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A new SPRT chart for monitoring process mean and variance

Author

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  • Ou, Yanjing
  • Wu, Zhang
  • Goh, Thong Ngee

Abstract

This article proposes a new control chart (called the ABS SPRT chart) for Statistical Process Control (SPC) based on Sequential Probability Ratio Test (SPRT). This chart is able to monitor the mean and variance of a variable x simultaneously by inspecting the absolute sample shift x-[mu]0 (where [mu]0 is the in-control mean or target value of x). The ABS SPRT chart is designed by an optimization algorithm, aiming at minimizing the Average Extra Quadratic Loss (AEQL) over the process shift domain. The results of intensive performance studies show that the ABS SPRT chart not only uniformly outperforms the CUSUM chart with a Variable Sample Size (VSS) feature, but is also more effective than a 2-SPRT scheme which incorporates a lower-sided SPRT chart and an upper-sided one. From a holistic viewpoint, the ABS SPRT chart detects process shifts in mean and variance faster than the VSS CUSUM chart and 2-SPRT scheme by more than 30% and 13%, respectively. Noteworthily, the design of an ABS SPRT chart is relatively easier than that of a VSS CUSUM chart, and much simpler than the design of a 2-SPRT scheme.

Suggested Citation

  • Ou, Yanjing & Wu, Zhang & Goh, Thong Ngee, 2011. "A new SPRT chart for monitoring process mean and variance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 303-314, August.
    • Handle: RePEc:eee:proeco:v:132:y:2011:i:2:p:303-314
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    References listed on IDEAS

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    1. Wu, Zhang & Yang, Mei & Khoo, Michael B.C. & Yu, Fong-Jung, 2010. "Optimization designs and performance comparison of two CUSUM schemes for monitoring process shifts in mean and variance," European Journal of Operational Research, Elsevier, vol. 205(1), pages 136-150, August.
    2. Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    3. Serel, Dogan A. & Moskowitz, Herbert, 2008. "Joint economic design of EWMA control charts for mean and variance," European Journal of Operational Research, Elsevier, vol. 184(1), pages 157-168, January.
    4. Ho, Linda Lee & Trindade, Anderson Laécio Galindo, 2009. "Economic design of an X chart for short-run production," International Journal of Production Economics, Elsevier, vol. 120(2), pages 613-624, August.
    5. De Magalhães, M.S. & Costa, A.F.B. & Moura Neto, F.D., 2009. "A hierarchy of adaptive control charts," International Journal of Production Economics, Elsevier, vol. 119(2), pages 271-283, June.
    6. Torng, Chau-Chen & Lee, Pei-Hsi & Liao, Nai-Yi, 2009. "An economic-statistical design of double sampling control chart," International Journal of Production Economics, Elsevier, vol. 120(2), pages 495-500, August.
    7. Zhang Wu & Jianxin Jiao & Mei Yang & Ying Liu & Zhaojun Wang, 2009. "An enhanced adaptive CUSUM control chart," IISE Transactions, Taylor & Francis Journals, vol. 41(7), pages 642-653.
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    Cited by:

    1. Du, Shichang & Lv, Jun, 2013. "Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes," International Journal of Production Economics, Elsevier, vol. 141(1), pages 377-387.
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    3. Lee, Pei-Hsi, 2013. "Joint statistical design of X¯ and s charts with combined double sampling and variable sampling interval," European Journal of Operational Research, Elsevier, vol. 225(2), pages 285-297.
    4. 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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