Understanding and modelling heterogeneity of human preferences for engineering design

In today's competitive market, it is essential for companies to provide products which not only achieve high performance, but also appeal to the tastes of consumers. Therefore, a key element of design is an understanding of consumer preferences for product features. In this work, the random-effects ordered logit model is proposed as the modelling framework to capture the impact of both product and human attributes on consumers' ratings of qualitative system and sub-system attributes. To support the modelling, a series of methodologies are developed to both understand and model the influence of consumer heterogeneity upon product preferences. To illustrate the methodologies, a human appraisal experiment for understanding preferences for automobile occupant package design is analysed. An issue with analysing human appraisal experiments is that the effect of respondent heterogeneity must be understood to separate the influence of design factors from that of human factors. Hierarchical Bayes estimation and cluster analysis are used to gain an understanding of respondent rating styles, which are subsequently modelled explicitly in the ordered logit model. Smoothing spline regression is used to determine the functional form of the ordered logit model. The proposed ordered logit model is validated using a vehicle design case study.