Title: Adaptive Questionnaires for Direct Identification of Optimal Product Design,
Abstract: We consider the problem of identifying the most profitable product design
from a finite set of candidates under unknown consumer preference. A standard
approach to this problem follows a two-step strategy: First, estimate the
preference of the consumer population, represented as a point in part-worth
space, using an adaptive discrete-choice questionnaire. Second, integrate the
estimated part-worth vector with engineering feasibility and cost models to
determine the optimal design. In this work, we (1) demonstrate that accurate
preference estimation is neither necessary nor sufficient for identifying the
optimal design, (2) introduce a novel adaptive questionnaire that leverages
knowledge about engineering feasibility and manufacturing costs to directly
determine the optimal design, and (3) interpret product design in terms of a
nonlinear segmentation of part-worth space, and use this interpretation to
illuminate the intrinsic difficulty of optimal design in the presence of noisy
questionnaire responses. We establish the superiority of the proposed approach
using a well-documented optimal product design task. This study demonstrates
how the identification of optimal product design can be accelerated by
integrating marketing and manufacturing knowledge into the adaptive
questionnaire.