An intelligent production workflow mining system for continual quality enhancement

G. T.S. Ho, H. C.W. Lau, Ka Man Lee, A. W.H. Ip, K. F. Pun

Research output: Journal article publicationJournal articleAcademic researchpeer-review

23 Citations (Scopus)


In today's globally competitive industries, high-quality and high-reliability products play an important role in achieving customer satisfaction, and insisting on quality is always the only way to survive in an enterprise. Studies indicate that automating quality audits and adding decision support in quality improvement is an attractive idea. In this environment, production workflow mining is an approach for extracting knowledge from different manufacturing processes in order to assist real-time quality prediction and improvement. This papers attempts to propose an intelligent production workflow mining system (IPWMS) embracing online analytical processing (OLAP) and data mining technology, together with the use of artificial intelligence combining artificial neural networks (ANNs) and fuzzy rule sets to realize knowledge discovery and decision support in high-quality manufacturing. To validate the feasibility of the proposed system, a prototype is developed and evaluated in a company, and a description of this case example is covered in this paper.
Original languageEnglish
Pages (from-to)792-809
Number of pages18
JournalInternational Journal of Advanced Manufacturing Technology
Issue number7-8
Publication statusPublished - 1 Apr 2006


  • Continual quality enhancement
  • Fuzzy logic
  • Neural continual quality enhancement
  • Neural networks
  • Online analytical processing
  • Production workflow

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering


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