Application of Multivariate Process Control (MPC) in the Natural Rubber Industry

Abstract

Statistical process control (SPC) monitors specified quality characteristics of a product orservice to detect whether the process has changed in a way that would affect product qualityand to measure the current quality of products or services. This study introduces severalapplications on multivariate process control in the rubber industry employing the factory datain the Dartonfield estate of the Rubber Research Institute of Sri Lanka (RRI). Multivariateprocess control was applied to consider two or more variables simultaneously. In this studymultivariate techniques were done for the application of bleaching agents, sodium bisulphateand acids in the process of crepe rubber production.

Multivariable scatter diagrams and control ellipse has been used to show the process whenthe multivariate data composed of two variables. Hotelling T2 control chart was used tomonitor application of chemicals simultaneously in a single chart. Principal components wereused to interpret dimensions of components and star charts were applied to diagnostic fordetermining the relative contributions of the each chemical component. The multivariateEWMA (MEWMA) control chart for monitoring the process mean vector was astraightforward extension of the univariate EWMA chart. The graphical presentation is veryimportant and it is a quick way to see the advantages of MPC.

Multivariate Statistical analysis is concerned with data that consist of sets of measurementson a number of variables. So, multivariate quality control provides a way for monitoring andevaluation of chemical application process. Scatter plots and control ellipse is very importantto detect out of control points and relationships as a graphical method. Hotelling T2 is animportant chart that can be decomposed into an overall measure of distance of the groupmeans from the target T2 and measure of variability. Principal components can be useful formultivariate quality control, especially to observe variation of dimensions of chemicalapplications. Multivariate EWMA procedure use additional information from recent historyof the process and it is more sensitive in monitoring chemical application process.

Therefore multivariate process control provides way for manufacturer to test their products inan environment that provide many advantages over univariate models.