In this paper, we present the importance of using a single criterion approach to DecisionBased Design (DBD)

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.

Our research is motivated by the need for developing a rigorous Decision-Based Design framework and the need for developing an approach to demand modeling that is critical for assessing the profit a product can bring. Even though demand modeling techniques exist in market research, little work exists on product demand modeling that addresses the specific needs of engineering design in particular that facilitates engineering decision-making. Building upon our earlier work on using the discrete choice analysis approach to demand modeling, in this work, we provide detailed guidelines for implementing the discrete choice demand modeling approach in product design. The modeling of a hierarchy of product attributes is introduced to cascade customer desires to specific key customer attributes that can be represented using engineering language. To improve the predictive capability of demand models, we propose to use the Kano method for providing the econometric justification when selecting the shape of the customer utility function. A real (passenger) vehicle engine case study, developed in collaboration with the market research firm J.D. Power & Associates and Ford Motor Company, demonstrates the proposed approaches. The example focuses on demand analysis and does not reach beyond the key customer attribute level. The obtained demand model is shown to be satisfactory through cross validation.

In today's highly competitive economy it is increasingly important to consider customer desires in engineering design on a systems level, that is, there is a need for integrating business decision-making and engineering decision-making. Building upon the earlier work on using the discrete choice analysis approach to demand modeling, in this work, the discrete choice analysis method is enhanced by introducing latent variables to include the customer's attitude and perception in a demand model. The latent variable approach better captures psychological factors that affect the purchase behavior of customers and facilitates the understanding of the relationship between customers' desires and product features. The approach is expected to enhance the predictive accuracy of demand models and help verify a designer's intent by assessing the contributions of various product attributes to the customer's perceived product performance. In this work, the existing binary latent variable discrete choice model is extended for multinomial choice and the mathematical formulation is developed to integrate a latent variable model and a multinomial logit choice model. The demand modeling of passenger vehicles with emphasis on studying the impact of engine design attributes is used to demonstrate the potential of the proposed approaches. It is important to keep in mind that the example's emphasis is on demonstrating the approaches rather than the results per se.

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