DEVELOPMENT OF AN INTEGRATED MODEL TO IMPROVE KANSEI ENGINEERING PERFORMANCE BY MEANS OF FACTOR ANALYSIS, GROUP TECHNOLOGY AND ANALYTIC NETWORK PROCESS

In the modern age, products have to be designed in such a way to meet customer satisfaction. To do so, designers employ different methods. Among these methods is Kansei engineering which determines product characteristics based on customer feelings. It does its job by taking customer into real atmosphere of product. In this paper, we develop an integrated model to improve Kansei engineering performance by means of factor analysis, group technology and analytic network process. Our study consists of three steps. First, we extract emotional words through the literature and interview and localize them for use in study of sofa in Mashhad. Next, we choose three sofa designs based on expert views and recommendations and incorporate them in a questionnaire to be evaluated through the words extracted in the previous step. By using factor analysis, we place the emotional words under nine factors. Then we determine technical specifications of sofas by studying the existing information and receiving designers’ views and recommendations. The specifications are classified into eight groups using cluster identification algorithm. Finally, we employed analytic network process to determine the relationship between emotional factors and technical specifications. This way we select the best technical specifications of each sofa design and provide the final design through combination of the selected technical specifications. The results helped us to choose appropriate technical specifications according to emotional factors. Therefore, it is essential to pay much attention to emotional words expressed by user, as they help designers to match their designs with users’ emotional needs.

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