Augmenting statistical quality control with machine learning techniques: an overview

Aikaterini Fountoulaki, Nikos Karacapilidis and Manolis Manatakis

International Journal of Business and Systems Research, 2011, vol. 5, issue 6, 610-626

Abstract: This paper attempts to provide practical insights to issues related to the enrichment of statistical quality control (SQC) systems with machine learning (ML). It reports on ML techniques that have already augmented the major SQC methods, comments on their advantages and disadvantages and identifies areas of improvement that could delineate future work directions. Three major SQC methods are considered: acceptance sampling, statistical process control and experimental design. The work reported in this paper reveals that ML techniques can significantly augment SQC systems.

Keywords: statistical quality control; machine learning; acceptance sampling; statistical process control; experimental design; industrial quality systems; system improvements; business; systems research. (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbsre:v:5:y:2011:i:6:p:610-626

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