The design of the control charts for the process mean assumes that this parameter remains fixed on its target value until the occurrence of a special cause that moves it. However, in many cases, it is more reasonable to assume that the mean wanders even in the absence of special causes. The AR(1) model has been considered to describe this wandering behavior. When the wandering behavior is responsible for significant proportion of data variability, the best performance of the x̄ chart is obtained with samples of size one (n=1). The same is not true for the EWMA control chart (except when the smoothing parameter λ is very close to one); its best performance is achieved with the adoption of n > 1 and small λ, even when the focus is to easily detect significant changes in the process mean position. In this study, the ATS, the average time between the occurrence of a change in the process mean position and the signal, was used as a performance measure. When the process mean wanders, the ATS becomes a function of the expected number the transient states of a Markov chain are visited.
x̄ chart; EWMA control chart; Autocorrelation; Statistical process control; Statistical quality control; Markov chains
