IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v324y2025i2p454-465.html
   My bibliography  Save this article

The use of IoT sensor data to dynamically assess maintenance risk in service contracts

Author

Listed:
  • Loeys, Stijn
  • Boute, Robert N.
  • Antonio, Katrien

Abstract

We explore the value of using operational sensor data to improve the risk assessment of service contracts that cover all maintenance-related costs during a fixed period. An initial estimate of the contract risk is determined by predicting the maintenance costs via a gradient-boosting machine based on the machine’s and contract’s characteristics observable at the onset of the contract period. We then periodically update this risk assessment based on operational sensor data observed throughout the contract period. These sensor data reveal operational machine usage that drives the maintenance risk. We validate our approach on a portfolio of about 4,000 full-service contracts of industrial equipment and show how dynamic sensor data improves risk differentiation.

Suggested Citation

  • Loeys, Stijn & Boute, Robert N. & Antonio, Katrien, 2025. "The use of IoT sensor data to dynamically assess maintenance risk in service contracts," European Journal of Operational Research, Elsevier, vol. 324(2), pages 454-465.
    • Handle: RePEc:eee:ejores:v:324:y:2025:i:2:p:454-465
    DOI: 10.1016/j.ejor.2025.01.041
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221725000840
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2025.01.041?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.

    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:eee:ejores:v:324:y:2025:i:2:p:454-465. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.