ARL PROPERTIES OF A SAMPLE AUTOCORRELATION CHART

There are several statistical process control (SPC) methods for detect ing the presence of special causes of variation when process observati ons are inherently autocorrelated. Most of these methods, however, foc us on studying changes in the mean or variance of a time series as a s ignal of the presence of these special causes. It is seldom emphasized in the quality literature that such causes of variation are manifeste d not only by changes in the mean or variance of a time series but als o by the changes in its stochastic behavior. A method that specificall y focuses on monitoring this type of change is the sample autocorrelat ion chart (SACC). The SACC is claimed to be capable of detecting chang es in mean, variance and stochastic behavior of a series, but no detai led studies have been reported concerning such properties. In this pap er, we conduct Monte Carlo experiments to analyze the average run leng th (ARL) properties of the SACC. The results show that, in comparison with the existing techniques for monitoring autocorrelated processes, the SACC is less sensitive in detecting mean and variance shifts but v ery competitive in detecting changes in the parameters of an ARMA mode l. (C) 1997 Elsevier Science Ltd.