Title: Time series analysis of process data

Authors: John K. Mahaney, Jr., David Lee Baker, James H. Hamburg, David E. Booth

Addresses: Metallurgical Consultants, Inc., 737 Hampton Ridge Drive, Akron, OH 44313-5082, USA. ' Department of Management and Information Systems, College of Business Administration, Kent State University, P.O. Box 5190, Kent, OH 44242-0001, USA. ' Department of Management, College of Business Administration, Walsh University, 2020 East Maple Street, Northwest, Canton, OH 44720, USA. ' Department of Management and Information Systems, College of Business Administration, Kent State University, P.O. Box 5190, Kent, OH 44242-0001, USA

Abstract: Statistical Process Control (SPC) is an integral component of almost every industrial process, and proper outlier (i.e., out of control) detection is crucial if processes are to remain in statistical control. The goal of this research is to determine whether a simple model may be useful as an approximation to a more exact and thus more difficult model; and still provide sufficient accuracy in outlier detection. We test an ARMA (1,1) model with the Chen and Liu (1993) Joint Estimation (JE) outlier detection algorithm with different sets of process data. We find that this approach is quite useful, especially for practitioners.

Keywords: ARMA (1,1); control charting; joint estimation analysis; JE; outlier detection; process data; statistical process control; SPC; time series analysis; process data; operational research.

DOI: 10.1504/IJOR.2007.012851

International Journal of Operational Research, 2007 Vol.2 No.3, pp.231 - 253

Published online: 19 Mar 2007 *

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