Application of Kansei Engineering to Design an Industrial Enclosure
Tipo de documentoTexto en actas de congreso
Fecha de publicación2014
Condiciones de accesoAcceso abierto
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 app lied 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 studi ed; (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 DOE factorial design) and (3) the synthesis phase, where both spaces are linked (that is, how each factor affects each response is discovered). We claimed that KE is a good example 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. This presentation is a practical application of the ideas exposed there to the design of electrical enclosures. The paper shows how well known statistical methods (DOE, principal component analysis and regression analysis) are used together in conjunction with other non statistical techniques and in the presence of practical real world restrictions to discover how different technical characteristics of the enclosures affect the selected “emotions”
CitaciónMarco-Almagro, L.; Tort-martorell, J. Application of Kansei Engineering to Design an Industrial Enclosure. A: Annual Conference of the European Network for Business and Industrial Statistics. "Papers of the 14th Annual ENBIS Conference". Linz: 2014.