Convergence of measurement systems analysis and artificial intelligence in the supply chain
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.
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Author's Biography
Jerry Hamilton is a Lockheed Martin Procurement Engineer and a Certified American Society for Quality (ASQ) Six Sigma Master Black Belt. Jerry works with a team of engineers to employ the use of statistical tools/methods to production processes within the supply chain. He has presented at 11 industry conferences on the topics of Lean, Six Sigma and Design for Six Sigma and has worked in the semiconductor, transportation and aerospace industries. Jerry holds a MSc degree in industrial engineering from the University of Houston and a BSc degree in mechanical engineering from New Mexico State University.
Christopher L. Colaw Chris Colaw is a Lockheed Martin Fellow with expertise in digital transformation, inspection technology, autonomation and 3D modelling. In this role, he is responsible for a Quality 4.0 and digital transformation portfolio valued at over US$200m in cost savings for Lockheed Martin. Prior to this role, Chris developed and led the global F-35 aircraft programme variation management strategy, which resulted in greater than 40 per cent increase in capable key characteristics (CpK >1.33) and demonstrated overall F-35 programme process capability achievement with the US Government Accountability Office (GAO) and F-35 customers. Chris holds MSc degrees in management and in mechanical engineering and a BSc degree in mechanical engineering, all from Southern Methodist University (SMU). Chris is a Certified Manager of Quality and Organizational Excellence from the American Society of Quality and serves as the Chairman of the SMU Mechanical Engineering Industrial Advisory Board, and also as Chairman of the University of Texas at El Paso Industrial, Manufacturing, and Systems Engineering (IMSE) Advisory Board.
Citation
Hamilton, Jerry and Colaw, Christopher L. (2022, June 1). Convergence of measurement systems analysis and artificial intelligence in the supply chain. In the Journal of Supply Chain Management, Logistics and Procurement, Volume 4, Issue 4. https://doi.org/10.69554/BUDN9483.
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