Abstract
A full-service maintenance contract covers all futurecosts of both preventive and corrective maintenanceover a predetermined time horizon in exchange fora fixed upfront price. Due to the stochastic natureof the maintenance costs the determination of thecorrect break-even price of such a contract is a keychallenge. We set out a data-driven methodologyto provide insight in the future maintenance costswithin a full-service contract. This methodology in-volves building predictive models for the frequencyof failure and the associated costs taking into accountmachine and customer characteristics. Not only willour approach lead to a break-even price driven bythe analysis of relevant historical data, it also leadsto a classification of the customer base. This classi-fication may in turn enable price discrimination offuture service contracts.
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