IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v53y2015i7p2179-2185.html
   My bibliography  Save this article

A conjugate Bayesian approach to control chart for multi-batch and low volume production

Author

Listed:
  • Xiaosong Wu
  • Rui Miao
  • Xinyi Zhang
  • Zhibin Jiang
  • Xuening Chu

Abstract

Control chart could effectively reflect whether a manufacturing process is currently under control or not. The calculation of control limits of the control chart has been focusing on traditional frequency approach, which requires a large sample size for an accurate estimation. A conjugate Bayesian approach is introduced to correct the calculation error of control limits with traditional frequency approach in multi-batch and low volume production. Bartlett’s test, analysis of variance test and standardisation treatment are used to construct a proper prior distribution in order to calculate the Bayes estimators of process distribution parameters for the control limits. The case study indicates that this conjugate Bayesian approach presents better performance than the traditional frequency approach when the sample size is small.

Suggested Citation

  • Xiaosong Wu & Rui Miao & Xinyi Zhang & Zhibin Jiang & Xuening Chu, 2015. "A conjugate Bayesian approach to control chart for multi-batch and low volume production," International Journal of Production Research, Taylor & Francis Journals, vol. 53(7), pages 2179-2185, April.
  • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:7:p:2179-2185
    DOI: 10.1080/00207543.2014.975857
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2014.975857
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2014.975857?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:tprsxx:v:53:y:2015:i:7:p:2179-2185. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    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.