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On monitoring process variability under double sampling scheme

Author

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  • Ahmad, Shabbir
  • Riaz, Muhammad
  • Abbasi, Saddam Akber
  • Lin, Zhengyan

Abstract

The presence of variation in all manufacturing and measurement processes is a natural phenomenon and is the key factor which affects the performance of all types of processes. A better understanding of the causes of variability in any processes is necessary to improve the process. For an efficient monitoring of process variability, we have suggested a set of variance type control charts based on auxiliary characteristics and evaluated their performances in terms of Average Time to Signal (ATS) (the performance measure at every point of variability shift) and Average Extra Quadratic Loss (AEQL) (the performance measure over the whole process shift range) under normal and gamma process environments. We have also examined the effects of contaminated environments on the ATS performance of different variance based charting structures. Illustrative examples on some selective variance type control structures are also provided for procedural details. Finally we have closed with concluding remarks about this study.

Suggested Citation

  • Ahmad, Shabbir & Riaz, Muhammad & Abbasi, Saddam Akber & Lin, Zhengyan, 2013. "On monitoring process variability under double sampling scheme," International Journal of Production Economics, Elsevier, vol. 142(2), pages 388-400.
    • Handle: RePEc:eee:proeco:v:142:y:2013:i:2:p:388-400
    DOI: 10.1016/j.ijpe.2012.12.015
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    References listed on IDEAS

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    1. J. Carlos Garcia-Diaz, 2007. "The 'effective variance' control chart for monitoring the dispersion process with missing data," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 1(1), pages 40-55.
    2. Ou, Yanjing & Wu, Zhang & Tsung, Fugee, 2012. "A comparison study of effectiveness and robustness of control charts for monitoring process mean," International Journal of Production Economics, Elsevier, vol. 135(1), pages 479-490.
    3. Arthur Yeh & Dennis Lin & Honghong Zhou & Chandramouliswaran Venkataramani, 2003. "A multivariate exponentially weighted moving average control chart for monitoring process variability," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(5), pages 507-536.
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    5. Ou, Yanjing & Wu, Zhang & Goh, Thong Ngee, 2011. "A new SPRT chart for monitoring process mean and variance," International Journal of Production Economics, Elsevier, vol. 132(2), pages 303-314, August.
    6. Muhammad Riaz & Ronald Does, 2009. "A process variability control chart," Computational Statistics, Springer, vol. 24(2), pages 345-368, May.
    7. Zhang, Jiujun & Li, Zhonghua & Wang, Zhaojun, 2010. "A multivariate control chart for simultaneously monitoring process mean and variability," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2244-2252, October.
    8. Machado, Marcela A.G. & Costa, Antonio F.B., 2008. "The double sampling and the EWMA charts based on the sample variances," International Journal of Production Economics, Elsevier, vol. 114(1), pages 134-148, July.
    9. Costa, Antonio Fernando Branco & Machado, Marcela Aparecida Guerreiro, 2011. "Variable parameter and double sampling charts in the presence of correlation: The Markov chain approach," International Journal of Production Economics, Elsevier, vol. 130(2), pages 224-229, April.
    10. Muhammad Riaz, 2008. "Monitoring process variability using auxiliary information," Computational Statistics, Springer, vol. 23(2), pages 253-276, April.
    11. Zhang Wu & Jianxin Jiao & Mei Yang & Ying Liu & Zhaojun Wang, 2009. "An enhanced adaptive CUSUM control chart," IISE Transactions, Taylor & Francis Journals, vol. 41(7), pages 642-653.
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    Cited by:

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    2. Taejong Joo & Minji Seo & Dongmin Shin, 2019. "An adaptive approach for determining batch sizes using the hidden Markov model," Journal of Intelligent Manufacturing, Springer, vol. 30(2), pages 917-932, February.
    3. Iziy Azamsadat & Sadeghpour Gildeh Bahram & Monabbati Ehsan, 2017. "Comparison Between the Economic-Statistical Design of Double and Triple Sampling X¯\bar{X} Control Charts," Stochastics and Quality Control, De Gruyter, vol. 32(1), pages 49-61, June.
    4. Guoyi Zhang, 2014. "Improved R and s control charts for monitoring the process variance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1260-1273, June.
    5. Khoo, Michael B.C. & Teoh, W.L. & Castagliola, Philippe & Lee, M.H., 2013. "Optimal designs of the double sampling X¯ chart with estimated parameters," International Journal of Production Economics, Elsevier, vol. 144(1), pages 345-357.

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