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Opportunities for Improved Statistical Process Control

Author

Listed:
  • Evan L. Porteus

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • Alexandar Angelus

    (Graduate School of Business, Stanford University, Stanford, California 94305)

Abstract

Our 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. We focus on describing how dynamic statistical process control (DSPC) rules can improve on traditional, static ones. We explore numerical examples and identify nine opportunities for improvement. Some of these ideas are well known and strongly supported in the literature. Other ideas may be less well understood. Our list includes the following: Cancel some of the inspections called for by an (economically) optimal static rule when starting in control (such as at the beginning of a production run and following a restoration). 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. Utilize evidence from previous inspections to justify either restoration or another inspection. Cancel inspections and hesitate to restore the process at the end of a production run. Consider using scheduled restoration, in which restoration is carried out regardless of the results of any inspections. Implementation, limitations, and extensions are addressed.

Suggested Citation

  • Evan L. Porteus & Alexandar Angelus, 1997. "Opportunities for Improved Statistical Process Control," Management Science, INFORMS, vol. 43(9), pages 1214-1228, September.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:9:p:1214-1228
    DOI: 10.1287/mnsc.43.9.1214
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    Citations

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    Cited by:

    1. Viliam Makis, 2008. "Multivariate Bayesian Control Chart," Operations Research, INFORMS, vol. 56(2), pages 487-496, April.
    2. Linderman, Kevin & McKone-Sweet, Kathleen E. & Anderson, John C., 2005. "An integrated systems approach to process control and maintenance," European Journal of Operational Research, Elsevier, vol. 164(2), pages 324-340, July.
    3. Tagaras, George, 2017. "New indices for the evaluation of the statistical properties of Bayesian x¯ control charts for short runsAuthor-Name: Nikolaidis, Yiannis," European Journal of Operational Research, Elsevier, vol. 259(1), pages 280-292.
    4. Makis, Viliam, 2009. "Multivariate Bayesian process control for a finite production run," European Journal of Operational Research, Elsevier, vol. 194(3), pages 795-806, May.
    5. Kulkarni, Shailesh S., 2008. "Loss-based quality costs and inventory planning: General models and insights," European Journal of Operational Research, Elsevier, vol. 188(2), pages 428-449, July.
    6. Erica L. Plambeck & Terry A. Taylor, 2019. "Testing by Competitors in Enforcement of Product Standards," Management Science, INFORMS, vol. 65(4), pages 1735-1751, April.
    7. Abraham Grosfeld-Nir & Yigal Gerchak & Qi-Ming He, 2000. "Manufacturing to Order with Random Yield and Costly Inspection," Operations Research, INFORMS, vol. 48(5), pages 761-767, October.
    8. Diwakar Gupta & William L. Cooper, 2005. "Stochastic Comparisons in Production Yield Management," Operations Research, INFORMS, vol. 53(2), pages 377-384, April.
    9. George Tagaras & Yiannis Nikolaidis, 2002. "Comparing the Effectiveness of Various Bayesian X̄ Control Charts," Operations Research, INFORMS, vol. 50(5), pages 878-888, October.
    10. Luo Hua & Wu Zhang, 2002. "Optimal np Control Charts with Variable Sample Sizes or Variable Sampling Intervals," Stochastics and Quality Control, De Gruyter, vol. 17(1), pages 39-61, January.
    11. Shoshana Anily & Abraham Grosfeld-Nir, 2006. "An Optimal Lot-Sizing and Offline Inspection Policy in the Case of Nonrigid Demand," Operations Research, INFORMS, vol. 54(2), pages 311-323, April.
    12. Nenes, George & Tagaras, George, 2007. "The economically designed two-sided Bayesian control chart," European Journal of Operational Research, Elsevier, vol. 183(1), pages 263-277, November.
    13. Abraham Grosfeld‐Nir & Eyal Cohen & Yigal Gerchak, 2007. "Production to order and off‐line inspection when the production process is partially observable," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(8), pages 845-858, December.
    14. Jue Wang & Chi-Guhn Lee, 2015. "Multistate Bayesian Control Chart Over a Finite Horizon," Operations Research, INFORMS, vol. 63(4), pages 949-964, August.

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