Combining an EWMA chart with a Shewhart chart is traditionally recommended as a means of providing good protection against both small and large shifts in the process mean. Capizzi and Masarotto have proposed an EWMA chart with a variable smoothing constant (AEWMA) for the same purpose. In this paper, we optimize the designs of the AEWMA and of the combined EWMA-Shewhart schemes with regard to p...

The multi-chart consists of several CUSUM or EWMA charts with different reference values that are used simultaneously to detect anticipated process changes. We not only prove that the chart can quickly achieve the asymptotic optimal bound, but also give an integral equation to determine the reference values to arrive at optimality. Simulation results are used to verify the theoretical optimal p...

Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart...

The Multivariate EWMA control chart, MEWMA, Lowry, Woodall, Champ and Ridgon [1] and its univariate version EWMA, may be designed to efficiently detect small shifts in the mean vector of a set of p quality characteristics of a production process. However, this work presents a method for the optimal design of MEWMA and EWMA charts parameters to control processes where it is not convenient to det...

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 ...

The “predictor-corrector” feedback controller, a process adjustment scheme proposed for semiconductor manufacturing run-to-run processes that drift, is extended to the multiple input–multiple output case. The controller is based on two coupled multivariate exponentially-weighted-moving-average (EWMA) equations, thus its performance depends on the choices of EWMA weight matrices. Stability condi...

Control chart is the most important Statistical Process Control (SPC) tool used to monitor reliability and performance of manufacturing processes. Variability EWMA charts are widely used for the detection of small shifts in process dispersion. For ease in computation all the variability EWMA charts proposed so far are based on asymptotic nature of control limits. It has been shown in this study...

background : there are few published studies that use real data testing to examine the performance of outbreak detection methods. the aim of this study was to determine the performance of the exponentially weighted moving average (ewma) in real time detection of a local outbreak in mashhad city, eastern iran . methods : the ewma algorithms (both ewma 1 with λ=0.3 and ewma 2 with λ=0.6) were app...

This work presents a comparative study of the performance of the cumulative sum (CuSum), as well as the exponentially weighted moving average (EWMA) control charts. The objective of this research is to verify when CuSum and EWMA control charts do the best control region, in order to detect small changes in the process average. Starting from the data of a productive process, several series were ...

This work presents an exponentially weighted moving average (EWMA)-based synthesised control scheme for a process with outliers. Based on experience, some processes occasionally have outliers. The use of traditional mean ( X ) and range (R) control charts (denoted as X=R) for monitoring process mean and variance leads to high-level false alarms. In this study, the fast-detection technique (EWMA...