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Augmenting statistical quality control with machine learning techniques: an overview

Author

Listed:
  • Aikaterini Fountoulaki
  • Nikos Karacapilidis
  • Manolis Manatakis

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.

Suggested Citation

  • Aikaterini Fountoulaki & Nikos Karacapilidis & Manolis Manatakis, 2011. "Augmenting statistical quality control with machine learning techniques: an overview," International Journal of Business and Systems Research, Inderscience Enterprises Ltd, vol. 5(6), pages 610-626.
    • Handle: RePEc:ids:ijbsre:v:5:y:2011:i:6:p:610-626
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