The control chart is a very popular tool of statistical process control. It is used to determine the existence of special cause variation to remove it so that the process may be brought in statistical control. Shewhart-type control charts are sensitive for large disturbances in the process, whereas cumulative sum (CUSUM)–type and exponentially weighted moving average (EWMA)–type control charts ...

Monitoring coefficient of variation is one of the successful approaches to Statistical Process Control (SPC) when the process mean and standard deviation are not constants. This paper presents a modified Exponentially Weighted Moving Average (EWMA) chart in order to further enhance the sensitivity of the EWMA control chart proposed by Castagliola et al. (2011). Tables are provided for the stati...

This paper proposes an exponentially weighted moving average scheme with variable sampling intervals for monitoring linear profiles. A computer program in Fortran is available to assist in the design of the control chart and the algorithm of the Fortran program is also given. Some useful guidelines are also provided to aid users in choosing parameters for a particular application. Simulation re...

Residual-based control charts are popular methods for statistical process control of autocorrelated processes. To implement these methods, a time series model of the process is required. The model must be estimated from data, in practice, and model estimation errors can cause the actual in-control average run length to differ substantially from the desired value. This article develops a method ...

The exponentially weighted moving average (EWMA) control chart has been widely studied as a tool for monitoring normal processes due to its simplicity and efficiency. However, relatively little attention has been paid to EWMA charts for monitoring Poisson processes. This paper extends EWMA charts to Poisson processes with emphasis on quick detection of increases in Poisson rate. Both cases with...

BACKGROUND Influenza viruses cause seasonal outbreaks in temperate climates, usually during winter and early spring, and are endemic in tropical climates. The severity and length of influenza outbreaks vary from year to year. Quick and reliable detection of the start of an outbreak is needed to promote public health measures. METHODS We propose the use of an exponentially weighted moving aver...

Monitoring medical outcomes is desirable to help quickly detect performance changes. Previous applications have focused mostly on binary outcomes, such as 30-day mortality after surgery. However, in many applications the survival time data are routinely collected. In this paper, we propose an updating exponentially weighted moving average (EWMA) control chart to monitor risk-adjusted survival t...

Distribution-free (nonparametric) control charts provide a robust alternative to a data analyst when there is lack of knowledge about the underlying distribution. A two-sided nonparametric Phase II exponentially weighted moving average (EWMA) control chart, based on the exceedance statistics (EWMA-EX), is proposed for detecting a shift in the location parameter of a continuous distribution. The...

The Adaptive Exponentially Weighted Moving Average (AEWMA) control chart has the advantage of detecting in balance mixed range of mean shifts. Its performance has been studied under the assumption that the process parameters are known. Under this assumption, previous studies have shown that AEWMA provides superior statistical performance when compared to other different types of control charts....

Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popula...