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dc.contributor.authorChan, Kit Yan
dc.contributor.authorLam, H.K.
dc.contributor.authorDillon, T.
dc.contributor.authorLing, S.
dc.identifier.citationChan, K.Y. and Lam, H.K. and Dillon, T. and Ling, S. 2015. A stepwise based fuzzy regression procedure for developing customer preference models in new product development. IEEE Transactions on Fuzzy Systems. 23 (5): pp. 1728-1745.

Fuzzy regression methods have commonly been used to develop consumer preferences models which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modelling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial which includes only significant regressors.Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least square regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly-used for new product development. Also, smaller-scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.

dc.subjectstepwise regression
dc.subjectnew product development
dc.subjectengineering characteristics
dc.subjectcustomer satisfaction
dc.subjectfuzzy regression
dc.subjectfuzzy least square regression
dc.subjectconsumer preferences
dc.titleA stepwise based fuzzy regression procedure for developing customer preference models in new product development
dc.typeJournal Article
dcterms.source.titleIEEE Transactions on Fuzzy Systems

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curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusOpen access

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