Data-driven preventive maintenance for a heterogeneous machine portfolio

Laurens Deprez*, Katrien Antonio, Joachim Arts, Robert Boute

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We describe a data-driven approach to optimize periodic maintenance policies for a heterogeneous portfolio with different machine profiles. When insufficient data are available per profile to assess failure intensities and costs accurately, we pool the data of all machine profiles and evaluate the effect of (observable) machine characteristics by calibrating appropriate statistical models. This reduces maintenance costs compared to a stratified approach that splits the data into subsets per profile and a uniform approach that treats all profiles the same.

Original languageEnglish (US)
Pages (from-to)163-170
Number of pages8
JournalOperations Research Letters
Issue number2
StatePublished - Mar 2023


  • Data pooling
  • Preventive maintenance
  • Proportional hazards
  • Small data

ASJC Scopus subject areas

  • Software
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics


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