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

Jerry Hamilton and Christopher L. Colaw
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Jerry Hamilton: Lockheed Martin, USA
Christopher L. Colaw: Lockheed Martin, USA

Journal of Supply Chain Management, Logistics and Procurement, 2022, vol. 4, issue 4, 314-330

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.

Keywords: measurement; system; analysis; gage; variation; artificial intelligence; Industry 4.0; Quality 4.0; hidden factory (search for similar items in EconPapers)
JEL-codes: L23 M11 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:aza:jscm00:y:2022:v:4:i:4:p:314-330

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