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Abstract:

Purpose – To demonstrate the applicability of machine-learning tools in quality management.

Design/methodology/approach – 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.

Findings – 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.

Originality/value – 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.

Keywords:

Decision trees, Manufacturing systems, Modems, Quality management

Article Type:

Research paper

Article URL:

http://www.emeraldinsight.com/10.1108/09544780510594207

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