However, to detect variations with magnitudes smaller or equivalent to one standard deviation in the vector of averages with greater sensitivity than the [PERSON] T\({}^{2}\) chart, multivariate cumulative sum (MCUSUM) and exponentially weighted moving average (MEWMA) control charts have been proposed, both control charts incorporate information from previous periods of the process allowing the increase in the detection capability of the chart in the face of variations in the process [7]. It should be noted that one of the disadvantages of multivariate statistical control charts is the lack of a mechanism focused on identifying the source of variation that generates a signal out of statistical control in the chart [8]. The negative consequence caused by the lack of interpretation of the warning signals produced in multivariate control charts is the loss of time and resources invested to locate the source of variation within the production system, resulting in a loss of quality and competitiveness in the organization. To facilitate the interpretation of out-of-control signals in multivariate charts or to increase their sensitivity to variability in manufacturing processes, several proposals have been made in the literature to complement the practical use of the charts; see Table 1. \begin{table}
\begin{tabular}{p{28.5 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Year** & **Methods** & **Contribution** & **Limitations** \\ \hline [PERSON] et al. [9] & 2020 & \(\bullet\) & Multivariate control charts, MCUSUM and MEWMA & The advantages of multivariate memory charts for identifying small magnitude changes in the vector of averages within a manufacturing process are demonstrated. In addition, control charts with mixed characteristics are proposed. & The proposal does not contemplate an interpretation of the signal outside statistical control. \\ \hline [PERSON] et al. [10] & 2020 & \(\bullet\) & Mahalanobis distance Support vector machines & A quality control approach for multivariate processes based on the prediction between variables of the manufacturing system is proposed. & A comparison of performance between different mechanisms for multivariate control was not performed. \\ \hline [PERSON] and [PERSON] [11] & 2021 & \(\bullet\) & MCUSUM, MEWMA and Hotelling’s T\({}^{2}\) multivariate control charts & The research proposes to monitor the quality using an SVM model to break the autocorrelation of the data and subsequently plot the residuals on MCUSUM, MEWMA, and Hotelling’s T\({}^{2}\) control charts. & The proposal does not contemplate an interpretation of the signal out of statistical control in the multivariate chart. \\ \hline [PERSON] and [PERSON] [12] & 2022 & \(\bullet\) & Multivariate control charts, MEWMA, MCUSUM, PEWMA, and MRA & The research shows how to quickly detect any changes in manufacturing processes under a multivariate statistical control between events. & The source of variation is not located, only the signal out of statistical control is detected in the multivariate chart. \\ \hline [PERSON] et al. [13] & 2022 & \(\bullet\) & Univariate control chart, EWMA Support vector machines (SVMs). & Establishes statistical control through pattern recognition on the EWMA control chart and support vector machine. & The approach used for process monitoring is pattern recognition in univariate control charts; correlation between multiple variables is not studied. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review. As can be seen in the literature review, the most used multivariate control charts are the MCUSUM and MEWMA for their sensitivity in detecting small changes in the process. As for a method that analyzes out-of-control signals in univariate charts or pattern recognition, artificial neural networks such as the multilayer perceptron and support vector machine have been used for their generalization capacity.