Deprez, LaurensAntonio, KatrienBoute, Robert2024-04-262024-04-2620230377-221710.1016/j.ejor.2022.03.055http://hdl.handle.net/20.500.12127/7444We propose a framework to assess the risk of full-service maintenance contracts. Our models quantify the impact of machine characteristics on the contract costs.• We build a differentiated, break-even tariff plan for the contract portfolio.• We augment the break-even price with appropriate risk marginsWe provide a data-driven framework to conduct a risk assessment, including data pre-processing, exploration, and statistical modeling, on a portfolio of full-service maintenance contracts. These contracts cover all maintenance-related costs for a fixed, upfront fee during a predetermined horizon. Charging each contract a price proportional to its risk prevents adverse selection by incentivizing low risk (i.e., maintenance-light) profiles to not renege on their agreements. We borrow techniques from non-life insurance pricing and tailor them to the setting of maintenance contracts to assess the risk and estimate the expected maintenance costs under a full-service contract. We apply the framework on a portfolio of about full-service contracts of industrial equipment and show how a data-driven analysis based on contract and machine characteristics, or risk factors, supports a differentiated, risk-based break-even tariff plan. We employ generalized additive models (GAMs) to predict the risk factors’ impact on the frequency (number of) and severity (cost) of maintenance interventions. GAMs are interpretable yet flexible statistical models that capture the effect of both continuous and categorical risk factors. Our predictive models quantify the impact of the contract and machine type, service history, and machine running hours on the contract cost. We additionally utilize the predictive cost distributions of our models to augment the break-even price with the appropriate risk margins to further protect against the inherently stochastic nature of the maintenance costs. The framework shows how maintenance intervention data can set up a differentiated tariff plan.enMaintenanceRisk AssessmentEmpirical AnalysisPredictive AnalyticsFrequency-Severity ModelingEuropean Journal of Operational Research1872-6860102358