Statistical Methods in Kansei Engineering: a Case of Statistical Engineering

Kansei Engineering (KE) is a technique used to incorporate emotions in the product design process. Its basic purpose is discovering in which way some properties of a product convey certain emotions in its users. It is a quantitative method, and data is typically collected using questionnaires. Japanese researcher Mitsuo Nagamachi is the founder of Kansei Engineering. Products where KE has been successfully applied include cars, phones, packaging, house appliances, clothes or websites, among others. Kansei Engineering studies typically follow a model with three main steps: (1) spanning the semantic space: defining the responses, those emotions that will be studied; (2) spanning the space of properties: deciding on the technical properties of the products that can be freely changed and that might affect the responses (factors in a factorial design) and (3) the synthesis phase, where both spaces are linked (that is, how each factor affects each response is discovered). The procedure resembles that of an experimental design in an industrial context. However, practitioners of KE are hardly ever statisticians. Many well-known statistical methods are commonly used in KE, such as principal component analysis and regression analysis, but the techniques are sometimes misused. Furthermore, the discipline could benefit from a more extensive use of statistical methods (some of them of higher complexity, but easily implemented with existing statistical software). Statistics is thus essential in Kansei Engineering. But if statisticians do not enter into this arena, others will do, as there is a real need and interest in the topic. Kansei Engineering is a good area of application of what Roger W. Hoerl and Ron Snee call statistical engineering: focusing not in advancement of statistics – developing new techniques, fine tuning existing ones – but on how current techniques can be best used in a new area. The aim of this paper is presenting the fundamentals of Kansei Engineering while giving a practical example of statistical engineering in a promising field.