ABSTRACT. Goal: The main objective of this research paper is to propose a chart, named  Modified Control Chart, where the process variance () allowed be larger than in-control value until maximum (), as long remains capable, in sense that it produces specified (tolerated) small fraction non-conforming items. Design / Methodology Approach: methodology was quantitative approach with statistical...

We consider statistical process control (SPC) of univariate processes when observed data are not normally distributed. Most existing SPC procedures are based on the normality assumption. In the literature, it has been demonstrated that their performance is unreliable in cases when they are used for monitoring non-normal processes. To overcome this limitation, we propose two SPC control charts f...

In recent years, the monitoring of compositional data using control charts has been investigated in Statistical Process Control field. this study, we will design a Phase II Multivariate Exponentially Weighted Moving Average (MEWMA) chart with variable sampling intervals to monitor based on isometric log-ratio transformation. The Time Signal be computed Markov chain approach investigate performa...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

The statistical modeling of social network data needs much effort  because of the complex dependence structure of the tie variables. In order to formulate such dependences, the statistical exponential families of distributions can provide a flexible structure. In this regard, the statistical characteristics of the network is provided to be encapsulated within an Exponential Random Graph Model (...

In most real-world applications, such as production and manufacturing processes, the underlying process distribution does not always follow a normal distribution. cases, statistical control literature recommends use of nonparametric (or distribution-free) charts. This paper introduces new distribution-free precedence chart using repetitive sampling. The performance proposed is investigated in t...

Control chart is the most important Statistical Process Control tool used to monitor reliability and performance of industrial processes. For monitoring changes in process dispersion, the R and S charts are widely used. These control charts perform better under the ideal assumption of normality but are well known to be very inefficient in presence of outliers or departures from normality. In th...

BACKGROUND Time series charts are increasingly used by clinical teams to monitor their performance, but statistical control charts are not widely used, partly due to uncertainty about which chart to use. Although there is a large literature on methods, there are few systematic comparisons of charts for detecting changes in rates of binary clinical performance data. METHODS We compared four co...

Multivariate statistical process control charts are often used for process monitoring to detect out-of-control anomalies. However, multivariate control charts based on conventional statistical distance measures, such as the one used in the Hotelling’s T 2 control chart, cannot scale up to large amounts of complex process data, e.g. data with a large number of variables and a high rate of data s...