On SPC for Short Run Autocorrelated Data

Abstract

ABSTRACT The existence of a large amount of historical data set and the assumption that process observations are independent and identically distributed are two necessary conditions to effectively and efficiently implement traditional control charts. In many manufacturing environments there is neither enough observations nor the data are i.i.d. In this article we show that the use of Q statistics in conjunction with residuals control charts is an appropriate Statistical Process Control (SPC) tool for short run autocorrelated data. A performance analysis is conducted to compare he proposed method to traditional residuals control charts. Results indicate that residual control charts provide much better shift detection properties than charts based on Q statistics. However, as the under control data set increases, the superiority of residual charts decreases. Key Words: ARLAutocorrelationControl chartLow volume dataQ statisticsMathematics Subject Classification: 62-XX62P30