Product Design Prediction Using Integrated Dynamic Kansei Engineering Scheme

Abstract

Substantially, the consumer preference as well as the product form varies with time. Past studies did not consider this important time-variant nature of product design. Therefore, this paper proposes an integrated dynamic Kansei prediction model (IDKP) to solve this product design problem. A practical example of smart phone design is presented. In the first stage, the semantic differential scheme is used to measure subjective preference of consumer about classified products month by month. In the second stage, the GM (1,N) model is adopted to define the relative weight of design attributes on each emotional attribute. By virtue of considering high weighting design attributes, the modeling work can be performed more efficiently and accurately. In the final stage, the dynamic autoregressive with exogeneous inputs (ARX) scheme in conjunction with the Kansei engineering are used to establish the prediction model of consumer preference. Through applying the IDKP scheme, designers may correctly catch the future trend of the product development.