A fuzzy ordinary regression method for modeling customer preference in tea maker design

NEUROCOMPUTING

卷:142

Article

Chan, Kit Yan¹  Kwong, C. K.²  Law, M. C.³ 

[1] Curtin Univ, Dept Elect & Comp Engn, Bentley, WA, Australia

[2] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Kowloon, Hong Kong, Peoples R China

[3] GEW Corp Ltd, Hong Kong, Hong Kong, Peoples R China

关键词: New product development;    Fuzzy regression;    Tea makers;    Customer preference;    Fuzzy modeling;   

DOI  :  10.1016/j.neucom.2013.12.056

来源: Elsevier

【 摘 要 】

Faced with fierce competition in marketplaces, manufacturers need to determine the appropriate settings of engineering characteristics of the new products so that the best customer preferences of the products can be obtained. To achieve this, functional models relating customer preferences to engineering characteristics need to be developed. As information regarding functional relationships between customer preferences are generally subjective or heuristic in nature, development of the customer preference models involve two uncertainties, namely fuzziness and randomness. Existing approaches use only fuzzy-based technologies to address the uncertainty caused by fuzziness. They are not designed to address the randomness of the observed data which is caused by a limited knowledge of the variability of influences between customer preferences and engineering characteristics. In this article, a fuzzy ordinary regression method is proposed to develop the customer preference models which are capable of addressing the two uncertainties of crispness and fuzziness of the customer preferences. A case study of a tea maker design which involves both uncertainties is used to demonstrate the effectiveness of the proposed method. (C) 2014 Elsevier B.V. All rights reserved.

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