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.
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