IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v43y1997i7p1017-1028.html
   My bibliography  Save this article

Using On-Line Sensors in Statistical Process Control

Author

Listed:
  • Linguo Gong

    (Department of Information Systems and Decision Sciences, Louisiana State University, Baton Rouge, Louisiana 70803)

  • Wushong Jwo

    (Department of Business Education, National Chunghwa University of Education, Chunghwa, Taiwan)

  • Kwei Tang

    (Department of Information Systems and Decision Sciences, Louisiana State University, Baton Rouge, Louisiana 70803)

Abstract

As manufacturing technology moves toward more computerized automation, statistical process control (SPC) techniques must adapt to keep pace with the new environment and take advantage of the development in automated on-line sensors. In this paper, a two-phase procedure is proposed for combining an on-line sensor and a control chart to improve statistical process control decisions. In phase 1 of this procedure, a production process is monitored continually by a sensor. When a sensor warning signal is observed, phase 2 takes place: A sample of items is drawn from the process and inspected. If the sample mean is outside the predetermined control limits, the process is stopped, and a search is initiated to determine the actual process status for possible necessary adjustment. If the sample mean is within the control limits, the process continues. A mathematical model is formulated for jointly determining the sample size and the control limit of the control chart and a decision rule for sending out sensor warning signals. The model is based on the assumption that there is only a weak relationship between the sensor measurement and the process condition. A solution algorithm based on a numerical search is developed. A numerical example is used to show the advantage of the proposed model over the models based separately on the sensor and the control chart, and a sensitivity analysis is used to show the effects of several important model parameters on the optimal solution.

Suggested Citation

  • Linguo Gong & Wushong Jwo & Kwei Tang, 1997. "Using On-Line Sensors in Statistical Process Control," Management Science, INFORMS, vol. 43(7), pages 1017-1028, July.
    • Handle: RePEc:inm:ormnsc:v:43:y:1997:i:7:p:1017-1028
    DOI: 10.1287/mnsc.43.7.1017
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.43.7.1017
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.43.7.1017?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
    ---><---

    Citations

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


    Cited by:

    1. Zhenlin Yang & Min Xie, 2000. "Process monitoring of exponentially distributed characteristics through an optimal normalizing transformation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(8), pages 1051-1063.
    2. 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).
    3. Xiao, Xiao & Jiang, Wei & Luo, Jianwen, 2019. "Combining process and product information for quality improvement," International Journal of Production Economics, Elsevier, vol. 207(C), pages 130-143.
    4. Haimonti Dutta, 2022. "A Consensus Algorithm for Linear Support Vector Machines," Management Science, INFORMS, vol. 68(5), pages 3703-3725, May.
    5. Tang, Kwei & Gong, Linguo & Chang, Dong-Shang, 2003. "Optimal process control policies under a time-varying cost structure," European Journal of Operational Research, Elsevier, vol. 149(1), pages 197-210, August.

    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:inm:ormnsc:v:43:y:1997:i:7:p:1017-1028. 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 Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.