Auxiliary information based generally weighted moving coefficient of variation (AIB-GWMCV) control chart

Abstract

Statistical Process Control (SPC) is a statistical technique to accurately determine process performance. It has been widely used in manufacturing industries and services. The most powerful instrument used in SPC is the control chart. It is designed to observe and detect timely assignable causes of the process. In general, the control chart is employed to detect shifts in process location and process dispersion. Monitoring the coefficient of variation (CV) is efficient in SPC when the standard deviation process changes with the mean. Also, the mean itself is expected to fluctuate with time but considered to be in-control. Recently, some studies investigate using auxiliary information to enhance the sensitivity of the CV control chart. This study investigates auxiliary information based CV-GWMA (AIB-GWMCV) control chart by using log-normal transformation to detect the small to large shifts in process CV. It turns out the proposed control chart performs better than the CV-GWMA control chart using log-normal transformation without auxiliary information. Also, a real case is presented to display the application of the AIB-GWMCV control chart. Results show that the proposed control chart is faster to detect shifts in the process CV than the control chart without auxiliary information.