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Convergence of measurement systems analysis and artificial intelligence in the supply chain

Author

Listed:
  • Hamilton, Jerry

    (Lockheed Martin, USA)

  • Colaw, Christopher L.

    (Lockheed Martin, USA)

Abstract

Just as products and services have inherent variation in them, measurement systems have variation in them as well. The key is to characterise how much variation they have, and to baseline this prior to the start of large-scale production runs. There exist industry standards by which to compare, and the smaller the amount of measurement variation possible is better. Excessive measurement variation in the supply chain can result in unfavourable business impacts including ‘hidden factory’ effects. This paper will address relevant considerations for how to characterise measurement variation in the supply chain through a Gage repeatability and reproducibility (R&R) process, and the application of Industry 4.0, Quality 4.0, data sciences, Big Data and artificial intelligence (AI) and their implications within the realm of measurement systems analysis.

Suggested Citation

  • Hamilton, Jerry & Colaw, Christopher L., 2022. "Convergence of measurement systems analysis and artificial intelligence in the supply chain," Journal of Supply Chain Management, Logistics and Procurement, Henry Stewart Publications, vol. 4(4), pages 314-330, June.
    • Handle: RePEc:aza:jscm00:y:2022:v:4:i:4:p:314-330
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    More about this item

    Keywords

    measurement; system; analysis; gage; variation; artificial intelligence; Industry 4.0; Quality 4.0; hidden factory;
    All these keywords.

    JEL classification:

    • L23 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Organization of Production
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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