The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion.
The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which are not only fashionable, but are also saleable in the market.
To evaluate the proposed approach, a case study is conducted in a Hong Kong-based fashion company in which a real-set of data are tested to generate fuzzy association rules. The results reveal that the FARM approach can provide knowledge support to the fashion industry during NPD, shorten the NPD cycle time, and increase customer satisfaction.
Compared with traditional association rule mining, the proposed FARM approach takes the fuzziness of data into consideration and the knowledge represented in the fuzzy rules is in a more human-understandable structure. It captures the voice of the customer into fashion product development and provides a specific solution to deal with the challenges brought by fast fashion. In addition, it helps increase the innovation and technological capability of the fashion industry.
The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting this project (Project Code: RPXV).
Lee, C.K.H., Tse, Y.K., Ho, G.T.S. and Choy, K.L. (2015), "Fuzzy association rule mining for fashion product development", Industrial Management & Data Systems, Vol. 115 No. 2, pp. 383-399. https://doi.org/10.1108/IMDS-09-2014-0277
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