A time‐series approach to discrete real‐time process quality control

Abstract Statistical process control methods are usually applied in an environment when periodic sampling and rational subgrouping of process output is appropriate. The resulting summary statistics can be graphically displayed and analysed using either traditional Shewhart control charts or other charts such as those based on the cumulative sum. This article presents an alternative approach, based on time series analysis of all the real‐time process data. The time series approach is employed because the sequence of process observations may not be statistically independent. The autocorrelative structure in the data may be captured using an ARIMA model, and the residuals from this model are shown to be an effective input signal for a variety of statistical process control procedures.