Effects of autocorrelation on control chart performance

Optimal Estimation and Sampling Allocation in Survey Sampling Under a General Correlated Superpopulation Model

“…Traditionally the statistical theory behind the control charts is based on the assumption that the sample measurements are independent. It is however quite common in practiceand especially in continuous manufacture or production lines-that this assumption is violated, and this produces misleading and unreliable control charts (Alwan, 1992;Montgomery and Mastrangelo, 1991) with tighter control limits than the true ones. A lot of attention has been drawn lately to this area of research; see for example Alwan and Roberts (1988), Harris and Ross (1991), Mastrangelo and Montgomery (1995), Apley and Lee (2003) and Reynolds (1999, 2001), and all proposed approaches make use of the present autocorrelation to either modify the existing control limits, or to model the process, identify the autocorrelation, and use the independent errors instead of the measurements for constructing any statistical tool.…”

The quality movement in hospital care

“…In many instances, the misplaced control limits result from the autocorrelation of the process observations, which violates a basic assumption often associated with the Shewhart chart (Woodall (2000)). Autocorrelation of process observations has been reported in many industries, including cast steel (Alwan (1992), wastewater treatment plants (Berthouex, Hunter, and Pallesen (1978)), chemical processes industries (Montgomery and Mastrangelo (1991) and many other service industries and programs. Several models have been proposed to monitor processes with auto correlated observations.…”

“…In the next section, we introduce methods and their possible use in processes having dynamic inputs [Yeh and Hwang, (2004)]. Alwan (1992) found that more than 85% of process control applications studied resulted in charts with possibly misplaced control limits. In many instances, the misplaced control limits result from the autocorrelation of the process observations, which violates a basic assumption often associated with the Shewhart chart (Woodall (2000)).…”

The impact of Weibull data and autocorrelation on the performance of the Shewhart and exponentially weighted moving average control charts

“…However, when autocorrelation was large, the ARLs resulted in significantly fewer false alarms (case two); the ARLs were small (under 50) and fairly constant regardless of the shifts in the mean (case one). Alwan (1992) also researched the capability of the Shewhart control chart to detect assignable causes in the presence of autocorrelation. Control limits were fixed and type I and II errors were used to analyze chart performance.…”