Cumulative sum control charts for monitoring process mean and/or variance

Abstract

Nowadays quality is an extremely important tool in satisfying customers and winning market shares. When a quality problem occurs, it is crucial to detect it quickly in order to avoid serious economic loss. Control chart is a powerful Statistical Process Control (SPC) method to monitor and diagnose the processes. The cumulative sum (CUSUM) control chart which accumulates historical information in the process is effective to detect process changes including mean and variance shifts. This thesis proposes several new CUSUM charts in detecting process shifts in mean and/or variance. An optimization model which uses an overall performance measure, Average Extra Quality Loss (AEQL), as the objective function is adopted to design these charts. The performance of these charts is compared with that of the most effective CUSUM charts that can be found in current literature. Furthermore, the effect of sampling cost and the probability distribution of process shifts on charts design and performance has also been investigated.