We provide an overview and discussion of some issues and guidelines related to monitoring
univariate processes with control charts. We offer some advice to practitioners to help them …

Classical monitoring schemes are typically designed under the assumption of known
process parameters, perfect measurements and normality. In real-life applications, these …

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

Statistical process monitoring (SPM) literature recommends the combination of Shewhart
and exponentially weighted moving average (EWMA) schemes to improve the ability of the …

Predictive process monitoring aims to produce early warnings of unwanted events. We
consider the use of the machine learning method extreme gradient boosting as the …

The performance of the triple sampling (TS) X¯ ̄X chart is investigated by using a linear
covariate error model that considers the presence of measurement errors (ME), called the …

In statistical process monitoring, the usual assumption when designing monitoring schemes
is that process parameters are known and have perfect measurements with independent …

Fast initial response (FIR) features are generally used to improve the sensitivity of memory‐
type control charts by shrinking time‐varying control limits in the earlier stage of the …

Abstract Machine tools constitute the backbone of the industrial sector, representing the
largest global inventory of equipment. The carbon emissions resulting from the production of …

The side sensitive group runs double sampling (SSGRDS) X¯ scheme is previously studied
under the known process parameters (Case-K) assumption. As the process parameters are …