Generally Weighted Moving Coefficient of Variation (GWMCV) Control Chart Using Three Parametric Log-Normal Transformations

Nowadays, statistical process control (SPC) is extensively utilized in manufacturing companies. Control charts are the foremost tool in SPC because they can timely detect assignable causes that influence the quality of the production process. Mostly, the control charts are used to monitor the process variability and the process mean. However, when the mean changes but is appraised as in-control and the standard deviation is linear to the mean, the Coefficient of Variation (CV) chart is suitable for evaluating process variability. In this study, we suggest a Generally Weighted Moving Coefficient of Variation (GWMCV) control chart using three-parameter logarithmic transformations which parameter weight for each time (Z) is more flexible than the Exponentially Weighted Moving Coefficient of Variation (EWMCV) control chart that has Z 1. The Simulation studies show that when parameter weight 0.6 d Z d 0.9, GWMCV chart is more sensitive than EWMCV chart. As a result, the flexible parameter weights increase the diagnosis ability of the control chart. The chart is also applied to monitor the process production of NPK fertilizer. The result shows that proposed chart detect out-of-control signal rapidly than the EWMCV.