Optimal Product Design by Sequential Experiments in High Dimensions

Abstract

The identification of the optimal design of products and packaging is challenged when attributes and their levels interact. Firms recognize this by testing prototypes prior to launch, where the effects of interactions are revealed in the head-to-head comparison of a small number of finalists. A difficulty in conducting analysis for design is dealing with the high dimensionality of the design space. We propose an experimental criteria for sequentially searching for the most preferred design concept, and incorporate a stochastic search variable selection method to selectively estimate relevant interactions among the attributes. A validation experiment confirms that our proposed method leads to improved design concepts in a high-dimensional space compared to alternative methods.