Enhancing Discrete Choice Demand Modeling for Decision-Based Design

[+] Author and Article Information
Henk Jan Wassenaar

Power Information Network,  J.D. Power & Associates, Troy, Michigan 48098wassenaer@yahoo.comIntegrated Design Automation Laboratory, Department of Mechanical Engineering,  Northwestern University, Evanston, Illinois 60208-3111wassenaer@yahoo.com

Wei Chen1

Power Information Network,  J.D. Power & Associates, Troy, Michigan 48098weichen@northwestern.eduIntegrated Design Automation Laboratory, Department of Mechanical Engineering,  Northwestern University, Evanston, Illinois 60208-3111weichen@northwestern.edu

Jie Cheng

Power Information Network,  J.D. Power & Associates, Troy, Michigan 48098

Agus Sudjianto

 Bank of America, Charlotte, North Carolina 28255

We use the notion “expected utility” in this paper as it stands for the selection criterion in the presence of uncertainty, while “value” is often interpreted as a selection criterion without uncertainty.

Profit is a result of accounting practices such as depreciation, which need not be related to engineering design. Therefore, with profit is meant net revenue, i.e., the difference between revenue and expenditure. The net revenue can be discounted to present value.


Corresponding author address: 2145 Sheridan Road, Tech B224, Evanston, IL 60208-3111.

J. Mech. Des 127(4), 514-523 (Aug 29, 2004) (10 pages) doi:10.1115/1.1897408 History: Received December 31, 2003; Revised August 29, 2004

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.

Copyright © 2005 by American Society of Mechanical Engineers
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Figure 1

Decision-based design flow chart (2)

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Figure 2

Example of Kano diagram

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Figure 3

Mapping top-level customer desires to design options

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Figure 4

Vehicle engine DBD description



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