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Using machine learning to support quality management: Framework and experimental investigation

Loukas Tsironis (Technical University of Crete, Chania, Greece)
Nikos Bilalis (Technical University of Crete, Chania, Greece)
Vassilis Moustakis (Technical University of Crete, Chania, Greece)

The TQM Magazine

ISSN: 0954-478X

Article publication date: 1 June 2005




To demonstrate the applicability of machine‐learning tools in quality management.


Two popular machine‐learning approaches, decision tree induction and association rules mining, were applied on a set of 960 production case records. The accuracy of results was investigated using randomized experimentation and comprehensibility of rules was assessed by experts in the field.


Both machine‐learning approaches exhibited very good accuracy of results (average error was about 9 percent); however, association rules mining outperformed decision tree induction in comprehensibility and correctness of learned rules.

Research limitations/implications

The proposed methodology is limited with respect to case representation. Production cases are described via attribute‐value sets and the relation between attribute values cannot be determined by the selected machine‐learning methods.

Practical implications

Results demonstrate that machine‐learning techniques may be effectively used to enhance quality management procedures and modeling of cause‐effect relationships, associated with faulty products.


The article proposes a general methodology on how to use machine‐learning techniques to support quality management. The application of the technique in ISDN modem manufacturing demonstrates the effectiveness of the proposed general methodology.



Tsironis, L., Bilalis, N. and Moustakis, V. (2005), "Using machine learning to support quality management: Framework and experimental investigation", The TQM Magazine, Vol. 17 No. 3, pp. 237-248.



Emerald Group Publishing Limited

Copyright © 2005, Emerald Group Publishing Limited

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