Evaluating the statistical process control performance for monitoring stationary observations using Monte Carlo simulation

This research aimed to evaluate the performance of the statistical control charts under the situation that the observations were correlated. The autoregressive moving average model, ARMA (1, 1), was utilized to characterize the disturbances. A process model was simulated to achieve the response, the average run length (ARL). The factorial design of experiment was conducted to define the impacts of critical factors e.g. ARMA coefficients, types of charts and shift sizes on the ARL. The experimental results showed that the exponentially weighted moving average (EWMA) was the most appropriate chart to monitor the ARMA (1, 1) observations. Additionally, both AR and MA parameters along with shift sizes had significant effects on the ARL. Therefore, the available results indicated that conditions at which the minimization of ARL can be achieved. If the performance of the statistical process control under stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when deploying SPC.