For processes described by linear transfer functions with additive disturbances, the best possible control in the mean square sense is realized when a minimum variance controller is implemented. It is shown that an estimate of the best possible control can be obtained by fitting a univariate time series to process data collected under routine control. No ‘identifiabüity’ constraints need be imposed. The use of this technique is demonstrated with pilot plant and production data.

Roberts (1959) first introduced the exponentially weighted moving average (EWMA) control scheme. Using simulation to evaluate its properties, he showed that the EWMA is useful for detecting small shifts in the mean of a process. The recognition that an EWMA control scheme can be represented as a Markov chain allows its properties to be evaluated more easily and completely than has previously been done. In this article, we evaluate the properties of an EWMA control scheme used to monitor the mean of a normally distributed process that may experience shifts away from the target value. A design procedure for EWMA control schemes is given. Parameter values not commonly used in the literature are shown to be useful for detecting small shifts in a process. In addition, several enhancements to EWMA control schemes are considered. These include a fast initial response feature that makes the EWMA control scheme more sensitive to start-up problems, a combined Shewhart EWMA that provides protection against both large and small shifts in a process, and a robust EWMA that provides protection against occasional outliers in the data that might otherwise cause an out-of-control signal. An extensive comparison reveals that EWMA control schemes have average run length properties similar to those for cumulative sum control schemes.

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.

A normalized performance index, η(b), is introduced to characterize the performance of feedback control schemes. η(b) is attractive because it provides a measure of the proximity of control to minimum variance control, which is the optimal feedback control provided that the process can be described by a linear transfer function with additive disturbance. Both time domain and spectral interpretations of this performance index are discussed. A fast, simple, on‐line method for estimating η(b) is given, along with some of the statistical properties of the estimator. Simulation and industrial examples demonstrate the utility of η(b/)

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