Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design

Abstract

Choice models play a critical role in enterprise-driven design by providing a link between engineering design attributes and customer preferences. However, existing approaches do not sufficiently capture heterogeneous consumer preferences nor address the needs of complex design artifacts, which typically consist of many subsystems and components. An integrated Bayesian hierarchical choice modeling (IBHCM) approach is developed in this work, which provides an integrated solution procedure and a highly flexible choice modeling approach for complex system design. The hierarchical choice modeling framework utilizes multiple model levels corresponding to the complex system hierarchy to create a link between qualitative attributes considered by consumers when selecting a product and quantitative attributes used for engineering design. To capture heterogeneous and stochastic consumer preferences, the mixed logit choice model is used to predict consumer system-level choices, and the random-effects ordered logit model is used to model consumer evaluations of system and subsystem level design features. In the proposed approach, both systematic and random consumer heterogeneity are explicitly considered, the ability to combine multiple sources of data for model estimation and updating is provided using the Bayesian estimation methodology, and an integrated estimation procedure is introduced to mitigate error propagated throughout the model hierarchy. The new modeling approach is validated using several metrics and validation techniques for behavior models. The benefits of the IBHCM method are demonstrated in the design of an automobile occupant package.