Efficient and distribution-free charts for monitoring the process location for individual observations

Sudden and sequential variations are crucial in industrial and production processes. To track these consistent changes in process parameters, effective charting methods are needed. The generally weighted moving average (GWMA) chart outperforms the exponentially weighted moving average (EWMA) chart in detecting small changes under various design parameters. However, its application relies on process distribution normality assumptions. This study presents new distribution-free GWMA control charts for individual measurements when the central limit theorem doesn't apply under simple random sampling. The charts' robustness and performance are evaluated under symmetric, skewed, and contaminated process environments, using run length properties, relative mean index (RMI), and extra quadratic loss (EQL) for overall assessment. The proposed chart outperforms existing charts in detecting specific and over-the-range shifts with appropriate design parameter choices. It's been applied to an electronics dataset where voltage on constant capacitance serves as a key quality characteristic, validating the theoretical findings.