An adaptive extended EWMA control chart for enhanced detection of process mean shifts

Adaptive control charts are widely used to detect process mean shifts when shift magnitudes are unknown or variable. Although the extended exponentially weighted moving average (EEWMA) chart improves detection by reducing variance, existing EEWMA schemes typically use fixed smoothing parameters and lack adaptability. This study proposes a novel adaptive EEWMA (AEEWMA) control chart to monitor process mean shifts. An unbiased shift estimator derived from the EEWMA statistic is employed to adaptively adjust the smoothing parameters according to shift magnitude. Unlike existing adaptive EWMA-type charts, the proposed scheme directly incorporates adaptive weighting into the EEWMA structure, enabling dynamic responses to unknown shifts while retaining variance-reduction benefits. Average run length results demonstrate improved detection of small and moderate shifts while maintaining satisfactory in-control performance, making the AEEWMA chart a flexible and effective monitoring approach for processes with uncertain shift magnitudes.