Statistical process control procedures for correlated observations

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.