Statistical process control procedures for correlated observations

TLDR

Industrial process measurements are frequently serially correlated. The paper investigates how serial correlation affects cumulative sum and exponentially weighted moving average charts and outlines time‑series methods to address it. Time‑series techniques are employed to adjust cumulative sum and EWMA charts for serial correlation. Ignoring serial correlation can lead to serious errors in assessing statistical process control, as demonstrated with basis weight measurements.

Abstract

Abstract Measurements from industrial processes are often serially correlated. The impact of this correlation on the performance of the cumulative sum and exponentially weighted moving average charting techniques is investigated in this paper. It is shown that serious errors concerning the “state of statistical process control” may result if the correlation structure of the observations is not taken into account. The use of time series methods for coping with serially correlated observations is outlined. Paper basis weight measurements are used to illustrate the time series methodology.