Stockout risk estimation and expediting for repairable spare parts

dc.authoridHekimoglu, Mustafa/0000-0001-9446-0582
dc.authorwosidHekimoglu, Mustafa/GRF-1500-2022
dc.contributor.authorHekimoglu, Mustafa
dc.contributor.authorKok, A. Gurhan
dc.contributor.authorSahin, Mustafa
dc.date.accessioned2023-10-19T15:12:13Z
dc.date.available2023-10-19T15:12:13Z
dc.date.issued2022
dc.department-temp[Hekimoglu, Mustafa] Kadir Has Univ, Fac Engn & Nat Sci, TR-34083 Istanbul, Turkey; [Kok, A. Gurhan] Koc Univ, Coll Adm Sci, TR-34450 Istanbul, Turkey; [Sahin, Mustafa] ITU Teknokent Istanbul, Invent Analyt, Ari 2, Istanbul, Turkeyen_US
dc.description.abstractStockouts of repairable spares usually lead to significant downtime costs. Managers of Maintenance Repair Organizations (MROs) seek advance indicators of future stockouts which might allow them to take proactive actions that are beneficial for achieving target service levels with reasonable costs. Among such (proactive) actions, the most common, and the cheapest one is expediting existing repair processes. In this study, we develop an advance stockout risk estimation system for repairable spare parts. To the best of our knowledge, this is the first study to estimate the future stockout risk of a repairable part. The method considers different statistics, e.g. the number of ongoing repair processes, demand rate, repair time, etc. to estimate stockout risk of a repairable part for a given planning horizon. In our field tests with empirical data, the suggested method overperforms two heuristic approaches and achieves accuracy rates of 63% for 15 day-planning horizon and 83% for 45 days. We also suggest a repairable inventory control system including repair expediting, inspection and con-demnation processes. To optimize the control parameters we suggest a simple algorithm considering two constraints: Target service level and maximum fraction of expedited demand. The algorithm is proved to be efficient for finding the optimum policy parameter in our tests with empirical data. Tests with empirical data suggest savings up to 8%. Both systems are implemented at an MRO as building blocks of a inventory control tower. The impact of the implementation is assessed with empirical simulations and verified from the financial indicators of the company.en_US
dc.identifier.citation3
dc.identifier.doi10.1016/j.cor.2021.105562en_US
dc.identifier.issn0305-0548
dc.identifier.issn1873-765X
dc.identifier.scopus2-s2.0-85115948865en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.cor.2021.105562
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5380
dc.identifier.volume138en_US
dc.identifier.wosWOS:000703386200003en_US
dc.identifier.wosqualityQ2
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers & Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInventoryEn_Us
dc.subjectDemandEn_Us
dc.subjectPoliciesEn_Us
dc.subjectManagementEn_Us
dc.subjectInventory
dc.subjectRepairable spare partsen_US
dc.subjectDemand
dc.subjectInventory controlen_US
dc.subjectPolicies
dc.subjectStockout risk estimationen_US
dc.subjectManagement
dc.subjectRepair expeditingen_US
dc.titleStockout risk estimation and expediting for repairable spare partsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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