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

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  • 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
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    References listed on IDEAS

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    1. Roel Verbelen & Katrien Antonio & Gerda Claeskens, 2018. "Unravelling the predictive power of telematics data in car insurance pricing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(5), pages 1275-1304, November.
    2. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2023. "Empirical risk assessment of maintenance costs under full-service contracts," European Journal of Operational Research, Elsevier, vol. 304(2), pages 476-493.
    3. Deprez, Laurens & Antonio, Katrien & Boute, Robert, 2021. "Pricing service maintenance contracts using predictive analytics," European Journal of Operational Research, Elsevier, vol. 290(2), pages 530-545.
    4. Collin Drent & Melvin Drent & Joachim Arts & Stella Kapodistria, 2023. "Real-Time Integrated Learning and Decision Making for Cumulative Shock Degradation," Manufacturing & Service Operations Management, INFORMS, vol. 25(1), pages 235-253, January.
    5. Vanderschueren, Toon & Boute, Robert & Verdonck, Tim & Baesens, Bart & Verbeke, Wouter, 2023. "Optimizing the preventive maintenance frequency with causal machine learning," International Journal of Production Economics, Elsevier, vol. 258(C).
    6. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
    7. Roel Henckaerts & Marie-Pier Côté & Katrien Antonio & Roel Verbelen, 2021. "Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods," North American Actuarial Journal, Taylor & Francis Journals, vol. 25(2), pages 255-285, April.
    8. Gilardoni, Gustavo L. & de Toledo, Maria Luiza Guerra & Freitas, Marta A. & Colosimo, Enrico A., 2016. "Dynamics of an optimal maintenance policy for imperfect repair models," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1104-1112.
    9. Henckaerts, Roel & Antonio, Katrien, 2022. "The added value of dynamically updating motor insurance prices with telematics collected driving behavior data," Insurance: Mathematics and Economics, Elsevier, vol. 105(C), pages 79-95.
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