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A single control chart for monitoring the frequency and magnitude of an event

Author

Listed:
  • Wu, Zhang
  • Jiao, Jianxin
  • He, Zhen

Abstract

While many control charts have been developed for monitoring the time interval (T) between the occurrences of an event; many other charts are employed to examine the magnitude (X) of the event. This article presents a single statistical process control (SPC) chart (called the rate chart) for simultaneously monitoring the time interval T and magnitude X based on the ratio between X and T. The new chart is more effective than an individual T chart, individual X chart, or a combined T&X chart for detecting the out-of-control status of the event, especially for detecting downward shifts (sparse occurrence and/or small magnitude). More profound is that, comparing with an individual T or X chart, the rate chart performs more uniformly for detecting both T shift and X shift, as well as the joint shift in T and X. The rate chart has demonstrated its potential not only for manufacturing systems, but also for non-manufacturing sectors such as supply chains, office administration and health care industry.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:119:y:2009:i:1:p:24-33
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    References listed on IDEAS

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    1. Wu, Zhang & Luo, Hua & Zhang, Xiaolan, 2006. "Optimal np control chart with curtailment," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1723-1741, November.
    2. Chen, Shun-Hsing & Yang, Ching-Chow & Lin, Wen-Tsann & Yeh, Tsu-Ming, 2008. "Performance evaluation for introducing statistical process control to the liquid crystal display industry," International Journal of Production Economics, Elsevier, vol. 111(1), pages 80-92, January.
    3. 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.
    4. Zhang, Cai Wen & Xie, Min & Goh, Thong Ngee, 2008. "Economic design of cumulative count of conforming charts under inspection by samples," International Journal of Production Economics, Elsevier, vol. 111(1), pages 93-104, January.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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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