Batch-means control charts for autocorrelated data

Modern statistical process control must often cope with large quantities of highly autocorrelated data. Alwan and Radson (1992) proposed the monitoring of autocorrelated processes by plotting the averages of small batches of data separated by skipping observations. Using results for the AR(1) process, we show that generally better performance can be achieved with no skipping and much larger batch sizes. The resulting batch-means charts derive from methods used in simulation output analysis and can be implemented easily with common digital control systems.