Incorporating Customer Preferences and Market Trends in Vehicle Package Design

Abstract

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.

Set-based design by simulation of usage scenario coverage

“…The design optimization framework to obtain the best "average" performances for their produ ser and usage context profiles, and the user preferen ile this approach may be reasonable for certain prod awed if one wants to obtain a precise model of the adequ ng a variety of usage situations and expectations. An ormance is generally independent of existing competing on model (see Allen 2001 for ng of n ultimate objectives in nce Messac et al 2003) Kumar et al (2007Kumar et al ( , 2009, for insta considering user heterogeneity, to be on tedious market investigations, ga which describe both performances a methods are not well adapted to rad services provided by the new produc In summary, designing an appropri between three spaces (see Figure 3) -the space of product offer so 1 In the sense of the Expected Utility Va 3 esented in the Pareto frontier and the distance of a gi ind of competitiveness in the solution. However, nei ed) correctly models the effect of the price attribute a mpetitiveness model.…”

Making Sense of Elegant Complexity in Design

“…As noted by Augustine [53], new issues in complexity are emerging in the flatter world we find ourselves in because of the increase of social influences, decentralization, and cultural forces now impacting the development of products and systems. For example, it has become important to be able to capture user preferences and then link them to a system optimization model [54][55][56][57]. Since this is a very difficult task given the potential variability in population preferences, many times the number of variables in a preference model is kept small.…”

Methods for Evaluating Suitability of Econometric Demand Models in Design for Market Systems

“…Kumar [3] and Shiau [19] constructed their own demand models for the purpose of design optimization. However, little emphasis has been placed on the choice of the functional form of utility used in these models.…”

“…These individuals were set aside for sampling purposes as well as individuals who purchased model year 2007 vehicles in 2006. 3 The authors arbitrarily set a minimum of five observations for each vehicle alternative (if at least five were available) to increase the sample of demographics for consumers of low market-share vehicles. 4 The authors utilized the weighting procedure available in Train's code.…”

“…In some cases either too few vehicle choices were available in the survey to match the sales or too few respondents would be sampled to represent the demographics of purchasers of a vehicle. 3 To account for this, a set of weights was generated for each individual in the sample to adjust the log-likelihood calculation to correctly match 2006 market shares. 4 The assumption made with this approach is that the sampled individuals who purchased a particular vehicle alternative are representative of all individuals who purchased that vehicle.…”