Adaptive Questionnaires for Direct Identification of Optimal Product Design
This addresses the challenge of accelerating optimal product design by integrating marketing and manufacturing knowledge, though it appears incremental as it builds on existing adaptive questionnaire methods.
The paper tackled the problem of identifying the most profitable product design from candidates under unknown consumer preferences, showing that accurate preference estimation is unnecessary and introducing a novel adaptive questionnaire that integrates engineering feasibility and costs to directly determine the optimal design, with demonstrated superiority in a documented task.
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.