JSM 2005 Online Program

Abstract #302894

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	234
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Quality and Productivity
Abstract - #302894
Title:	Multivariate Process Control for Improving Detection and Cause Identification
Author(s):	Amit Mitra*+
Companies:	Auburn University
Address:	College of Business, Auburn, AL, 36849-5240, United States
Keywords:	Process control ; Cause identification ; Control charts ; Average run length
Abstract:	Traditional approaches for monitoring multiple variables through control charts, such as T-squared chart, have a couple of disadvantages. They do not identify which of the causal factors (e.g., process adjustment parameters or uncontrollable noise factors or both) are possible reasons for out-of-control signals. In this paper, we consider better identification of the causal system for out-of-control conditions. The impact of the process adjustment parameters is identified through their leverage, while the effect of the noise factors is determined through an adjusted distance measure that removes the impact of the process adjustment parameters. Performance measures such as the average run length to first detection of out-of-control conditions are used to study the proposed approach.

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