In recent years, there has been a considerable focus on developing effective methods for
monitoring health care processes. Utilizing Statistical Process Monitoring (SPM) …

Memory control chart such as multivariate CUSUM (MCUSUM) and multivariate EWMA
(MEWMA) control charts are considered superior for the detection of small-to-moderate …

In the context of big data-driven smart manufacturing, data is often characterized by high
dimensionality, numerous variables, and complex associations. As a result, mixed …

Improvements in measuring devices, the development of sensing technologies, automated
record-keeping and cloud storage facilities have offered us a large volume of data streams …

In recent years, adaptive control charts in which the design parameters depend on the
observed samples have been successfully used as efficient alternatives for traditional …

Exponentially distributed data are commonly encountered in high‐quality processes. Control
charts dedicated to the univariate exponential distribution have been extensively studied by …

The special causes of variations, which is also known as a shift, can occur in a single or
more than one related process characteristics. Statistical process control tools such as …

Multivariate statistical process control (MSPC) addresses the concurrent monitoring of
several measurements. Since multivariate normality is rare in practice, nonparametric …

Control charts play a significant role to monitor the performance of a process. Nonparametric
control charts are helpful when the probability model of the process output is not known. In …

Nonparametric control charts (NPCC) have shown great potential for monitoring processes
in conditions of smart manufacturing with complex structures, various monitored …