  As more manufacturers shift their focus from selling products to end
solutions, full-service maintenance contracts gain traction in the business
world. These contracts cover all maintenance related costs during a
predetermined horizon in exchange for a fixed service fee and relieve customers
from uncertain maintenance costs. To guarantee profitability, the service fees
should at least cover the expected costs during the contract horizon. As these
expected costs may depend on several machine-dependent characteristics, e.g.
operational environment, the service fees should also be differentiated based
on these characteristics. If not, customers that are less prone to high
maintenance costs will not buy into or renege on the contract. The latter can
lead to adverse selection and leave the service provider with a
maintenance-heavy portfolio, which may be detrimental to the profitability of
the service contracts. We contribute to the literature with a data-driven
tariff plan based on the calibration of predictive models that take into
account the different machine profiles. This conveys to the service provider
which machine profiles should be attracted at which price. We demonstrate the
advantage of a differentiated tariff plan and show how it better protects
against adverse selection.
