
), pp. 63
–78
.14.
Chen
, W.
, and Yuan
, C.
, 1999, “A Probabilistic-based Design Model for Achieving Flexibility in Design
,” ASME J. Mech. Des.
1050-0472, 121
(1
), pp. 77
–83
.15.
Rao
, S. S.
, and Cao
, L.
, 2002, “Optimum Design of Mechanical Systems Involving Interval Parameters
,” ASME J. Mech. Des.
1050-0472, 124
(3
), pp. 465
–472
.16.
Du
, S.
, Sudjianto
, A.
, and Chen
, W.
, 2004, “An Integrated Framework for Optimization Under Uncertainty Using Inverse Reliability Strategy
,” ASME J. Mech. Des.
1050-0472, 126
(4
), pp. 562
–570
.17.
Gunawan
, S.
, and Azarm
, S.
, 2005, “Multi-Objective Robust Optimization Using a Sensitivity Region Concept
,” Struct. Multidiscip. Optim.
1615-147X, 29
(1
), pp. 50
–60
.18.
Li
, M.
, Azarm
, S.
, and Boyars
, A.
, 2005, “A New Deterministic Approach Using Sensitivity Region Measures for Multi-Objective Robust and Feasibility Robust Design Optimization
,” CD-ROM Proceedings of the ASME IDETC/CIE, Paper no. IDETC05–85095, Sep. 24–28, Long Beach, CA.19.
Hsee
, C.
, and Leclerc
, F.
, 1998, “Will Products Look More Attractive When Presented Seperately or Together?
,” J. Consum. Res.
0093-5301, 25
(2
), pp. 175
–186
.20.
Camerer
, C.
, 1995, Handbook of Experimental Economics
, Princeton University Press
, Princeton
, NJ.21.
Sudman
, S.
, Bradburn
, N.
, and Schwarz
, N.
, 1996, Thinking About Answers: The Application of Cognitive Processes To Survey Methodology
, Jossey-Bass
, San Francisco
.22.
Swait
, J.
, Adamowicz
, W.
, Hanemann
, M.
, Diederich
, A.
, Krosnick
, J.
, Layton
, D.
, Provencher
, W.
, Schkade
, D.
and Tourangeau
, R.
, 2002, “Context Dependence and Aggregation in Disaggregate Choice Analysis
,” Marketing Letters
, 13
(3
), pp. 195
–205
.23.
Louviere
, J.
, Street
, D.
, Carson
, R.
, Ainslie
, A.
, Deshazo
, J. R.
, Cameron
, T.
, Hensher
, D.
, Kohn
, R.
and Marley
, T.
, 2002, “Dissecting the Random Component of Utility
,” Marketing Letters
, 13
(3
), pp. 177
–193
.24.
Vriens
, M.
, Wedel
, M.
, and Wilms
, T.
, 1996, “Metric Conjoint Segmentation Methods: A Monte Carlo Comparison
,” J. Mark. Res.
0022-2437, 33
, pp. 73
–85
.25.
Besharati
, B.
, Azarm
, S.
, and Kannan
, P. K.
, 2005, “A Decision Support System for Product Design Selection: A Generalized Purchase Modeling Approach
,” Decision Support Systems, in press.26.
Chen
, W.
, Allen
, J. K.
, Tsui
, K. L.
, and Mistree
, F.
, 1996, “A Procedure for Robust Design: Minimizing Variations Caused by Noise Factors and Control Factors
,” ASME J. Mech. Des.
1050-0472, 118
(4
), pp. 478
–485
.27.
Kalsi
, M.
, Hacker
, K.
, and Lewis
, K.
, 2001, “A Comprehensive Robust Design Approach for Decision Trade-Offs in Complex Systems Design
,” ASME J. Mech. Des.
1050-0472, 123
(1
), pp. 1
–10
.28.
Green
, P. E.
, and Srinivasan
, V.
, 1990, “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice
,” J. Marketing
0022-2429, 54
(1
), pp. 3
–1
.29.
Kamakura
, W. A.
, and Russell
, G. J.
, 1989, “A Probabilistic Choice Model for Market Segmentation and Elasticity Structure
,” J. Mark. Res.
0022-2437, 26
, pp. 379
–390
.30.
Akaike
, H.
, 1973, “Information Theory and an Extension of the Maximum Likelihood Principle
,” Proceedings of the second International Symposium of Information Theory
, pp. 267
–281
.31.
Carrol
, J. D.
, and Green
, P. E.
, 1995, “Psychometric Methods in Marketing: Part I, Conjoint Analysis
,” J. Mark. Res.
0022-2437, 32
(1
), pp. 385
–391
.32.
Ben-Akiva
, M.
, and Lerman
, S.
, 1985, Discrete Choice Analysis: Theory and Application to Travel Demand
, MIT
, Cambridge
.33.
Sawtooth Choice-Based Conjoint User Manual, 2001, Sawtooth Software Inc., Sequim, WA, Appendix C, C1–C3.
34.
Greene
, W. H.
, 2000, Econometric Analysis
, Prentice Hall
, Upper Saddle River
, NJ.35.
Narayanan
, S.
, and Azarm
, S.
, 1999, “On Improving Multiobjective Genetic Algorithms for Design Optimization
,” Struct. Optim.
0934-4373, 18
, pp. 146
–155
.36.
Kurapati
, A.
, Azarm
, S.
, and Wu
, J.
, 2002, “Constraint Handling Improvements for Multi-Objective Genetic Algorithms
,” Struct. Multidiscip. Optim.
1615-147X, 23
, pp. 204
–213
.37.
Besharati
, B.
, Azarm
, S.
, Luo
, L.
, and Kannan
, P. K.
, 2004, “An Integrated Robust Design and Marketing Approach for Design Selection Process
,” Proceedings of ASME DETC ’04
, Design Automation Conference
, DETC04/DAC57405, Sep. 28th—October 2nd, Salt Lake City, UT.Copyright © 2006
by American Society of Mechanical Engineers
You do not currently have access to this content.
