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A comparison study of effectiveness and robustness of control charts for monitoring process mean

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  • Ou, Yanjing
  • Wu, Zhang
  • Tsung, Fugee

Abstract

This article compares the effectiveness and robustness of nine typical control charts for monitoring the mean of a variable, including the most effective optimal and adaptive Sequential Probability Ratio Test (SPRT) charts. The nine charts are categorized into three types (the X¯ type, CUSUM type and SPRT type) and three versions (the basic version, optimal version and fully adaptive (FA) version). While the charting parameters of the basic charts are determined by common wisdoms, the parameters of the optimal and fully adaptive charts are designed optimally in order to minimize an index, Average Extra Quadratic Loss (AEQL), for the best overall performance. A Performance Comparison Index, PCI, is also proposed as the measure of the relative overall performance of the charts. This comparison study does not only compare the detection effectiveness of the charts, but also investigate their robustness in performance. Moreover, the probability distribution of the mean shift δ is studied explicitly as an influential factor in a factorial experiment. Apart from many other findings, the results of this study reveal that the SPRT chart is more effective than the CUSUM chart and X¯ chart by 58% and 126%, respectively, from an overall viewpoint. Moreover, it is found that the optimization design of charting parameters can increase the detection effectiveness by 29% on average, and the adaptive features can further enhance the detection power by 35%. Finally, a set of design tables are provided to facilitate the users to select a chart for their applications.

Suggested Citation

  • Ou, Yanjing & Wu, Zhang & Tsung, Fugee, 2012. "A comparison study of effectiveness and robustness of control charts for monitoring process mean," International Journal of Production Economics, Elsevier, vol. 135(1), pages 479-490.
    • Handle: RePEc:eee:proeco:v:135:y:2012:i:1:p:479-490
    DOI: 10.1016/j.ijpe.2011.08.026
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    References listed on IDEAS

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    1. Lim, S.L. & Khoo, Michael B.C. & Teoh, W.L. & Xie, M., 2015. "Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated," International Journal of Production Economics, Elsevier, vol. 166(C), pages 20-35.
    2. Qu, Liang & Wu, Zhang & Khoo, Michael B.C. & Castagliola, Philippe, 2013. "A CUSUM scheme for event monitoring," International Journal of Production Economics, Elsevier, vol. 145(1), pages 268-280.
    3. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    4. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    5. Khoo, Michael B.C. & Teoh, W.L. & Castagliola, Philippe & Lee, M.H., 2013. "Optimal designs of the double sampling X¯ chart with estimated parameters," International Journal of Production Economics, Elsevier, vol. 144(1), pages 345-357.
    6. Teoh, W.L. & Khoo, Michael B.C. & Castagliola, Philippe & Yeong, W.C. & Teh, S.Y., 2017. "Run-sum control charts for monitoring the coefficient of variation," European Journal of Operational Research, Elsevier, vol. 257(1), pages 144-158.
    7. Nasir Abbas & Muhammad Riaz & Shabbir Ahmad & Muhammad Abid & Babar Zaman, 2020. "On the Efficient Monitoring of Multivariate Processes with Unknown Parameters," Mathematics, MDPI, vol. 8(5), pages 1-32, May.
    8. Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.

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