A stepwise based fuzzy regression procedure for developing customer preference models in new product development
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 character...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/45723 |
| _version_ | 1848757364353662976 |
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| author | Chan, Kit Yan Lam, H.K. Dillon, T. Ling, S. |
| author_facet | Chan, Kit Yan Lam, H.K. Dillon, T. Ling, S. |
| author_sort | Chan, Kit Yan |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:26:55Z |
| format | Journal Article |
| id | curtin-20.500.11937-45723 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:26:55Z |
| publishDate | 2015 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-457232017-09-13T14:23:35Z A stepwise based fuzzy regression procedure for developing customer preference models in new product development Chan, Kit Yan Lam, H.K. Dillon, T. Ling, S. stepwise regression new product development engineering characteristics customer satisfaction fuzzy regression fuzzy least square regression consumer preferences 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. 2015 Journal Article http://hdl.handle.net/20.500.11937/45723 10.1109/TFUZZ.2014.2375911 IEEE fulltext |
| spellingShingle | stepwise regression new product development engineering characteristics customer satisfaction fuzzy regression fuzzy least square regression consumer preferences Chan, Kit Yan Lam, H.K. Dillon, T. Ling, S. A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title | A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title_full | A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title_fullStr | A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title_full_unstemmed | A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title_short | A stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| title_sort | stepwise based fuzzy regression procedure for developing customer preference models in new product development |
| topic | stepwise regression new product development engineering characteristics customer satisfaction fuzzy regression fuzzy least square regression consumer preferences |
| url | http://hdl.handle.net/20.500.11937/45723 |