Performance of sensitizing rules on Shewhart Control Charts with auto correlated data

Sandy D. Balkin, Dennis K.J. Lin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Sensitizing Rules are commonly applied to Shewhart Charts to increase their effectiveness in detecting shifts in the mean that may otherwise go unnoticed by the usual "outof-control" signals. The purpose of this paper is to demonstrate how well these rules actually perform when the data exhibit autocorrelation compared to non-correlated data. Since most control chart data are collected as time series, it is of interest to examine the performance of Shewhart's ̄ Chart using data generated from typical time series models. In this paper, measurements arising from autoregressive (AR), moving average (MA) and autoregressive moving average (ARMA) processes are examined using Shewhart Control Charts in conjunction with several sensitizing rules. The results indicate that the rules work well when there are strong autocorrelative relationships, but are not as effective in recognizing small to moderate levels of correlation. We conclude with the recommendation to practitioners that they use a more definitive measure of autocorrelation such as the Sample Autocorrelation Function correlogram to detect dependency.

Original languageEnglish (US)
Pages (from-to)159-171
Number of pages13
JournalInternational Journal of Reliability, Quality and Safety Engineering
Issue number2
StatePublished - 2001

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Nuclear Energy and Engineering
  • Safety, Risk, Reliability and Quality
  • Aerospace Engineering
  • Energy Engineering and Power Technology
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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