A Comparative Analysis of Robust Moving Average Control Charts for Process Dispersion

  • Moustafa Omar Ahmed Abu-Shawiesh Department of Mathematics, Faculty of Science, The Hashemite University, Al-Zarqa, Jordan
  • Aamir Saghir Department of Mathematics, Mirpur University of Science and Technology, Mirpur, Pakistan
  • Mohammed Hani Mufleh Almomani Department of Mathematics, Faculty of Science, The Hashemite University, Al-Zarqa, Jordan
  • Mokhtar Abdullah Deputy Vice Chancellor for Academic Affairs, Meritus University, Kuala Lumpur, Malaysia
  • Hatim Solayman Ahmed Migdadi Department of Mathematics, Faculty of Science, The Hashemite University, Al-Zarqa, Jordan
Keywords: Moving average control chart, robust dispersion estimator, standard deviation, non-normal distribution, simulation study, average run length

Abstract

In this paper, a comparative analysis of alternative methods for the moving average (MA) control chart for dispersion is developed using robust estimators. To compare the ability and performance of the existing moving average (MA) control charts for dispersion based on the sample standard deviation (S) and the proposed alternative methods based on robust estimators to detect shifts in a process, a Monte Carlo simulation study is used. It is observed from the results of the simulation study that the proposed robust alternative methods are effective in determining small shifts in the process and gives better performance as compared to the existing moving average (MA) control charts for dispersion, i.e. it provides swift indication about shifts in a process. An application numerical example with a real data set is used to illustrate the application and implementation of the control charts considered in this study which also supported the findings of the simulation study to some extent.

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Published
2021-03-29
Section
Articles