Statistical Process Control (SPC) is used to monitor the process stability which ensures the predictability of the process. In 1920‟s Shewhart introduced the control chart techniques that are one of the most important techniques of quality control to detect if assignable causes exist. The widely used control chart techniques are R X  and S X  . These are called traditional variable control ch...

This paper develops Mean and Range control charts in fuzzy environment using different transformation methods. It is assumed that the observations of each sample are fuzzy random variables, which have triangular membership functions. After calculating fuzzy mean and fuzzy range of each sample using fuzzy arithmetic, their representative values are calculated exploiting the transformation method...

Control charts are tools from the field of statistical process control for visualizing the longitudinal development of quality indicators, and detecting whether the underlying process is changing. They have been used in critical care and disease management settings to monitor and improve patient outcomes. This paper investigates the application of control charts to monitor adherence to clinical...

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 order to monitor a process, quality practitioners frequently use Shewhart control charts (e.g. X –R, P-charts, etc.), so named after the pioneering work of Dr. Walter Shewhart. It can be shown that if there are sharp, intermittent changes to a process, these types of charts are highly effective. However, if one is interested in a small, sustained shift in a process, other types of control ch...

We propose a Shewhart control chart based on gauging theoretically continuous observations into multiple groups. This chart is designed to monitor the process mean and standard deviation for deviations from stability. By assuming an underlying normal distribution, we derive the optimal grouping criteria that maximizes the expected statistical information available in a sample. Control charts ba...

In many circumstances, the quality of a process or product is best characterized by a given mathematical function between a response variable and one or more explanatory variables that is typically referred to as profile. There are some investigations to monitor auto-correlated linear and nonlinear profiles in recent years. In the present paper, we use the linear mixed models to account autocor...

The purpose of this study was to retrospectively evaluate the results from a Helical TomoTherapy Hi-Art treatment system relating to quality controls based on daily static and dynamic output checks using statistical process control methods. Individual value X-charts, exponentially weighted moving average charts, and process capability and acceptability indices were used to monitor the treatment...