Development of an intelligent quality management system using fuzzy association rules

Henry C.W. Lau, George T.S. Ho, K.F. Chu, William Ho, Carman K.M. Lee

Research output: Contribution to journalArticlepeer-review


In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ‘‘distributed process mining” feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.
Original languageEnglish
Pages (from-to)1801-1815
Number of pages15
JournalExpert Systems with Applications
Issue number2 Part 1
Publication statusPublished - Mar 2009


  • production quality
  • quality improvement
  • quality information system


Dive into the research topics of 'Development of an intelligent quality management system using fuzzy association rules'. Together they form a unique fingerprint.

Cite this