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The Problem of Misplaced Control Limits

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  • Layth C. Alwan
  • Harry V. Roberts

Abstract

The paper draws on simple data analysis and time series ideas to assess the limitations of standard control chart procedures in various practical applications in which the underlying assumptions of control charts may be materially violated and, in consequence, the control limits may be misplaced. to give an idea of the kinds and frequency of violations that occur in practice, we summarize an empirical study of a sample of 235 quality control applications. This collection includes two data sets reported by leading statisticians that are explicitly discussed here to illustrate difficulties in the proper placement of control limits. The results of the study of all 235 data sets suggest that violations of assumptions are the rule rather than the exception in practice.

Suggested Citation

  • Layth C. Alwan & Harry V. Roberts, 1995. "The Problem of Misplaced Control Limits," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(3), pages 269-278, September.
    • Handle: RePEc:bla:jorssc:v:44:y:1995:i:3:p:269-278
    DOI: 10.2307/2986036
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    Cited by:

    1. P. Vellaisamy & S. Sankar, 2005. "A Unified Approach for Modeling and Designing Attribute Sampling Plans for Monitoring Dependent Production Processes," Methodology and Computing in Applied Probability, Springer, vol. 7(3), pages 307-323, September.
    2. C. D. Lai & K. Govindaraju & M. Xie, 1998. "Effects of correlation on fraction non-conforming statistical process control procedures," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 535-543.
    3. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    4. Ord, J. Keith & Koehler, Anne B. & Snyder, Ralph D. & Hyndman, Rob J., 2009. "Monitoring processes with changing variances," International Journal of Forecasting, Elsevier, vol. 25(3), pages 518-525, July.
    5. Ridley, D. & Duke, D., 2007. "Moving -window spectral model based statistical process control," International Journal of Production Economics, Elsevier, vol. 105(2), pages 492-509, February.
    6. Nandini Das, 2011. "Control Charts Based on the g-and-h Distribution," Stochastics and Quality Control, De Gruyter, vol. 26(1), pages 3-14, January.
    7. C. D. Lai & M. Xie & K. Govindaraju, 2000. "Study of a Markov model for a high-quality dependent process," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(4), pages 461-473.

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