An integrated latent variable choice modeling approach for enhancing product demand modeling

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.