The objective of this paper is to quantify the effect of autocorrelation coefficients, shift magnitude, types of control charts, types of controllers, and types of monitored signals on a control system. Statistical process control (SPC) and automatic process control (APC) were studied under non-stationary stochastic disturbances characterized by the integrated moving average model, ARIMA？ . A process model was simulated to achieve two responses, mean squared error (MSE) and average run length (ARL). A factorial design experiment was conducted to analyze the simulated results. The results revealed that not only shift magnitude and the level of autocorrelation coefficients, but also the interaction between these two factors, affected the integrated system performance. It was also found that the most appropriate combination of SPC and APC is the utilization of the minimum mean squared error (MMSE) controller with the Shewhart moving range (MR) chart, while monitoring the control signal (X) from the controller. Therefore, integrating SPC and APC can improve process manufacturing, but the performance of the integrated system is significantly affected by process autocorrelation. Therefore, if the performance of the integrated system under non-stationary disturbances is correctly characterized, practitioners will have guidelines for achieving the highest possible performance potential when integrating SPC and APC. 1. Introduction Better quality leads to cost reduction. The two major techniques used to monitor and reduce the variation in manufacturing processes are statistical process control (SPC) and automatic process control (APC). The SPC method separates assignable causes from common causes. Shewhart control charts are traditional SPC tools based on the assumption that each observation is uncorrelated. However, the independence assumption is violated in many scenarios, especially in continuous process industries where advanced measurement technologies and shortened sampling intervals are used. Under normal, uncorrelated conditions, the process model has a fixed mean ( ), and the fluctuation around the mean is the result of white noise ( ). A process model of SPC can be expressed as follows: However, when observations are correlated, the correlation structure and drift in the mean are characterized by disturbances. If process observations vary around a fixed mean and have a constant variance, this type of variability is called the stationary behaviour. Otherwise, the behaviour is non-stationary. MacGregor [1] indicated that there are two types of
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