Year: 2014
Type: article
Abstract: This paper presents original research initiated by the monitoring needs of a semiconductor production plant. The industrial operations rely on an Information Technology (IT) system, and several time series data are controlled statistically. Unfortunately, these variables often contain outliers, as well as structural changes because of external decisions in the IT activity. As a consequence, it has been observed that the monitoring results obtained with standard techniques could be severely biased. This paper attempts to overcome such difficulties. A new monitoring method is proposed, based on robust Holt–Winters smoothing algorithm, and coupled with a relearning procedure for structural break detection. Such a method is flexible enough for a large‐scale industrial application. We evaluate performance through simulation and show its usefulness in real industrial applications for univariate and multivariate time series. The scope of application deals with IT activity monitoring, but the introduced statistical methods are generic enough for being used in other industrial fields. Copyright © 2014 John Wiley & Sons, Ltd.
DOI: 10.1002/qre.1651
Authors: Esperan Padonou, Olivier Roustant, Michel Lutz
Institution: Mines Saint-Étienne
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1588896074