An algorithm for quality control charts for autocorrelated data

Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objective of this paper is to develop an algorithm to adjust a model ARMA(p,q), for calculate the run length distribution (RLD), the average run length (ARL), and the standard deviation of the run length (SRL), for residual control charts X(ind) and MR used to monitor autocorrelated processes. The algorithm was used for analysis of real data. We conclude that for negative first-order autocorrelation, the residuals chart is performing better than the Shewhart chart for independent observations.