IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v32y1986i9p1164-1176.html
   My bibliography  Save this article

Incorporating Learning Curve Analysis into Medium-Term Capacity Planning Procedures: A Simulation Experiment

Author

Listed:
  • Timothy L. Smunt

    (Graduate School of Business Administration, Washington University, St. Louis, Missouri 63130)

Abstract

The purpose of this paper is to determine the type of company which can benefit from incorporating learning curve analysis into its medium-term capacity planning procedures and the effect of information aggregation on learning curve capacity projections. The research into these two issues uses a simulation study that describes the relevant production and information characteristics. Four production characteristic variables are investigated: (1) average learning rate, (2) run-time variance, (3) learning rate mix, and (4) run-time distribution. A fifth variable, level of data aggregation, is an information characteristic and is also investigated. The results of this study indicate that there are significant increases possible in the accuracy of medium-term capacity projections from the incorporation of learning curve analysis in firms with high rates of learning or with low noise levels in their data. However, when a company has either low to moderate rates of learning or moderate noise, a standard analysis, which uses the sample mean of the last planning horizon's data, provides projections approximately as accurate as a learning curve estimate.

Suggested Citation

  • Timothy L. Smunt, 1986. "Incorporating Learning Curve Analysis into Medium-Term Capacity Planning Procedures: A Simulation Experiment," Management Science, INFORMS, vol. 32(9), pages 1164-1176, September.
    • Handle: RePEc:inm:ormnsc:v:32:y:1986:i:9:p:1164-1176
    DOI: 10.1287/mnsc.32.9.1164
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.32.9.1164
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.32.9.1164?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. Wang, Weijia & Plante, Robert D. & Tang, Jen, 2013. "Minimum cost allocation of quality improvement targets under supplier process disruption," European Journal of Operational Research, Elsevier, vol. 228(2), pages 388-396.
    2. Gerchak, Yigal & Golany, Boaz, 2000. "Hiring policies in an uncertain environment: Cost and productivity trade-offs," European Journal of Operational Research, Elsevier, vol. 125(1), pages 195-204, 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:32:y:1986:i:9:p:1164-1176. 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.