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An attribute control chart for monitoring the variability of a process

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  • Lee Ho, Linda
  • Quinino, Roberto Costa

Abstract

Recently, Wu et al. (2009) proposed the use of an npx chart to monitor a process mean by attribute inspection as an alternative to the use of an X¯ chart. Motivated by the simplicity of this control chart and its good performance, the possibility of using a similar chart, an npS2 control chart, to monitor the variability of a process was explored, and the results are described in this paper. A comparison of the proposed npS2 control chart with the S2 and R control charts is presented, and a numerical example is provided to demonstrate the use of the npS2 control chart.

Suggested Citation

  • Lee Ho, Linda & Quinino, Roberto Costa, 2013. "An attribute control chart for monitoring the variability of a process," International Journal of Production Economics, Elsevier, vol. 145(1), pages 263-267.
    • Handle: RePEc:eee:proeco:v:145:y:2013:i:1:p:263-267
    DOI: 10.1016/j.ijpe.2013.04.046
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    References listed on IDEAS

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    1. Wu, Zhang & Khoo, Michael B.C. & Shu, Lianjie & Jiang, Wei, 2009. "An np control chart for monitoring the mean of a variable based on an attribute inspection," International Journal of Production Economics, Elsevier, vol. 121(1), pages 141-147, September.
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    Citations

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    Cited by:

    1. Lim, S.L. & Khoo, Michael B.C. & Teoh, W.L. & Xie, M., 2015. "Optimal designs of the variable sample size and sampling interval X¯ chart when process parameters are estimated," International Journal of Production Economics, Elsevier, vol. 166(C), pages 20-35.
    2. Samrad Jafarian-Namin & Muhammad Aslam & Mohammad Saber Fallah Nezhad & Fatemeh Eskandari-Kataki, 2021. "Efficient designs of modeling attribute control charts for a Weibull distribution under truncated life tests," OPSEARCH, Springer;Operational Research Society of India, vol. 58(4), pages 942-961, December.
    3. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
    4. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
    5. Ho, Linda Lee & Aparisi, Francisco, 2016. "ATTRIVAR: Optimized control charts to monitor process mean with lower operational cost," International Journal of Production Economics, Elsevier, vol. 182(C), pages 472-483.
    6. Simões, Felipe Domingues & Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2020. "The Trinomial ATTRIVAR control chart," International Journal of Production Economics, Elsevier, vol. 224(C).
    7. Muhammad Aslam & Osama Hasan Arif, 2019. "Classification of the State of Manufacturing Process under Indeterminacy," Mathematics, MDPI, vol. 7(9), pages 1-8, September.
    8. Tomohiro, Ryosuke & Arizono, Ikuo & Takemoto, Yasuhiko, 2020. "Economic design of double sampling Cpm control chart for monitoring process capability," International Journal of Production Economics, Elsevier, vol. 221(C).

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