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Statistical computation and analyses for attribute events

Author

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  • Liu, Yafen
  • He, Zhen
  • Shu, Lianjie
  • Wu, Zhang

Abstract

This article studies the monitoring of the attribute events based on statistical computation and analyses. The size of an attribute event is an integer rather than a continuous variable. For example, the detection of a product lot containing defectives is an attribute event, the size of which is the number of defectives found in this lot. While many control charts have been developed for monitoring the time interval (T) between the occurrences of an event, many other attribute charts may be employed to examine the size (C) of the event. However, these two types of control charts have been investigated and applied separately with limited syntheses in Statistical Process Control (SPC). This article presents a single SPC chart (called the rate chart for attribute, or rate chart in short) for simultaneously monitoring the time interval T and size Cof an attribute event based on the ratio between C and T. Our studies show that the new chart is more effective for detecting the out-of-control status of the attribute event compared with an individual t chart or an individual c chart, as well as a combined chart. More profound is that the rate chart performs more uniformly than other charts for detecting both T shift and C shift, as well as the joint shift in T and C. The rate chart has demonstrated its potential for both manufacturing systems and non-manufacturing sectors (e.g., supply chain management, office administration and health care industry), especially for the latter.

Suggested Citation

  • Liu, Yafen & He, Zhen & Shu, Lianjie & Wu, Zhang, 2009. "Statistical computation and analyses for attribute events," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3412-3425, July.
  • Handle: RePEc:eee:csdana:v:53:y:2009:i:9:p:3412-3425
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    References listed on IDEAS

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    1. Rigby, R.A. & Stasinopoulos, D.M. & Akantziliotou, C., 2008. "A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 381-393, December.
    2. Wu, Zhang & Zhang, Xiaolan & Yeo, Song Huat, 2001. "Design of the sum-of-conforming-run-length control charts," European Journal of Operational Research, Elsevier, vol. 132(1), pages 187-196, July.
    3. 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.
    4. S. Chakraborti & S. W. Human, 2008. "Properties and performance of the c-chart for attributes data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(1), pages 89-100.
    5. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
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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. Bersimis, Sotiris & Koutras, Markos V. & Maravelakis, Petros E., 2014. "A compound control chart for monitoring and controlling high quality processes," European Journal of Operational Research, Elsevier, vol. 233(3), pages 595-603.
    3. 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.
    4. Wenjuan Liang & Xiaolong Pu & Dongdong Xiang, 2017. "A distribution-free multivariate CUSUM control chart using dynamic control limits," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2075-2093, August.

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