Abstract
The dispersion control charts monitor the variability of a process that may increase or decrease. An increase in dispersion parameter implies deterioration in the process for an assignable cause, while a decrease in dispersion indicates an improvement in the process. Multivariate variability control charts are used to monitor the shifts in the process variance-covariance matrix. Although multivariate EWMA and CUSUM dispersion control charts are designed to detect the small amount of change in the covariance matrix but to gain more efficiency, we have developed a Mixed Multivariate EWMA-CUSUM (MMECD) chart. The proposed MMECD chart is compared with its existing counterparts by using some important performance run length-based properties such as ARL, SDRL, EQL, SEQL, and different quantile of run length distribution. A real application related to carbon fiber tubing process is presented for practical considerations.
Highlights
Control charts are widely used to detect changes in a process location and/or dispersion parameter
The most common examples include Cumulative sum (CUSUM) control chart proposed by Page [2] and Exponentially Weighted Moving Average (EWMA) control chart by Roberts [3]
The measures covered in this study include average run length (ARL), standard deviation run length (SDRL), median run length (MDRL), extra quadratic loss (EQL), and sequential extra quadratic loss (SEQL), and some useful percentiles/quantiles (Qis) of the run length distribution
Summary
Control charts are widely used to detect changes in a process location and/or dispersion parameter. Pignatiello Jr and Runger [5] and Crosier [6] proposed memory type multivariate control charts They offered Multivariate CUSUM (MCUSUM) control charts that monitor the mean vector. Alt [8] proposed a multivariate control chart that monitored the variance-covariance matrix and named it as generalized variance chart. Djauhari et al [9] introduced vector variance control chart, which can be employed when the variance-covariance matrix is singular This chart monitors both rational subgroups and individual observations. The rest of this study is organized as: Section II presents the information of the existing multivariate control charts for monitoring the process variance-covariance matrix, along with the newly proposed control chart. The design structures of these charts will be given, and it will be discussed how the process is declared in-control (IC) or out-of-control (OOC)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
