The classical Shewhart np-chart that constructed based on the binomial distribution is inappropriate in monitoring over-dispersion and correlated binary data where it tends to overestimate or underestimate the dispersion and subsequently lead to higher or lower false alarm rate in detecting out-of-control signals. Consequently, the np-chart is recommended based on a multiplicative-binomial dist...

BACKGROUND Comparison of outcomes requires adequate risk adjustment for differences in patient risk and the type of intervention performed. Both unintentional and intentional misclassification (also called gaming) of risk factors might lead to incorrect benchmark results. Therefore, misclassification of risk factors should be detected. We investigated the use of statistical process control tech...

The most commonly used statistical process control charts to detect special causes are Shewhart and Cusum charts. However, the Cuscore chart is a contemporary alternative that is especially well suited for detecting special causes that can be modeled in advance based on their characteristic effect on the system. In this paper, we develop the appropriate Cuscore statistic and the required contro...

When using control charts to monitor manufacturing processes, Shewhart control chart is known to be useful for detecting transient shifts, while the EWMA and CUSUM charts are useful for detecting persistent shifts. The efficiency of EWMA chart in monitoring location parameter can be improved by using an auxiliary variable that is closely related to the variable of interest. In this paper, an EW...

Some quality control schemes have been developed when several related quality characteristics are to be monitored. The familiar multivariate process monitoring and control procedure is the Hotelling’s T 2 control chart for monitoring the mean vector of the process. It is a direct analog of the univariate shewhart x̄ chart. As in the case of univariate, the ARL improvements are very important par...

Process safety is a critical component in various process industries. Statistical process monitoring techniques were initially developed to maximize efficiency and productivity, but over the past few decades with catastrophic industrial disasters, process safety has become a top priority. Sensors play a crucial role in recording process measurements, and according to the number of monitored var...

Abstract This study aim to propose a moving average control chart explicit formula based on the Generalized Poisson distribution ( MA GP ) and compare performance of with that Shewhart C ). The run length ARL is used consider performance. results indicated superior for detecting small shifts. Practitioners will find this be accurate as well simple apply use.

Industrial production requires multivariate control charts to enable monitoring of several components. Recently there has been an increased interest also in other areas such as detection of bioterrorism, spatial surveillance and transaction strategies in finance. In the literature, several types of multivariate counterparts to the univariate Shewhart, EWMA and CUSUM methods have been proposed. ...

The control chart is an important statistical technique that is used to monitor the quality of a process. Shewhart control charts are used to detect larger disturbances in the process parameters, whereas CUSUM and EWMA charts are meant for smaller and moderate changes. Runs rules schemes are generally used to enhance the performance of Shewhart control charts. In this study, we propose two runs...

During the last two decades, many new methods have appeared in statistical process monitoring with synthetic-type control charts being a prominent constituent. These became popular due to their simplicity and proclaimed excellent change point detection performance. Synthetic are nothing more than application of well-known long established runs rules. We show that better performance can be obtai...