IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v42y2010i8p577-588.html
   My bibliography  Save this article

Chart allocation strategy for serial-parallel multistage manufacturing processes

Author

Listed:
  • Ming Jin
  • Yanting Li
  • Fugee Tsung

Abstract

The application of Statistical Process Control (SPC) to multistage manufacturing process has received considerable attention recently. How to effectively allocate conventional SPC charts in a serial multistage environment to monitor the process quality has not been thoroughly studied. This article adopts the approach of a linear state space model to describe multistage processes and proposes a strategy to properly allocate control charts in serial parallel-multistage manufacturing processes by considering the interrelationship information between stages. Based on the proposed chart allocation strategy it proves possible to make rational chart allocation decisions to achieve a quicker detection capability over the whole potential fault set. A hood assembly example is used to demonstrate the applications of the chart allocation strategy. Extensions are also discussed.

Suggested Citation

  • Ming Jin & Yanting Li & Fugee Tsung, 2010. "Chart allocation strategy for serial-parallel multistage manufacturing processes," IISE Transactions, Taylor & Francis Journals, vol. 42(8), pages 577-588.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:8:p:577-588
    DOI: 10.1080/07408170903394330
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07408170903394330
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/07408170903394330?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.

    Citations

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


    Cited by:

    1. Guangzhou Diao & Liping Zhao & Yiyong Yao, 2016. "A weighted-coupled network-based quality control method for improving key features in product manufacturing process," Journal of Intelligent Manufacturing, Springer, vol. 27(3), pages 535-548, June.
    2. Cheng, Guoqing & Li, Ling, 2020. "Joint optimization of production, quality control and maintenance for serial-parallel multistage production systems," Reliability Engineering and System Safety, Elsevier, vol. 204(C).

    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:uiiexx:v:42:y:2010:i:8:p:577-588. 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/uiie .

    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.