IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v54y2013i2p523-539.html
   My bibliography  Save this article

Using p values to design statistical process control charts

Author

Listed:
  • Zhonghua Li
  • Peihua Qiu
  • Snigdhansu Chatterjee
  • Zhaojun Wang

Abstract

Conventional Phase II statistical process control (SPC) charts are designed using control limits; a chart gives a signal of process distributional shift when its charting statistic exceeds a properly chosen control limit. To do so, we only know whether a chart is out-of-control at a given time. It is therefore not informative enough about the likelihood of a potential distributional shift. In this paper, we suggest designing the SPC charts using p values. By this approach, at each time point of Phase II process monitoring, the p value of the observed charting statistic is computed, under the assumption that the process is in-control. If the p value is less than a pre-specified significance level, then a signal of distributional shift is delivered. This p value approach has several benefits, compared to the conventional design using control limits. First, after a signal of distributional shift is delivered, we could know how strong the signal is. Second, even when the p value at a given time point is larger than the significance level, it still provides us useful information about how stable the process performs at that time point. The second benefit is especially useful when we adopt a variable sampling scheme, by which the sampling time can be longer when we have more evidence that the process runs stably, supported by a larger p value. To demonstrate the p value approach, we consider univariate process monitoring by cumulative sum control charts in various cases. Copyright Springer-Verlag 2013

Suggested Citation

  • Zhonghua Li & Peihua Qiu & Snigdhansu Chatterjee & Zhaojun Wang, 2013. "Using p values to design statistical process control charts," Statistical Papers, Springer, vol. 54(2), pages 523-539, May.
  • Handle: RePEc:spr:stpapr:v:54:y:2013:i:2:p:523-539
    DOI: 10.1007/s00362-012-0447-0
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-012-0447-0
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00362-012-0447-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Luo, Yunzhao & Li, Zhonghua & Wang, Zhaojun, 2009. "Adaptive CUSUM control chart with variable sampling intervals," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2693-2701, May.
    2. Chunguang Zhou & Changliang Zou & Yujuan Zhang & Zhaojun Wang, 2009. "Nonparametric control chart based on change-point model," Statistical Papers, Springer, vol. 50(1), pages 13-28, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ohunakin, Olayinka S. & Adaramola, Muyiwa S. & Oyewola, Olanrewaju. M. & Fagbenle, Richard O., 2014. "Solar energy applications and development in Nigeria: Drivers and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 294-301.
    2. Al-Kayiem, Hussain H. & Aja, Ogboo Chikere, 2016. "Historic and recent progress in solar chimney power plant enhancing technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1269-1292.
    3. Chasnyk, O. & Sołowski, G. & Shkarupa, O., 2015. "Historical, technical and economic aspects of biogas development: Case of Poland and Ukraine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 227-239.
    4. Shengjin Gan & Su-Fen Yang & Li-Pang Chen, 2023. "A New EWMA Control Chart for Monitoring Multinomial Proportions," Sustainability, MDPI, vol. 15(15), pages 1-19, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Qin & Luo, Yunzhao & Wang, Zhaojun, 2010. "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1634-1645, June.
    2. Baocai Guo & Bing Xing Wang, 2018. "Control charts for the coefficient of variation," Statistical Papers, Springer, vol. 59(3), pages 933-955, September.
    3. 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.
    4. Guanfu Liu & Xiaolong Pu & Lei Wang & Dongdong Xiang, 2015. "CUSUM chart for detecting range shifts when monotonicity of likelihood ratio is invalid," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(8), pages 1635-1644, August.
    5. Changliang Zou & Zhaojun Wang & Fugee Tsung, 2012. "A spatial rank‐based multivariate EWMA control chart," Naval Research Logistics (NRL), John Wiley & Sons, vol. 59(2), pages 91-110, March.
    6. Zhang, Min & Nie, Guohua & He, Zhen, 2014. "Performance of cumulative count of conforming chart of variable sampling intervals with estimated control limits," International Journal of Production Economics, Elsevier, vol. 150(C), pages 114-124.
    7. Lee, Pei-Hsi, 2011. "Adaptive R charts with variable parameters," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 2003-2010, May.
    8. Muhammad Riaz & Babar Zaman & Ishaq Adeyanju Raji & M. Hafidz Omar & Rashid Mehmood & Nasir Abbas, 2022. "An Adaptive EWMA Control Chart Based on Principal Component Method to Monitor Process Mean Vector," Mathematics, MDPI, vol. 10(12), pages 1-27, June.
    9. Pei-Hsi Lee & Yi-Hsien Huang & Tsen-I Kuo & Ching-Cheng Wang, 2013. "The effect of the individual chart with variable control limits on the river pollution monitoring," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(4), pages 1803-1812, June.
    10. Gordon Ross & Dimitris Tasoulis & Niall Adams, 2013. "Sequential monitoring of a Bernoulli sequence when the pre-change parameter is unknown," Computational Statistics, Springer, vol. 28(2), pages 463-479, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stpapr:v:54:y:2013:i:2:p:523-539. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.