Our research is motivated by the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring under the decision-based design framework. Even though demand modeling techniques exist in market research, little work exists on demand modeling that addresses the specific needs of engineering design, in particular, that facilitates engineering decision making. In this work, we enhance the use of discrete choice analysis to demand modeling in the context of decision-based design. The consideration of a hierarchy of product attributes is introduced to map customer desires to engineering design attributes related to engineering analyses. To improve the predictive capability of demand models, the Kano method is employed to provide econometric justification when selecting the shape of the customer utility function. A (passenger) vehicle engine case study, developed in collaboration with the market research firm, J. D. Power & Associates, and the Ford Motor Company, is used to demonstrate the proposed approaches.

1.
Hazelrigg
,
G. A.
, 1998, “
A Framework for Decision Based Engineering Design
,”
J. Mech. Des.
1050-0472,
120
, pp.
653
658
.
2.
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.
490
497
.
3.
Gu
,
X.
,
Renaud
,
J. E.
,
Ashe
,
L. M.
,
Batill
,
S. M.
,
Budhiraja
,
A. S.
, and
Krajewski
,
L. J.
, 2002, “
Decision-Based Collaborative Optimization
,”
ASME J. Mech. Des.
1050-0472,
124
(
1
), pp.
1
13
.
4.
Tappeta
,
R. V.
, and
Renaud
,
J. E.
, 2001, “
Interactive Multiobjective Optimization Design Strategy for Decision Based Design
,”
ASME J. Mech. Des.
1050-0472,
123
(
2
), pp.
205
215
.
5.
Wan
,
J.
, and
Krishnamurty
,
S.
, 2001, “
Learning-Based Preference Modeling in Engineering Design Decision-Making
,”
ASME J. Mech. Des.
1050-0472,
123
(
2
), pp.
191
198
.
6.
Thurston
,
D. L.
, 2001, “
Real and Misconceived Limitations to Decision Based Design With Utility Analysis
,”
ASME J. Mech. Des.
1050-0472,
123
(
2
), pp.
176
186
.
7.
Thurstone
,
L.
, 1927, “
A Law of Comparative Judgment
,”
Psychol. Rev.
0033-295X,
34
, pp.
273
286
.
8.
Luce
,
R.
, 1959,
Individual Choice Behavior: A Theoretical Analysis
,
Wiley
, New York.
9.
Marschak
,
J.
, 1960, “
Binary Choice Constraints on Random Utility Indicators
,”
Stanford Symposium on Mathematical Methods in the Social Sciences
,
K.
Arrow
, ed.,
Stanford University Press
, Stanford, California.
10.
Tversky
,
A.
, 1972, “
Elimination by Aspects: A Theory of Choice
,”
Psychol. Rev.
0033-295X,
79
, pp.
281
299
.
11.
Green
,
P. E.
, and
Srinivasan
,
V.
, 1990, “
Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice
,”
J. Marketing
0022-2429,
54
, pp.
3
19
.
12.
Green
,
P. E.
, and
Srinivasan
,
V.
, 1978, “
Conjoint Analysis in Consumer Research: Issues and Outlook
,”
J. Consum. Res.
0093-5301,
5
, pp.
103
123
.
13.
Green
,
P. E.
, and
Wind
,
Y.
, 1975, “
New Ways to Measure Consumer Judgments
,”
Harvard Bus. Rev.
0017-8012,
53
, pp.
107
117
.
14.
Louviere
,
J. J.
, 2000, “
Why Stated Preference Discrete Choice Modelling is NOT Conjoint Analysis (and what SPDCM IS)
,” Memetrics white paper.
15.
Ben-Akiva
,
M.
, and
Lerman
,
S. R.
, 1985,
Discrete Choice Analysis
,
MIT Press
, Cambridge, MA.
16.
Cook
,
H. E.
, 1997,
Product Management: Value, Quality, Cost, Price, Profit, and Organization
,
Chapman & Hall
, London.
17.
Donndelinger
,
J.
, and
Cook
,
H. E.
, 1997, “
Methods for Analyzing the Value of Automobiles
,” SAE Paper No. 970762, Warrendale, PA,
Society of Automotive Engineers, Inc.
18.
Li
,
H.
, and
Azarm
,
S.
, 2000, “
Product Design Selection Under Uncertainty and With Competitive Advantage
,”
ASME Design Technical Conference
, ASME Paper No. DETC2000∕DAC-14234, Baltimore, MD.
19.
Besharati
,
B.
,
Azarm
,
S.
, and
Farhang-Mehr
,
A.
, 2002, “
A Customer-Based Expected Utility for Product Design Selection
,”
Proc. of ASME Design Engineering Technical Conference
,
Montreal
, Canada,
ASME
, New York, DETC2002/DAC-34081.
20.
Wassenaar
,
H. J.
, “
An Approach to Decision-Based Design
,” Ph.D. Dissertation, University of Illinois at Chicago, October 2003.
21.
Hensher
,
D. A.
, and
Johnson
,
L. W.
, 1981,
Applied Discrete Choice Modeling
,
Halsted Press
, New York.
22.
Daganzo
,
C.
, 1979,
Multinomial Probit, the theory and Its Application to Demand Forecasting
,
Academic Press
, New York.
23.
Shiba
,
S.
,
Graham
,
A.
, and
Walden
,
D.
, 1993,
New American TQM: Four Practical Revolutions in Management
,
Productivity Press
, Cambridge, Mass.
24.
Saari
,
D. G.
, 2000, “
Mathematical Structure of Voting Paradoxes, I; Pairwise Vote, II; Positional Voting
,”
J. Econ. Theory
0022-0531,
15
, pp.
1
103
.
25.
Otto
,
K. N.
, and
Wood
,
K.
, 2001,
Product Design: Techniques in Reverse Engineering and New Product Development
,
Prentice Hall
, Upper Saddle River, NJ.
26.
Krueger
,
R. A.
, 1994,
Focus Groups: A Practical Guide for Applied Research
,
2nd Edition
,
Sage Publications
, Thousand Oaks, California.
27.
Louviere
,
J. J.
,
Hensher
,
D. A.
, and
Swait
,
J. D.
, 2000,
Stated Choice Methods, Analysis and Application
,
Cambridge University Press
;
Hair
,
J. F.
, (editor),
Anderson
,
R. E.
,
Tatham
,
R. L.
, and
Black
,
W. C.
, 1998,
Multivariate Data Analysis
,
5th Edition
,
Prentice Hall College Div.
28.
Hastie
,
T.
,
Tibshirani
,
R.
, and
Friedman
,
J.
, 2001,
The Elements of Statistical Learning
,
Springer
, New York.
29.
Raftery
,
A.
, 1995, “
Bayesian Model Selection in Social Research
,”
Sociol. Methodol.
0081-1750
25
, pp.
111
163
.
30.
Loehlin
,
J. C.
, 1998,
Latent Variable Models, An Introduction to Factor, Path, and Structural Analysis
,
3rd Edition
,
L. Erlbaum Associates
, Mahwah, NJ.
31.
Breiman
,
L.
,
Spector
,
P.
, 1992, “
Submodel Selection and Evaluation in Regression: The X-Random Case
,”
Int. Statist. Rev.
0306-7734,
60
, pp.
291
319
.
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