The scientific literature is abundant regarding control charts in multivariate environments for numerical and mixed data; however, there are few publications qualitative data. Qualitative variables provide valuable information on processes various industrial, productive, technological, health contexts. Social no exception. There multiple nominal ordinal categorical used economics, psychology, l...

Traditionally, the statistical quality control techniques utilize either an attributes or variables product quality measure. Recently, some methods such as three-level control chart have been developed for monitoring multi attribute processes. Control chart usually has three design parameters: the sample size (n), the sampling interval (h) and the control limit coefficient (k).The design parame...

Control limits in traditional Multivariate Quality Control Charts (MQCC), such as Hotelling's T control chart, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially-Weighted Moving Average (MEWMA) control charts are based on multivariate normal assumption. This assumption is not usually satisfied, especially in real life applications. The purpose of a control chart based on suppor...

In some statistical process monitoring applications, quality of a process or product is described by more than one ordinal factors called ordinal multivariate process. To show the relationship between these factors, an ordinal contingency table is used and modeled with ordinal log-linear model. In this paper, a new control charts based on ordinal-normal statistic is developed to monitor the ord...

Control charts are extensively used in manufacturing contexts to monitor production processes. This article illustrates economical design of a variable sample size and control limit Hotelling’s T2 control chart based on a novel cost model when occurrence times of the assignable causes are exponentially distributed. The proposed nonlinear cost model is an extension of Duncan’s (J Am Stat ...

The multivariate exponentially weighted moving average (MEWMA) control chart is one of the best statistical control chart that are usually used to detect simultaneous small deviations on the mean of more than one cross-correlated quality characteristics. The economic design of MEWMA control charts involves solving a combinatorial optimization model that is composed of a nonlinear cost function ...