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 language | English (US) |
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Article number | 108798 |
Journal | International Journal of Production Economics |
Volume | 258 |
DOIs | |
State | Published - Apr 2023 |
Funding
This work was supported by the BNP Paribas Fortis Chair in Fraud Analytics, BASF Chair on Robust Predictive Analytics, FWO Research Project G015020N , and FWO PhD Fellowship 11I7322N . The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government – department EWI.
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