Determining of Customer’s Kansei Needs and Product Design Attributes by Rough Set Theory
- 48 Downloads
The trend of new product development taking into account a customer’s feeling and needs has become very important for companies’ development and competition in the market. Kansei engineering is a consumer-oriented technology that seeks to capture the voice of the customer to produce a successful product. This method helps to transform customers feeling into the design parameters. In order to improve customer satisfaction, it is very important to determine the design parameters that make up the product. This paper presents a design support system intended for use in designing new product. A product has a lots of design attributes. For this reason, the product design attributes are reduced with Rough sets theory and main design attributes are obtained for developing a new product. Rough sets theory deals with uncertain or conflicting data. After determining product design attributes, the different products are produced for evaluating customer’s feel with Kansei adjectives. Customer evaluations were conducted using the semantic differential method to examine the relationship between users’ assessments of product and design elements. Kansei results are analyzed by applied Principle Component Analysis to determine the relationships between products and emotions that affect the general preferences of customers. Baby cradle design is taken as a case study; but this method can be used to develop other products. As a result, this paper presents a design support system intended for use in designing new product, so the designed product can fit more closely to the consumers’ desires.
KeywordsKansei engineering Rough sets theory Principle component analysis Product design
This work was supported by the Scientific and Technological Research Council of Turkey “TÜBİTAK-TEYDEB” programme with the project number-5170065.
- 7.Pawlak Z (1991) Rough sets, theoretical aspects of reasoning about data. Kluwer, DordrechtGoogle Scholar
- 8.Komorowski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Skowron A (ed) Rough fuzzy hybridization. Springer, Berlin, pp 3–98Google Scholar
- 10.Pawlak Z (1996) Why rough sets. In: Proceedings of the Fifth International Conference on Fuzzy Systems, vol 2, New Orleans, LA, pp 738–743Google Scholar
- 12.Mundt JT, Glenn NF, Weber KT, Prather TS, Lass LW, Pettingill J (2005) Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques. Remote Sens Environ 96:509–517. https://doi.org/10.1016/j.rse.2005.04.004 CrossRefGoogle Scholar
- 13.Tian Y, Guo P, Lyu MR (2005) Comparative studies on feature extraction methods for multispectral remote sensing image classification. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics 2:1275–1279. https://doi.org/10.1109/ICSMC.2005.1571322
- 15.Lokman AM (2010) Design and emotion: the Kansei engineering methodology. Malays J Comput 1(1):1–11Google Scholar
- 16.Osgood CE, Suci CJ (1957) The measurement of meaning. University of Illinois Press, Urbana, ILGoogle Scholar