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ABSTRACT
The exponentially weighted moving average (EWMA) control chart is a very popular memory-type chart and also effective in detecting small shifts in the process mean. Several modifications of the EWMA chart, such as the double and triple EWMA charts (regarded as DEWMA and TEWMA charts, respectively) have been developed to enhance its performance in detecting small shifts. In the present article, we propose the quadruple EWMA chart (regarded as QEWMA chart) in order to improve much more the detection ability of the EWMA chart. The run-length characteristics of the proposed chart are evaluated by performing Monte Carlo simulations. Comparing with the EWMA, DEWMA and TEWMA charts, it is found that the QEWMA chart outperforms its competitors for small shifts. Moreover, it is shown that the proposed chart is more in-control (IC) robust under several non-normal distributions than the other charts, especially for a medium value of the smoothing parameter. The effect of inertia on the performance of the QEWMA chart is also investigated as a part of this article. Finally, two examples are provided to demonstrate the application of the proposed chart.
Acknowledgments
The authors would like to thank the Editor and the referees for their useful comments which resulted in improving the quality of this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Additional information
Notes on contributors
Vasileios Alevizakos
Vasileios Alevizakos is a PhD candidate of the Department of Mathematics at the National Technical University of Athens, Greece. His research interests include statistical process control, process capability analysis, and robust parameter design.
Kashinath Chatterjee
Kashinath Chatterjee is an adjunct professor of the Division of Biostatistics and Data Science at the Augusta University, Georgia. His research interests include experimental and optimal designs, statistical quality control, reliability analysis, and robust parameter design.
Christos Koukouvinos
Christos Koukouvinos is a professor of the Department of Mathematics at the national Technical University of Athens, Greece. His research interest include statistical experimental and optimal designs, statistical quality control, biostatistics, and combinatorial designs.