A fuzzy ordinary regression method for modeling customer preference in tea maker design
| Autor: | Chun Kit Kwong, Kit Yan Chan, M. C. Law |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Voice of the customer
Operations research Computer science Management science Heuristic business.industry Cognitive Neuroscience Customer preference Fuzzy logic Regression Computer Science Applications Competition (economics) Artificial Intelligence New product development Customer intelligence business |
| Zdroj: | Neurocomputing. 142:147-154 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2013.12.056 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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