Statistical process control for serially correlated data

Abstract

Statistical Process Control (SPC) aims at quality improvement through reduction of variation. The best known tool of SPC is the control chart. Over the years, the control chart has proved to be a successful practical technique for monitoring process measurements. However, its usefulness in practice is limited to those situations where it can be assumed that successive measurements are independently distributed, whereas most data sets encountered in practice exhibit some form of serial correlation. In Chapter 1, several ‘real-life’ examples are discussed in which the independence assumption is violated. The examples show that in some cases, a control chart signals too frequently when the process is actually in control. In other cases, a control chart does not signal when it should. In either case, it is obvious that such a control chart is not the proper tool to monitor serially correlated process data. The question that is considered in this thesis is what control chart methods should be used to monitor serially correlated data, and how the signals on such charts should be interpreted.

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