Abstract
In many industrial manufacturing processes, the quality of a process or product is represented by a relationship between the response variable and one or more explanatory variables. We call this relationship profile process. Control chart is the most widely used to monitor profile process in the statistical process control (SPC). In this article, we are going to investigate strengths and weakness of Zou, Tsung and Wang (2007) MEWMA chart which is used to monitor general linear profiles. Then provide a method to solve the practical shortcomings. First, we compare the performances between this chart and other charts which are used to monitor linear profiles. Then we want to know how many in-control phase I datasets can make true in-control average run length (ARL0) achieve nominal ARL0. In general case, usually can’t collected a lot of phase I datasets. So we adjust control limit by bootstrap, which can guarantee that use the adjusted control limit have a fixed probability let true ARL0 more than nominal ARL0. At the end, a real example is used to illustrate how to use adjusted and unadjusted control limit for monitoring linear profiles.