A COMBINED MLE AND EWMA CHART APPROACH TO ESTIMATE THE CHANGE POINT OF A GAMMA PROCESS WITH INDIVIDUAL OBSERVATIONS

摘要

Statistical process control (SPC) charts are commonly used for detecting process disturbances. However, they do not provide enough information to identify the root causes of an out-of-control process. This difficulty can be overcome if we are able to promptly estimate the change point of a process, due to the fact that the change point usually reveals the most accurate information about root causes. As a consequence, this estimation becomes a very important research issue in SPC applications. Although recent studies have shown that the maximum likelihood estimation (MLE) estimator could be an effective estimate of the change point for a normal process, very little is known about the feasibility of using an MLE estimator for a gamma process with individual observations. In this study, our goal is to propose a fruitful approach to solving this problem. This study proposes the combination of MLE and the exponentially weighted moving average (EWMA) control charts to estimate the change point of a gamma process. We investigate various SPC modes and gamma process designs in this study, and the results show that an effective change point estimator could be achieved.