Opportunities for Improved Statistical Process Control

A Bayesian dynamic programming model builds on existing models to account for inspection delay, choice of keeping production going during inspection and/or restoration, and lot sizing. How dynamic statistical process control rules can improve on traditional, static ones is described. Numerical examp...

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Main Authors: ANGELUS, Alexandar, Evan, Porteus
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Language:English
Published: Institutional Knowledge at Singapore Management University 1997
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Online Access:https://ink.library.smu.edu.sg/lkcsb_research/1052
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spelling sg-smu-ink.lkcsb_research-20512010-09-23T06:24:04Z Opportunities for Improved Statistical Process Control ANGELUS, Alexandar Evan, Porteus A Bayesian dynamic programming model builds on existing models to account for inspection delay, choice of keeping production going during inspection and/or restoration, and lot sizing. How dynamic statistical process control rules can improve on traditional, static ones is described. Numerical examples are explored and 9 opportunities for improvement are identified. Opportunities for improvement include: 1. Cancel some of the inspections called for by an optimal static rule when starting in control. 2. Inspect more frequently than called for by an optimal static rule once inspections begin, and inspect even more frequently than that when negative evidence is accumulated. 3. Utilize evidence from previous inspections to justify either restoration or another inspection. 4. Cancel inspections and hesitate to restore the process at the end of a production run. 5. Consider using scheduled restoration, in which restoration is carried out regardless of the results of any inspections. 1997-09-01T07:00:00Z text https://ink.library.smu.edu.sg/lkcsb_research/1052 info:doi/10.1287/mnsc.43.9.1214 Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Business Administration, Management, and Operations
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Business Administration, Management, and Operations
spellingShingle Business Administration, Management, and Operations
ANGELUS, Alexandar
Evan, Porteus
Opportunities for Improved Statistical Process Control
description A Bayesian dynamic programming model builds on existing models to account for inspection delay, choice of keeping production going during inspection and/or restoration, and lot sizing. How dynamic statistical process control rules can improve on traditional, static ones is described. Numerical examples are explored and 9 opportunities for improvement are identified. Opportunities for improvement include: 1. Cancel some of the inspections called for by an optimal static rule when starting in control. 2. Inspect more frequently than called for by an optimal static rule once inspections begin, and inspect even more frequently than that when negative evidence is accumulated. 3. Utilize evidence from previous inspections to justify either restoration or another inspection. 4. Cancel inspections and hesitate to restore the process at the end of a production run. 5. Consider using scheduled restoration, in which restoration is carried out regardless of the results of any inspections.
format text
author ANGELUS, Alexandar
Evan, Porteus
author_facet ANGELUS, Alexandar
Evan, Porteus
author_sort ANGELUS, Alexandar
title Opportunities for Improved Statistical Process Control
title_short Opportunities for Improved Statistical Process Control
title_full Opportunities for Improved Statistical Process Control
title_fullStr Opportunities for Improved Statistical Process Control
title_full_unstemmed Opportunities for Improved Statistical Process Control
title_sort opportunities for improved statistical process control
publisher Institutional Knowledge at Singapore Management University
publishDate 1997
url https://ink.library.smu.edu.sg/lkcsb_research/1052
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