Demand classification for spare parts supply chains in the presence of three dimensional printers

dc.contributor.advisorHekimoğlu, Mustafaen_US
dc.contributor.authorHekimoğlu, Mustafa
dc.date2022-01
dc.date.accessioned2023-07-30T11:19:22Z
dc.date.available2023-07-30T11:19:22Z
dc.date.issued2022
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Mühendislik ve Doğa Bilimleri Ana Bilim Dalıen_US
dc.description.abstractThree-dimensional printers (3DPs) are currently the source of the supply chain and are used to ensure spare parts supply in case of shortages. However, the reliability of the part produced in 3DP is lower than the original part supplied by the original equipment manufacturer (OEM). Failure of parts creates demand and the failure probability of original and printed part is different than each other. Thus, knowing the total demand distribution have great importance in optimizing the order quantity given to the OEM in the presence of 3DPs. In this study, the demand distribution of system failures has been determined by using the distribution classification methods put forward by Ord (1967) and Adan et al. (1995). In line with the results, according to study of Ord(1967), demand distribution is found as Hypergeometric and Binomial distribution. Discrete distribution family of Adan et al. (1995) gives Binomial distribution for the system demand. All results are tested with chi-square test and likelihood ratio test.en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/4421
dc.identifier.yoktezid771259en_US
dc.language.isoenen_US
dc.publisherKadir Has Üniversitesien_US
dc.relation.publicationcategoryTezen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject3D Printersen_US
dc.subjectSupply Chainen_US
dc.subjectDemand Classificationen_US
dc.subjectDistribution Selectionen_US
dc.subjectOrd Hypergeometric Distribution Familyen_US
dc.subjectAdan Discrete Distribution Familyen_US
dc.titleDemand classification for spare parts supply chains in the presence of three dimensional printersen_US
dc.typeMaster Thesisen_US
dspace.entity.typePublication
relation.isAuthorOfPublication533132ce-5631-4068-91c5-2806df0f65bb
relation.isAuthorOfPublication.latestForDiscovery533132ce-5631-4068-91c5-2806df0f65bb

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