  Identifying anomalies in the fuel consumption of the vehicles of a fleet is a
crucial aspect for optimizing consumption and reduce costs. However, this
information alone is insufficient, since fleet operators need to know the
causes behind anomalous fuel consumption. We combine unsupervised anomaly
detection techniques, domain knowledge and interpretable Machine Learning
models for explaining potential causes of abnormal fuel consumption in terms of
feature relevance. The explanations are used for generating recommendations
about fuel optimization, that are adjusted according to two different user
profiles: fleet managers and fleet operators. Results are evaluated over
real-world data from telematics devices connected to diesel and petrol vehicles
from different types of industrial fleets. We measure the proposal regarding
model performance, and using Explainable AI metrics that compare the
explanations in terms of representativeness, fidelity, stability,
contrastiveness and consistency with apriori beliefs. The potential fuel
reductions that can be achieved is round 35%.
