Run sum chart for monitoring multivariate coefficient of variation

Alex J. X. Lim, Michael B.C. Khoo*, W. L. Teoh, Abdul Haq

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)


Coefficient of variation (CV) is an important quality characteristic to take into account when the process mean and standard deviation are not constants. A setback of the existing chart for monitoring the multivariate CV is that the chart is slow in detecting a multivariate CV shift in the Phase-II process. To overcome this problem, this paper proposes a run sum chart for monitoring the multivariate CV in the Phase-II process. The average run length (ARL), standard deviation of the run length (SDRL) and expected average run length (EARL), under the zero state and steady state cases, are used to compare the performance of the proposed chart with the existing multivariate CV chart. The proposed chart's optimal parameters are computed using the Mathematica programs, based on the Markov chain model. Two one-sided run sum charts for monitoring the multivariate CV are considered, where they can be used simultaneously to detect increasing and decreasing multivariate CV shifts. The effects of different in-control CV values, number of regions, shift and sample sizes, and number of variables being monitored are studied. The implementation of the proposed chart is illustrated with an example using the data dealing with steel sleeve inside diameters.

Original languageEnglish
Pages (from-to)84-95
Number of pages12
JournalComputers and Industrial Engineering
Early online date18 Apr 2017
Publication statusPublished - Jul 2017


  • Average run length (ARL)
  • Coefficient of variation (CV)
  • Expected average run length (EARL)
  • Markov chain
  • Run sum
  • Standard deviation of the run length (SDRL)

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)


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