Integrated bayesian hierarchical choice modeling to capture heterogeneous consumer preferences in engineering design

Christopher Hoyle, Wei Chen*, Nanxin Wang, Frank S. Koppelman

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


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.

Original languageEnglish (US)
Article number121010
JournalJournal of Mechanical Design, Transactions Of the ASME
Issue number12
StatePublished - Dec 24 2010


  • Bayesian estimation
  • complex system design
  • discrete choice analysis
  • error mitigation
  • heterogeneous consumer preference
  • hierarchical modeling
  • ordered logit

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

Fingerprint Dive into the research topics of 'Integrated bayesian hierarchical choice modeling to capture heterogeneous consumer preferences in engineering design'. Together they form a unique fingerprint.

Cite this