IDEAS home Printed from https://ideas.repec.org/a/taf/conmgt/v18y2000i1p65-75.html
   My bibliography  Save this article

A model for predicting plant maintenance costs

Author

Listed:
  • David Edwards
  • Gary Holt
  • Frank Harris

Abstract

A model is presented that predicts the total cost of plant maintenance (i.e. direct cost of maintenance plus indirect cost of lost production) and is derived studying a random sample of tracked hydraulic excavators. Analysis is based on the machine history file data of 33 plant items, modelled using multiple regression (MR) analysis. Validation of the model was determined via the combination of an observed high R 2 at 0.94 and various statistical tests which confirmed the prerequisites of a rigorous MR analysis. Machine weight, type of industry and company attitude towards predictive maintenance were found to be the best predictor variables of total plant maintenance cost. The paper also discusses reasons underlying the inclusion of predictor variables in the final model, and concludes with clear directions for future research in this field.

Suggested Citation

  • David Edwards & Gary Holt & Frank Harris, 2000. "A model for predicting plant maintenance costs," Construction Management and Economics, Taylor & Francis Journals, vol. 18(1), pages 65-75.
    • Handle: RePEc:taf:conmgt:v:18:y:2000:i:1:p:65-75
    DOI: 10.1080/014461900370960
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/014461900370960
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/014461900370960?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. Pascual, R. & Meruane, V. & Rey, P.A., 2008. "On the effect of downtime costs and budget constraint on preventive and replacement policies," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 144-151.
    2. Xiao Wang & Hongwei Wang & Chao Qi, 2016. "Multi-agent reinforcement learning based maintenance policy for a resource constrained flow line system," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 325-333, 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:taf:conmgt:v:18:y:2000:i:1:p:65-75. 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/RCME20 .

    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.