Statistical process control by model Bayesian

Abstract

Currently considerable attention has been given to the effect of data correlation on statistical process control (SPC). Use of traditional SPC methods when observations are correlated often leads to misleading conclusions as to whether or not the process is under control. The objetive of this paper is to develop an algorithm to adjust a Dynamic Linear Model, to calculate the run length distribution (RLD), the average run length (ARL), standard deviation of the run length (SRL), for residual control charts X and R. The algorithm is applied to data collected from a textile company. The results showed that the process had been out of control needing systematic monitoring, with the objective of improving the quality of the products.