Pricing service maintenance contracts using predictive analytics

Laurens Deprez*, Katrien Antonio, Robert Boute

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


As more manufacturers shift their focus from selling products to end solutions, full-service maintenance contracts gain traction in the business world. These contracts cover all maintenance related costs during a predetermined horizon in exchange for a fixed service fee and relieve customers from uncertain maintenance costs. To guarantee profitability, the service fees should at least cover the expected costs during the contract horizon. As these expected costs may depend on several machine-dependent characteristics, e.g. operational environment, the service fees should also be differentiated based on these characteristics. If not, customers that are less prone to high maintenance costs will not buy into or renege on the contract. The latter can lead to adverse selection and leave the service provider with a maintenance-heavy portfolio, which may be detrimental to the profitability of the service contracts. We contribute to the literature with a data-driven tariff plan based on the calibration of predictive models that take into account the different machine profiles. This conveys to the service provider which machine profiles should be attracted at which price. We demonstrate the advantage of a differentiated tariff plan and show how it better protects against adverse selection.

Original languageEnglish (US)
Pages (from-to)530-545
Number of pages16
JournalEuropean Journal of Operational Research
Issue number2
StatePublished - Apr 16 2021


  • Calibration
  • Contract pricing
  • Maintenance
  • Predictive analytics
  • Servitization

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


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