A new S2‐TEWMA control chart for monitoring process dispersion

Abstract

Abstract Control charts are very popular quality tools used to detect and control industrial process deviations in statistical process control. The current paper presents a new triple exponentially weighted moving average (TEWMA) control chart for monitoring the process dispersion (named as ‐TEWMA chart), based on a three‐parameter logarithmic transformation of the sample variance. A performance evaluation of the proposed chart is conducted, through Monte‐Carlo simulations, in terms of the average run length and the standard deviation of the run length. A comparison study with the ‐EWMA, CUSUM‐, CS‐EWMA and ‐HEWMA charts indicates that the ‐TEWMA chart is more efficient, than the competing ones, in detecting small shifts in the process variability. Finally, two illustrative examples are provided to explain the application of the ‐TEWMA control chart.