Statistical process control, a recognized technique for improving quality and productivity, has been widely employed throughout various industries. The conventional Shewhart control charts are applicable only when the collected sample data are real-valued data. For the purpose of controlling uncertain information when interval-valued data inevitably appear in the manufacturing or service proces...

The quality characteristic(s) are assumed to follow the normal distribution in many control chart constructions, although this assumption may not hold some instances. This study proposes Bayesian-I and Bayesian-II Shewhart-type charts for monitoring Maxwell scale parameter phase II study. posterior predictive distributions used construct limits proposed charts, respectively. Various performance...

An effective control scheme can be instrumental in increasing productivity and reducing cost. While facing an outlier-existing process, using the mean ( X ) control chart and the range (R) control chart for monitoring the process mean and variance will lead to high level false alarms. Recently, some median ( X~ ) control charts, such as the X~ Shewhart control chart, the exponentially weighted ...

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...

Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart...

In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric S...

Numerous papers have been written to show which combinations of Shewhart-type quality-control charts are optimal for detecting systematic shifts in the mean response of a process, increases in the random error of a process, and linear drift effects in the mean response across the assay batch. One paper by Westgard et al. (Clin Chem 1977;23:1857-67) especially seems to have attracted the attenti...

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...

While the assumption of normality is required for the validity ofmost of the available control charts for jointmonitoring of unknown location and scale parameters, we propose and study a distribution-free Shewhart-type chart based on the Cucconi statistic, called the Shewhart-Cucconi (SC) chart. We also propose a follow-up diagnostic procedure useful to determine the type of shift the process m...