A fuzzy ordinary regression method for modeling customer preference in tea maker design
|dc.contributor.author||Chan, Kit Yan|
|dc.identifier.citation||Chan, K.Y. and Kwong, C. and Law, M.C. 2014. A fuzzy ordinary regression method for modeling customer preference in tea maker design. Neurocomputing. 142: pp. 147-154.|
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.
|dc.subject||New product development|
|dc.title||A fuzzy ordinary regression method for modeling customer preference in tea maker design|
NOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, Vol. 142 (2014). DOI: 10.1016/j.neucom.2013.12.056
|curtin.department||Department of Electrical and Computer Engineering|