Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36179
Title: A fuzzy ordinary regression method for modeling customer preference in tea maker design
Authors: Chan, KY
Kwong, CK 
Law, MC
Keywords: New product development
Fuzzy regression
Tea makers
Customer preference
Fuzzy modeling
Issue Date: 2014
Publisher: Elsevier
Source: Neurocomputing, 2014, v. 142, p. 147-154 How to cite?
Journal: Neurocomputing 
Abstract: 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.
URI: http://hdl.handle.net/10397/36179
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2013.12.056
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