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ABSTRACT
Most of the literature on statistical process monitoring (SPM) concerns a single process or a few of them. Yet, in a modern manufacturing plant there may be thousands of process measurements worth monitoring. When scaling up, supporting software should provide three important capabilities. First, it should sort by appropriate summary measures to allow the engineer to easily identify processes of most concern. Second, it should show process health of all processes in one graph. Third, it should meet the challenges of big data for test multiplicity, robustness, and computational speed.
This article describes the summaries and graphs that can be especially useful when monitoring thousands of process measures that are to be analyzed retrospectively. This article's goal is to propose these for routine reports on processes and invite discussion as to whether these features belong in the mainstream.
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John Sall
John Sall earned a bachelor's degree in history from Beloit College and a master's degree in economics from Northern Illinois University (NIU). Both NIU and NC State awarded him honorary doctorates. In 1976 he was an establishing member of SAS Institute and currently heads the JMP business division, which created interactive and highly visual statistical discovery software for scientists and engineers. In 1997 he helped found the Cary Academy.
He is a Fellow of the American Statistical Association, the American Association for the Advancement of Science, serves on the World Wildlife Fund board, the Smithsonian Institution's National Museum of Natural History advisory board, and is a former board member of The Nature Conservancy. He is also a former trustee of North Carolina State University.