Average run length performance approach to determine the best control chart when process data is autocorrelated

Ana Sofia Matos, Rogério Puga-Leal, José Gomes Requeijo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Most conventional Statistical Process Control techniques have been developed under the assumption of the independence of observations. However, due to advances in data sensing and capturing technologies, larger volumes of data are routinely being collected from individual units in manufacturing industries and therefore data autocorrelation phenomena is more likely to occur. Following this changes in manufacturing industries, many researchers have focused on the development of appropriate SPC techniques for autocorrelated data. This paper presents a methodology to be applied when the data exhibit autocorrelation and, in parallel, to evidence the strong capabilities that simulation can provide as a key tool to determine the best control chart to be used, taking into account the process’s dynamic behavior. To illustrate the proposed methodology and the important role of simulation, a numerical example with data collected from a pulp and paper industrial process is provided.Aset of control charts based on the exponentially weighted moving average (EWMA) statistic was studied and the in and out-of-control average run length was chosen as performance criteria.The proposed methodology constitutes a useful tool for selecting the best control chart, taking into account the autocorrelated structure of the collected data.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Management Science and Engineering Management - Focused on Intelligent System and Management Science
PublisherSpringer-Verlag
Pages3-12
Number of pages10
Volume280
ISBN (Electronic)9783642551819
DOIs
Publication statusPublished - 2014
Event8th International Conference on Management Science and Engineering Management, ICMSEM 2014 - Lisbon, Portugal
Duration: 25 Jul 201427 Jul 2014

Publication series

NameAdvances in Intelligent Systems and Computing
Volume280
ISSN (Print)21945357

Conference

Conference8th International Conference on Management Science and Engineering Management, ICMSEM 2014
CountryPortugal
CityLisbon
Period25/07/1427/07/14

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Keywords

  • Autocorrelated data
  • Average run length (ARL)
  • EWMAST chart
  • Exponentially weighted moving average (EWMA)
  • MCEWMA chart
  • Statistical process control (SPC)

Cite this

Matos, A. S., Puga-Leal, R., & Requeijo, J. G. (2014). Average run length performance approach to determine the best control chart when process data is autocorrelated. In Proceedings of the 8th International Conference on Management Science and Engineering Management - Focused on Intelligent System and Management Science (Vol. 280, pp. 3-12). (Advances in Intelligent Systems and Computing; Vol. 280). Springer-Verlag. https://doi.org/10.1007/978-3-642-55182-6_1