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dc.contributor.authorStanula, Patrick
dc.contributor.authorPraetzas, Christopher
dc.contributor.authorKohn, Oliver
dc.contributor.authorMetternich, Joachim
dc.contributor.authorWeigold, Matthias
dc.contributor.authorBuchwald, Arne
dc.date.accessioned2020-10-01T07:21:18Z
dc.date.available2020-10-01T07:21:18Z
dc.date.issued2020en_US
dc.identifier.issn2212-8271
dc.identifier.doi10.1016/j.procir.2020.03.080
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6570
dc.description.abstractThe acquisition costs of expensive machine tools are often a financial challenge for small and medium-sized enterprises, which is why many companies draw on traditional leasing models. For some types of machines, such as milling machines, however, there is no linear relationship between use and wear, thus creating a principle-agent problem and a potentially low(er) residual value of the machine in case of above-average use. Modern machine tools are increasingly equipped with sensors to monitor machining operations. The data from these sensors can help to deduce the wear of its components from the stress on the machine. Nevertheless, this has not resulted in data-driven, alternative payment models of expensive machines. Therefore, this paper presents a novel data-driven payment model based on a so-called stress factor, describing the aggregated machine wear due to the production process. This approach considers the economic and technologic perspectives to bring transparency to machine leasing.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectBusiness Modelen_US
dc.subjectPay-Per-Xen_US
dc.subjectMachine Toolen_US
dc.subjectRemaining Usefull Lifetimeen_US
dc.titleStress-oriented, data-based payment model for machine toolsen_US
refterms.dateFOA2020-11-03T10:04:51Z
dc.identifier.journalProcedia CIRPen_US
dc.source.volume93en_US
dc.source.issue2020en_US
dc.source.beginpage1526en_US
dc.source.endpage1531en_US
dc.contributor.departmentInstitute for Production Management, Technology and Machine Tools (PTW), Otto-Berndt-Straße 2, 64287 Darmstadt, Germanyen_US
dc.contributor.departmentEBS Business School, Information Systems Group, Gustav-Stresemann-Ring 3, 65189 Wiesbaden, Germanyen_US
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typearticleJournal articleen_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid270410en_US


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