Fuzzy exponentially weighted moving average control chart for univariate data with a real case application

Sevil Şentürk; Nihal Erginel; Ihsan Kaya; Cengiz Kahraman

doi:10.1016/j.asoc.2014.04.022

Fuzzy exponentially weighted moving average control chart for univariate data with a real case application

Sevil Şentürk, Nihal Erginel, Ihsan Kaya^*, Cengiz Kahraman

^*Corresponding author for this work

Department of Industrial Engineering

Research output: Contribution to journal › Article › peer-review

60 Citations (Scopus)

Abstract

Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular control charts, exponentially weighted moving average control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.

Original language	English
Pages (from-to)	1-10
Number of pages	10
Journal	Applied Soft Computing
Volume	22
DOIs	https://doi.org/10.1016/j.asoc.2014.04.022
Publication status	Published - Sept 2014

Keywords

EWMA
Fuzzy control charts
Fuzzy EWMA
Statistical process control

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10.1016/j.asoc.2014.04.022

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abstract = "Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular control charts, exponentially weighted moving average control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.",

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AU - Kaya, Ihsan

AU - Kahraman, Cengiz

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AB - Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popular control charts, exponentially weighted moving average control chart (EWMA) for univariate data are developed under fuzzy environment. The fuzzy EWMA control charts (FEWMA) can be used for detecting small shifts in the data represented by fuzzy numbers. FEWMA decreases number of false decisions by providing flexibility on the control limits. The production process of plastic buttons is monitored with FEWMA in Turkey as a real application.

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Fuzzy exponentially weighted moving average control chart for univariate data with a real case application

Abstract

Keywords

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