Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16791
Title: Fuzzy measurement in quality management systems
Authors: Ho, GTS
Lau, HCW
Chung, NSH
Ip, WH 
Issue Date: 2010
Source: Studies in fuzziness and soft computing, 2010, v. 252, p. 515-536 How to cite?
Journal: Studies in Fuzziness and Soft Computing 
Abstract: The fluctuating and competitive economy today is affecting the production industry worldwide. Under the stress of various forceful challenges, customer satisfaction and product loyalties form the necessary key for enterprises to survive or even thrive in this decade. The concept of Quality Management System (QMS) offers a chance for enterprises to win customer satisfaction by producing consistently high-quality products. With the use of Data Mining (DM) and Artificial Intelligence (AI) techniques, enterprises are able to discover previously hidden yet useful knowledge from large and related databases which assists to support the high-valued continuous quality improvement. Continuous quality improvement is of utmost importance to enterprises as it helps turn them potent to compete in today's rivalrous global business market. In this chapter, Intelligent Quality Management System with the use of Fuzzy Association Rules is the main focus. Fuzzy Association Rule is a useful data mining technique which has received tremendous attention. Through integrating the fuzzy set concept, enterprises or users are able to decode the discovered rules and turn them into more meaningful and easily understandable knowledge, for instance, they can extract interesting and meaningful customer behavior pattern from a pile of retail data. In order to better illustrate how this technique is used to deal with the quantitative process data and relate process parameters with the quality of finished products, an example is provided as well to help explain the concept.
URI: http://hdl.handle.net/10397/16791
ISBN: 9783642120510
ISSN: 1434-9922
DOI: 10.1007/978-3-642-12052-7_21
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