Mohsen Mohamadi - Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
Mehdi oumani - School of Applied Sciences and Engineering, Monash University, Gippsland Campus, Churchill, VIC ۳۸۴۲, Australia
Babak Abbasi - School of Mathematical and Geospatial Sciences, RMIT University, Melbourne, Australia

The classical method of process capability analysis necessarily assumes that collected data are independent; nonetheless, some processessuch as biological and chemical processes are autocorrelated and violate the independency assumption. Many processes exhibit a certaindegree of correlation and can be treated by autoregressive models among which the autoregressive model of order one (AR (1)) is the mostcommonly used one. In this paper, we discuss the effect of autocorrelation on the process capability analysis when a set of observations areproduced by an autoregressive model of order one. We employ the multivariate regression model to modify the process capabilityestimated from the classical method where AR (1) parameters are utilized as regression explanatory variables. Finally, the performance ofthe method developed in this paper is investigated using a Monte Carlo simulation

Process capability analysis; Statistical process control; Autocorrelation; AR (1)