The control charts are graphical tools and proven techniques to improve the performance of a process. Usually, the processes are not naturally controlled, so the use of control charts will help to reduce the variability and increase the stability of the process. In the traditional approach, control charts with fix sample size and constant sampling intervals were used to identify the changes in ...

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...

The need for process monitoring in industry is ubiquitous. By monitoring process output, problems may be rapidly detected and corrected. However, in many industrial and medical applications observations are censored either due to inherent limitations or cost/time considerations. For example, when testing breaking strengths or failure times often a limited stress test is performed and only a sma...

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...

Often the least appropriate assumption in traditional control charting technology is that process data constitute a random sample. In reality most process data are correlated—either temporally, spatially, or due to nested sources of variation. One approach to monitoring temporally correlated data uses a control chart on the forecast errors from a time series model of the process with, possibly,...

In some applications, the quality of a process is characterized by the functional relationship between a response variable and one or more explanatory variables. Profile monitoring is a technique for checking the stability of this relationship over time. General linear profile monitoring is particularly useful in practice due to its simplicity and flexibility. The existing monitoring methods su...

In this paper, we proposed a new quality control chart for the sample mean based on a cost free and anti wasting sampling unit’s scheme known by the folded ranked set sampling (FRSS). The new control charts were compared with the classical control charts when the data obtained by using simple random sampling (SRS) and ranked set sampling (RSS). A simulation study showed that the FRSS based cont...

OBJECTIVE This study aims to compare different control charts to monitor the nosocomial infection rate per 1,000 patient-days. METHODS The control charts considered in this study were the traditional Shewhart chart and a variation of this, the Cumulative Sum and Exponentially Weighted Moving Average charts. RESULTS We evaluated 238 nosocomial infections that were registered in the intensive...

In usual quality control methods, the of a process or product is evaluated by monitoring one more characteristics using their corresponding distributions. However, when characteristic defined through relationship between response and independent variables, regime referred to as profiles monitoring. this article, we improve performance Exponentially Weighted Moving Average Range (EWMAR) charts, ...