4.5 Article

Product platform design for a product family based on Kansei engineering

Journal

JOURNAL OF ENGINEERING DESIGN
Volume 20, Issue 6, Pages 589-607

Publisher

TAYLOR & FRANCIS LTD
DOI: 10.1080/09544820802132410

Keywords

Kansei engineering; product platform design; regression analysis; cluster analysis; preference similarity

Ask authors/readers for more resources

Many studies on product platform design have been performed, and have focused on factors such as product function, performance level, and production cost. In platform design, customers' affective needs should be taken into account. This paper presents a new product platform design method based on Kansei engineering. First, platform and individual parameters are identified, and the quantified relationship between the product's perceptual image and the design parameters is established by using regression analysis from an affective evaluation survey. Second, customers are grouped according to their preference similarity coefficients by a cluster analysis of the preference evaluation survey in which the values of the platform parameters are fixed. Based on the clusters, the number of platforms is determined. Third, the quantified relationship between the average preference and the individual parameters is established for each cluster by the regression method. Finally, the values of the individual parameters are determined based on the satisfaction of each customer group. The product platform developed by the proposed method can achieve customer satisfaction, and a company can combine the simple individual form elements to the platform to rapidly develop a customised product form to meet a certain customer's affective need. A mobile phone body design case is used as an example to illustrate the proposed method.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available