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Economic design of attribute np control charts using a variable sampling policy

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  • Imen Kooli
  • Mohamed Limam

Abstract

To use a control chart, the quality engineer should specify three decision variables, namely the sample size, the sampling interval and the critical region of the chart. A significant part of recent research relaxed the constraint of using fixed design parameters to open the way to a new type of control charts called adaptive ones where at least one of the decision variables may change in real time based on the last data information. These adaptive schemes have proven their effectiveness from economical and statistical point of views. In this paper, the economic design of an attribute np control chart using a variable sampling interval (VSI) is treated. A sensitivity analysis is conducted to search for optimal design parameters minimizing the expected total cost per hour and to reveal the impact of the process and cost parameters on the behavior of optimal solutions. An economic comparison between the classical np chart, variable sample size (VSS) np control chart and VSI chart is conducted. It is found that switching from the classical attribute chart to the VSI sampling strategy results in notable cost savings and in reduction of the average time to signal and the average number of false alarms. In most cases of the sensitivity analysis, the VSI np chart outperforms the VSS np chart based on economical and statistical considerations. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Imen Kooli & Mohamed Limam, 2015. "Economic design of attribute np control charts using a variable sampling policy," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 31(4), pages 483-494, July.
  • Handle: RePEc:wly:apsmbi:v:31:y:2015:i:4:p:483-494
    DOI: 10.1002/asmb.2042
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    Cited by:

    1. Xiao, Xiao & Jiang, Wei & Luo, Jianwen, 2019. "Combining process and product information for quality improvement," International Journal of Production Economics, Elsevier, vol. 207(C), pages 130-143.
    2. Barry R. Cobb, 2021. "Statistical Process Control for the Number of Defectives with Limited Memory," Decision Analysis, INFORMS, vol. 18(3), pages 203-217, September.

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