  Machine maintenance is a challenging operational problem, where the goal is
to plan sufficient preventive maintenance to avoid machine failures and
overhauls. Maintenance is often imperfect in reality and does not make the
asset as good as new. Although a variety of imperfect maintenance policies have
been proposed in the literature, these rely on strong assumptions regarding the
effect of maintenance on the machine's condition, assuming the effect is (1)
deterministic or governed by a known probability distribution, and (2)
machine-independent. This work proposes to relax both assumptions by learning
the effect of maintenance conditional on a machine's characteristics from
observational data on similar machines using existing methodologies for causal
inference. By predicting the maintenance effect, we can estimate the number of
overhauls and failures for different levels of maintenance and, consequently,
optimize the preventive maintenance frequency to minimize the total estimated
cost. We validate our proposed approach using real-life data on more than 4,000
maintenance contracts from an industrial partner. Empirical results show that
our novel, causal approach accurately predicts the maintenance effect and
results in individualized maintenance schedules that are more accurate and
cost-effective than supervised or non-individualized approaches.
