Integration of sequential process adjustment and process monitoring techniques

Detecting abnormal disturbances and correcting them through adjustment are essential functions of quality control. This paper discusses a general sequential adjustment procedure based on stochastic approximation techniques and combines it with a control chart for detection. It is assumed that step-like shifts of unknown size occur in the process mean at unknown points of time. The performance of the proposed methods depends on the sensitivity of the control chart to detect shifts in the process mean, on the accuracy of the initial estimate of the shift size, and on the number of sequential adjustments that are made. It is shown that sequential adjustments are superior to single adjustment strategies for almost all types of process shifts and magnitudes considered. A CUSUM (cumulative sum) chart used in conjunction with our sequential adjustment approach can improve the average squared deviations, the performance index considered herein, more than any other combined scheme unless the shift size is very large. The proposed integrated approach is compared with always applying a standard integral or exponentially weighted moving average controller with no monitoring component. Combining control charts and sequential adjustments is recommended for monitoring and adjusting a process when random shocks occur infrequently in time.