Abstract
•The adaptive EWMA based on CUSUM control chart for process monitoring is proposed.•The proposed study efficiently identifies special causes in industrial processes.•The Huber and Tukey’s functions helps to identify the special causes in processes.•The numerical findings are also compared with other charts to check superiority.•The study is also presented with real data set along other charts for practioniars.
Random causes of variation are part of every process and are harmless to product quality characteristics, while special cause variations appear due to a fault in the process and need special attention. The adaptive exponential weighted moving average (EWMA) control chart based on the cumulative sum (CUSUM) accumulation error is proposed in this study. The aim of this study is detecting imbalanced mixed range (different sizes) shifts (special cause) in process dispersion. The Huber and Tukey bi-square functions are used to enhance the proposed chart efficiency to simultaneously detect these small to large shifts. To check the sensitivity of the proposed chart, numerical results are provided through Monto Carlo simulations. The average run length (ARL) for single shift performance and extra quadratic loss (EQL), relative average run length (RARL) and performance comparison index (PCI) measures are used for overall performance evaluation of the proposed chart. The proposed scheme is compared to existing charts in the literature to determine superiority. For practical purposes, the guidelines are provided using real-life data for their practical implementation.