Optimizing the preventive maintenance frequency with causal machine learning

Toon Vanderschueren*, Robert Boute, Tim Verdonck, Bart Baesens, Wouter Verbeke

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Maintenance is a challenging operational problem where the goal is to plan sufficient preventive maintenance (PM) to avoid asset overhauls and failures. Existing work typically relies on strong assumptions (1) to model the asset's overhaul and failure rate, assuming a stochastic process with known hazard rate, (2) to model the effect of PM on this hazard rate, assuming the effect is deterministic or governed by a known probability distribution, and (3) by not taking asset-specific characteristics into account, but assuming homogeneous hazard rates and PM effects. Instead of relying on these assumptions to model the problem, this work uses causal inference to learn the effect of the PM frequency on the overhaul and failure rate, conditional on the asset's characteristics, from observational data. Based on these learned outcomes, we can optimize each asset's PM frequency to minimize the combined cost of failures, overhauls, and preventive maintenance. We validate our approach on real-life data of more than 4000 maintenance contracts from an industrial partner. Empirical results on semi-synthetic data show that our methodology based on causal machine learning results in individualized maintenance schedules that are more accurate and cost-effective than a non-causal approach that does not deal with selection bias and a non-individualized approach that prescribes the same PM frequency to all machines.

Original languageEnglish (US)
Article number108798
JournalInternational Journal of Production Economics
Volume258
DOIs
StatePublished - Apr 2023

Keywords

  • Causal inference
  • Individual treatment effects
  • Machine learning
  • Maintenance

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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