Monitoring Process Performance using Multivariate Control Chart

Abstract

Statistical Process Control (SPC) is the most important procedure and decision making method which allows us to see when a process is working properly and when it is not. Walter A. Shewhart developed the concept of control chart in 1920’s, which provides a simple way to determine if the process is in control or not. Variation is present in any process, deciding when the variation is natural and when it needs correction is the key to quality control. Traditional SPC methodologies are not suitable to monitor and control multiple variables while one variable is correlated with other variables. Further, univariate control charts are difficult to manage and analyze because of the large numbers of control charts of each process variable. Multivariate analyses utilize the additional information due to the relationships among the variables and these concepts may be used to develop more efficient control charts than the simultaneous operation of several univariate control charts. Multivariate control chart for process mean is based upon Hotelling's T² distribution, which was introduced by Hotelling (1947). Multivariate surveillance is of interest in industrial production, for example, in order to monitor several sources of variation in assembled products. This paper studies the application of Multivariate Statistical Process Control (MSPC) charts to monitor yarn quality monitoring production process in a textile industry.

Keywords: Statistical process control, control chart, Shewhart control chart, multivariate control chart

Cite this Article

Sasikumar R, Bangusha Devi S. Monitoring Process Performance Using Multivariate Control Chart. Research & Reviews: Journal of Statistics. 2018; 7(1): 53s–56sp.