Reference : Empirical risk assessment of maintenance costs under full-service contracts
Scientific journals : Article
Business & economic sciences : Production, distribution & supply chain management
http://hdl.handle.net/10993/50936
Empirical risk assessment of maintenance costs under full-service contracts
English
Deprez, Laurens mailto [University of Luxembourg > Faculty of Law, Economics and Finance (FDEF) > Department of Economics and Management (DEM) >]
Antonio, Katrien [KU Leuven > Faculty of Economics and Business]
Boute, Robert [KU Leuven > Faculty of Economics and Business]
In press
European Journal of Operational Research
Elsevier
Yes (verified by ORBilu)
0377-2217
1872-6860
Amsterdam
Netherlands
[en] Maintenance ; Empirical analysis ; Risk assessment ; Predictive analytics ; Frequency-severity modeling
[en] We 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 5 000 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.
http://hdl.handle.net/10993/50936

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