Empirical risk assessment of maintenance costs under full-service contracts

We provide a data-driven framework to conduct a risk assessment, including data pre-processing, ex-ploration, 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 in-surance 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 5 0 0 0 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 statisti-cal 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.(c) 2022 Elsevier B.V. All rights reserved.