The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. employment SQC methodologies is frequently employed for monitoring identification process irregularities across fields. This research proposes implements a novel methodology agricultural areas. A control chart one the tools that facilitates ...

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...

In this paper, a comparative study of performance of some parametric and nonparametric EWMA control charts for detecting the process mean shift is considered. In section 2, we briefly describe each of the various EWMA models that are taken into account in our present study, Viz. the Parametric X -EWMA and Nonparametric Grouped Signed Rank (GSR)-EWMA, for the purpose of comparative study. In Sec...

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....

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...

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...

Traditionally, an ?̄? chart is used to control the process mean and an R chart is used to control the variance. However, these charts are not sensitive to the small shifts in the processes. The adaptive charts might be considered if the aim is to detect process changes quickly. In this paper, we propose a new adaptive single control chart which integrates the exponentially weighted moving averag...

Monitoring multivariate quality characteristics is very common in production and service environment. Therefore, many control charts have been suggested by authors for monitoring multivariate processes. In another side, profile monitoring is a new approach in the area of statistical process control. In this approach, the quality of a product or a process is characterized by a relation between o...