INCORPORATING CUSTOMER PREFERENCES AND MARKET TRENDS IN VEHICLE PACKAGE DESIGN

Demand models play a critical role in enterprise-driven design by expressing revenues and costs as functions of product attributes. However, existing demand modeling approaches in the design literature do not sufficiently address the unique issues that arise when complex systems are being considered. Current approaches typically consider customer preferences for only quantitative product characteristics and do not offer a methodology to incorporate customer preference-data from multiple component/subsystem-specific surveys to make product-level design trade-offs. In this paper, we propose a hierarchical choice modeling approach that addresses the special needs of complex engineering systems. The approach incorporates the use of qualitative attributes and provides a framework for pooling data from multiple sources. Heterogeneity in the market and in customer-preferences is explicitly considered in the choice model to accurately reflect choice behavior. Ordered logistic regression is introduced to model survey-ratings and is shown to be free of the deficiencies associated with competing techniques, and a Nested Logit-based approach is proposed to estimate a system-level demand model by pooling data from multiple component/subsystem-specific surveys. The design of the automotive vehicle occupant package is used to demonstrate the proposed approach and the impact of both packaging design decisions and customer demographics upon vehicle choice are investigated. The focus of this paper is on demonstrating the demand (choice) modeling aspects of the approach rather than on the vehicle package design.Copyright © 2007 by ASME

[1]  Shapour Azarm,et al.  A Customer-Based Expected Utility Metric for Product Design Selection , 2002, DAC 2002.

[2]  Deepak Kumar,et al.  A Market-Driven Approach to the Design of Platform-Based Product Families , 2006 .

[3]  Matthew B. Parkinson,et al.  Optimizing Vehicle Occupant Packaging , 2006 .

[4]  Wei Chen,et al.  An Approach to Decision-Based Design With Discrete Choice Analysis for Demand Modeling , 2003 .

[5]  Tao Jiang,et al.  Target Cascading in Optimal System Design , 2003, DAC 2000.

[6]  Nanxin Wang,et al.  Design and Verification of a New Computer Controlled Seating Buck , 2006, DAC 2006.

[7]  Jaroslaw,et al.  AIAA 2002-5409 Bi-Level Integrated System Synthesis (BLISS) for Concurrent and Distributed Processing , 2022 .

[8]  Panos Y. Papalambros,et al.  Combined maximization of interior comfort and frontal crashworthiness in preliminary vehicle design , 2004 .

[9]  Carol A. C. Flannagan,et al.  A Statistical Method for Predicting Automobile Driving Posture , 2002, Hum. Factors.

[10]  H. Li,et al.  Product Design Selection Under Uncertainty and With Competitive Advantage , 2000 .

[11]  Catherine L. Kling,et al.  Linking Revealed and Stated Preferences to Test External Validity , 1999 .

[12]  Jeremy J. Michalek,et al.  Linking Marketing and Engineering Product Design Decisions via Analytical Target Cascading , 2005 .

[13]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[14]  A. Tversky.  Elimination by aspects: A theory of choice. , 1972 .

[15]  J. Sobieszczanski-Sobieski,et al.  Bilevel Integrated System Synthesis for Concurrent and Distributed Processing , 2002 .

[16]  L. Thurstone.  A law of comparative judgment. , 1994 .

[17]  Robert D. Braun,et al.  Collaborative optimization: an architecture for large-scale distributed design , 1996 .

[18]  R. Duncan Luce,et al.  Individual Choice Behavior: A Theoretical Analysis , 1979 .

[19]  Jordan J. Louviere,et al.  Combining sources of preference data , 1998 .

[20]  Jacob Marschak,et al.  Binary-Choice Constraints and Random Utility Indicators (1960) , 1974 .

[21]  Nanxin Wang,et al.  A Parametric Approach to Vehicle Seating Buck Design , 2004, DAC 2004.

[22]  Jie Cheng,et al.  An Integrated Latent Variable Choice Modeling Approach for Enhancing Product Demand Modeling , 2004 .

[23]  Eun Suk Suh,et al.  Flexible product platforms: framework and case study , 2007 .

[24]  Carol A. C. Flannagan,et al.  Effects of Vehicle Interior Geometry and Anthropometric Variables on Automobile Driving Posture , 2000, Hum. Factors.

[25]  R. McKelvey,et al.  A statistical model for the analysis of ordinal level dependent variables , 1975 .

[26]  George A. Hazelrigg,et al.  The Implications of Arrow’s Impossibility Theorem on Approaches to Optimal Engineering Design , 1996 .

[27]  Deepak Kumar,et al.  Multilevel optimization for enterprise driven decision-based product design , 2006 .

[28]  Jeremy J. Michalek,et al.  Manufacturing Investment and Allocation in Product Line Design Decision-Making , 2005, DAC 2005.

[29]  Joffre Swait,et al.  Stated Choice Methods: Relaxing the IID assumption – introducing variants of the MNL model , 2000 .

[30]  D. Hedeker,et al.  A random-effects ordinal regression model for multilevel analysis. , 1994, Biometrics.

[31]  Greg M. Allenby,et al.  Adjusting Choice Models to Better Predict Market Behavior , 2005 .

[32]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[33]  J. Herriges,et al.  Combining Revealed and Stated Preferences: Consistency Tests and Their Interpretations , 2003 .