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Abstract
In the present article, we propose a generally weighted moving average control chart based on a three-parameter logarithmic transformation for monitoring shifts in the process variability (regarded as S 2-GWMA chart). Monte Carlo simulations are used to evaluate its performance in terms of the run-length distribution. A comparison study versus the S 2-EMWA, S 2-CUSUM, CS-EWMA, S 2-HEWMA and GWMAV charts indicates that the proposed chart is more effective than its competitors in detecting small to moderate upward shifts while it performs similarly with the other charts for large shifts. Finally, a real example is presented to display the application of the S 2-GWMA 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.
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 interests include statistical experimental and optimal designs, statistical quality control, biostatistics, and combinatorial designs.
Angeliki Lappa
Angeliki Lappa is a PhD Candidate of the Department of Mathematics at the National Technical University of Athens, Greece. Her research interests include factorial designs, generalized linear models, and statistical process control.