A clustering approach to identify the time of a step change in Shewhart control charts

Control charts are the most popular Statistical Process Control (SPC) tools used to monitor process changes. When a control chart indicates an out of control signal it means that the process has changed. However control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as change-point estimation problem. Most of the traditional methods of estimating the process change-point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart changepoints. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase-I and phase-II of normal and non-normal processes. At the end, we discuss the performance of the proposed method in comparison to the traditional procedures through extensive simulation studies.