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A CUSUM scheme for event monitoring

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  • Qu, Liang
  • Wu, Zhang
  • Khoo, Michael B.C.
  • Castagliola, Philippe

Abstract

This article presents a single CUSUM scheme (called the GCUSUM chart) for simultaneously monitoring the time interval T and magnitude X of an event. For example, a traffic accident may be considered as an event, and the total loss in dollars in each case is the event magnitude. Since the GCUSUM chart is developed based on a synthetic statistic G which is a function of both T and X, this new chart is able to make use of the information about the event frequency, as well as the information about the event magnitude. Moreover, the detection power of the GCUSUM chart can be allocated in an optimal manner between those against T shifts and against X shifts, and between those against small shifts and against large shifts. The performance studies show that the GCUSUM chart is more effective than all other charts in the current literature for detecting the out-of-control status of an event. Furthermore, the GCUSUM chart performs more uniformly for detecting process shifts of different types and sizes. This chart is also easier to be designed and implemented than other CUSUM charts for monitoring both T and X. The GCUSUM chart has the potential to be applied to many different areas, especially to the non-manufacturing and service sectors, such as supply chain management, homeland security, office administration and the health care industry.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:268-280
    DOI: 10.1016/j.ijpe.2013.04.048
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    References listed on IDEAS

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    1. Wu, Zhang & Jiao, Jianxin & He, Zhen, 2009. "A single control chart for monitoring the frequency and magnitude of an event," International Journal of Production Economics, Elsevier, vol. 119(1), pages 24-33, May.
    2. 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.
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    4. Patrick Bourke, 2001. "The geometric CUSUM chart with sampling inspection for monitoring fraction defective," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(8), pages 951-972.
    5. Lee, Pei-Hsi & Torng, Chau-Chen & Liao, Li-Fang, 2012. "An economic design of combined double sampling and variable sampling interval X¯ control chart," International Journal of Production Economics, Elsevier, vol. 138(1), pages 102-106.
    6. 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.
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    9. Patrick Bourke, 2006. "The RL2 chart versus the np chart for detecting upward shifts in fraction defective," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(1), pages 1-15.
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    Cited by:

    1. Ali, Sajid & Pievatolo, Antonio, 2018. "Time and magnitude monitoring based on the renewal reward process," Reliability Engineering and System Safety, Elsevier, vol. 179(C), pages 97-107.
    2. 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.
    3. Shamstabar, Yousof & Shahriari, Hamid & Samimi, Yaser, 2021. "Reliability monitoring of systems with cumulative shock-based deterioration process," Reliability Engineering and System Safety, Elsevier, vol. 216(C).

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