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dc.contributor.authorDeprez, Laurens
dc.contributor.authorAntonio, Katrien
dc.contributor.authorBoute, Robert
dc.date.accessioned2020-08-25T03:40:31Z
dc.date.available2020-08-25T03:40:31Z
dc.date.issued2021en_US
dc.identifier.issn0377-2217
dc.identifier.doi10.1016/j.ejor.2020.08.022
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6530
dc.description.abstractAs 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 profiftability, 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 profi tability of the service contracts. We contribute to the literature with a data-driven tarif plan based on the calibration of predictive models that take into account the different machine profi les. This conveys to the service provider which machine pro files should be attracted at which price. We demonstrate the advantage of a differentiated tarif plan and show how it better protects against adverse selection.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMaintenanceen_US
dc.subjectServitizationen_US
dc.subjectContract Pricingen_US
dc.subjectPredictive Analyticsen_US
dc.subjectRisk Managementen_US
dc.subjectCalibrationen_US
dc.titlePricing service maintenance contracts using predictive analyticsen_US
dc.identifier.journalEuropean Journal of Operational Researchen_US
dc.source.volume290
dc.source.issue2
dc.source.beginpage530
dc.source.endpage545
dc.contributor.departmentFaculty of Economics and Business, KU Leuven, Belgiumen_US
dc.contributor.departmentFaculty of Economics and Business, University of Amsterdam, The Netherlandsen_US
dc.identifier.eissn1872-6860
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleVlerick strategic journal articleen_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid102358en_US


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