Comparison of efficiency between classical and modified EWMA control charts using different robust estimators

Quality control is a crucial practice across various industries to ensure that products and services meet specific standards and fulfill customer expectations. Statistical process control (SPC) is a quality control method that utilizes statistical techniques to monitor and manage a process. This study focuses on EWMA control charts along with robust estimators for monitoring the process mean. The EWMA control chart is particularly effective in detecting small shifts in a process. By implementing SPC, it is usually assumed that the data follows a normal distribution. However, in real-life scenarios, this assumption may not always hold true, and the actual distribution of the data might be unknown. Therefore, in this study, we propose the use of Qn and Biweight Midvariance estimators for constructing EWMA Control Charts. Two data sets, one with heavy skewed and one is slightly skewed, were used in this study. As a result, the EWMA-Qn Control Chart is deemed the most efficient, as it can detect out-of-control points more quickly, regardless of whether the data is heavy skewed or slightly skewed. This method is especially useful in fields such as finance and economics, healthcare and manufacturing where process stability and early detection of shifts are critical. Consequently, the efficiency of the EWMA-Qn Control Chart results in better process monitoring, fewer false alarms, and better decision-making in practical situations, which results in better quality control and utilization of resources in different industries.

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