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Information 2018, 9(12), 312;

A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean

Department of Mathematics and Statistics, Riphah International University, Islamabad 45210, Pakistan
Department of Statistics, Faculty of Science, King Abdulaziz University, Jeddah 21551, Saudi Arabia
Department of Industrial Engineering, Sharif University of Technology, P.O. Box 11155-9414 Azadi Ave., Tehran 15119-43943, Iran
Author to whom correspondence should be addressed.
Received: 30 September 2018 / Revised: 18 November 2018 / Accepted: 4 December 2018 / Published: 7 December 2018
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Conventional control charts are one of the most important techniques in statistical process control which are used to assess the performance of processes to see whether they are in- or out-of-control. As traditional control charts deal with crisp data, they are not suitable to study unclear, vague, and fuzzy data. In many real-world applications, however, the data to be used in a control charting method are not crisp since they are approximated due to environmental uncertainties and systematic ambiguities involved in the systems under investigation. In these situations, fuzzy numbers and linguistic variables are used to grab such uncertainties. That is why the use of a fuzzy control chart, in which fuzzy data are used, is justified. As an exponentially weighted moving average (EWMA) scheme is usually used to detect small shifts, in this paper a fuzzy EWMA (F-EWMA) control chart is proposed to detect small shifts in the process mean when fuzzy data are available. The application of the newly developed fuzzy control chart is illustrated using real-life data. View Full-Text
Keywords: crisp data; fuzzy data; fuzzy control charts; EWMA charts; fuzzy EWMA charts crisp data; fuzzy data; fuzzy control charts; EWMA charts; fuzzy EWMA charts

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Khan, M.Z.; Khan, M.F.; Aslam, M.; Niaki, S.T.A.; Mughal, A.R. A Fuzzy EWMA Attribute Control Chart to Monitor Process Mean. Information 2018, 9, 312.

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