This paper investigates control charts for detecting special causes in an ARIMA(0,1,1) process that is being adjusted automatically after each observation using a minimum mean-squared error adjustment policy. It is assumed that the adjustment mechanism is designed to compensate for the inherent variation due to the ARIMA(0,1,1) process, but it is desirable to detect and eliminate special causes...

BACKGROUND There is considerable recent interest in the monitoring of individual surgeon or hospital surgical outcomes. If one aggregates data over time and assesses performance with a funnel plot, then the detection of any process deterioration or improvement could be delayed. The variable life adjusted display (VLAD) is widely used for monitoring on a case-by-case basis, but we show that use ...

Many extensions and modifications have been made to standard process monitoring methods such as the exponentially weighted moving average (EWMA) chart cumulative sum (CUSUM) chart. In addition, new schemes proposed based on alternative weighting of past data, usually put greater emphasis data less weight current recent data. other cases, output one method, EWMA statistic, is used input another ...

Cumulative sum (CUSUM) and exponentially weighted moving average (EWMA) control charts are commonly used to detect small changes in the parameters of production processes. Recently, a new control structure was introduced, named as mixed EWMA–CUSUM control chart, which combined both charts. The current study provides a detailed comparison of these three types of control charts when the process p...

In this research, we propose the new mixed control chart called Moving Average-Cumulative Sum (MA-CUSUM) used for monitoring parameter changes in asymmetrical and symmetrical processes. Its efficiency was compared with that of Shewhart, Cumulative (CUSUM), Average (MA), Sum-Moving (CUSUM-MA) charts by using their average run lengths (ARLs), standard deviation length (SDRL), median (MRL) via Mon...

A Cumulative Sum (CUSUM) control chart capable of detecting changes in both the mean and the standard deviation for autocorrelated data, referred to as the Max-CUSUM chart for Autocorrelated Process chart (MCAP chart), is proposed. This chart is based on fitting a time series model to the data, and then calculating the residuals. The observations are represented as a first-order autoregressive ...

Statistical process control charts are intended to assist operators of a usually stable system in monitoring whether a change has occurred in the process. When a change does occur, the control chart should detect it quickly. If the operator can also be provided information that aids in the search for the special cause, then critical off-line time can be saved. We investigate a process-monitorin...