P. Soleimani - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
R. Noorossana - Industrial Engineering Department, Iran University of Science and Technology, Tehran, Iran

Profile monitoring in statistical quality control has attracted attention of many researchers recently. A profile is a function between response variables and one or more independent variables. There have been only a limited number of researches on monitoring multivariate linear profiles. Indeed, monitoring correlated multivariate profiles is a new subject in the filled of statistical process control. In this paper, we investigate the effect of autocorrelations in monitoring multivariate linear profiles in phase II. The effect of three main models namely AR(1), MA(1), and ARMA(1,1) on the methods of multivariate linear profile monitoring is evaluated and compared by using simulation study and average run length criteria. Results indicate that autocorrelation affects performance of the existing methods significantly.

Multivariate linear profiles, Autocorrelation, Time series modeling, Average Run Length