ENBIS: European Network for Business and Industrial Statistics

Interpreting Shewhart Type Control Chart Signals Using Pattern Based Rules and Artificial Intelligence Techniques

Abstract

Submitted by

Athan Kallioras

Authors

V.P. Plagianakos Department of Informatics with Biomedical Applications, University of Central Greece, Papasiopoulou 2-4, Lamia, 35100, Greece. A. Kallioras National Statistical Service of Greece, Kiprou 38, Lamia, 35100, Greece S. Bersimis Department of Informatics & Telematics, Harokopio University, 89, Harokopou Street, 176 71, Kallithea, Greece.

Abstract

Shewhart type control charts are widely used in Phase I for both, making a decision about the status of the process and estimating the parameters of the process. Specifically, Shewhart type control charts indicate whether the process under study is in-control or it exhibits a non-random behavior. Subsequently, if the process is under statistical control, Shewhart type control charts can be used to estimate the process parameters. Alternatively, if the process exhibits a non-random behavior, Shewhart type control charts can be used for identifying which is the exact problem of the process. In this work, two new procedures are proposed that can improve the ability of Shewhart type control charts to identify the exact problem of the process in Phase I. These procedures can be applied to control charts for the mean and control charts for the dispersion of the process. The first method uses pattern based rules while the second one uses computational intelligence techniques. These two procedures are compared against alternative approaches giving very interesting results.

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