Multivariate SPC methods for controlling manufacturing processes using predictive models – A case study in the automotive sector

Abstract

The main objective is to develop and test a novel SPC method to ensure the stability of final customer characteristics by controlling the upstream characteristics of manufacturing processes, considering their importance/contribution. The originality of the proposed method lies in the use of predictive models (multivariate regression), whose coefficients are used to weigh the contribution of each upstream characteristic to predict the final characteristics. In the context of continuous improvement environments, the application of different SPC approaches to manufacturing processes were compared. The results showed that the multivariate SPC method based on partial least squares regression was superior to traditional univariate and multivariate SPC methods in terms of the predictive precision to detect downstream faults in customer characteristics. However, the use of multiple linear regression may also be an option, since the identification of what upstream characteristic is causing the out-of-control signal is simpler than that of partial least squares regression, and predictive precision in the case of each of the two methods is comparable in practical terms.