Stockout Risk Estimation and Expediting for Repairable Spare Parts

dc.authorid Hekimoglu, Mustafa/0000-0001-9446-0582
dc.authorwosid Hekimoglu, Mustafa/GRF-1500-2022
dc.contributor.author Hekimoglu, Mustafa
dc.contributor.author Hekimoğlu, Mustafa
dc.contributor.author Kok, A. Gurhan
dc.contributor.author Sahin, Mustafa
dc.contributor.other Industrial Engineering
dc.date.accessioned 2023-10-19T15:12:13Z
dc.date.available 2023-10-19T15:12:13Z
dc.date.issued 2022
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, Turkey en_US
dc.description.abstract Stockouts 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.citationcount 3
dc.identifier.doi 10.1016/j.cor.2021.105562 en_US
dc.identifier.issn 0305-0548
dc.identifier.issn 1873-765X
dc.identifier.scopus 2-s2.0-85115948865 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.cor.2021.105562
dc.identifier.uri https://hdl.handle.net/20.500.12469/5380
dc.identifier.volume 138 en_US
dc.identifier.wos WOS:000703386200003 en_US
dc.identifier.wosquality Q2
dc.khas 20231019-WoS en_US
dc.language.iso en en_US
dc.publisher Pergamon-Elsevier Science Ltd en_US
dc.relation.ispartof Computers & Operations Research en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 5
dc.subject Inventory En_Us
dc.subject Demand En_Us
dc.subject Policies En_Us
dc.subject Management En_Us
dc.subject Inventory
dc.subject Repairable spare parts en_US
dc.subject Demand
dc.subject Inventory control en_US
dc.subject Policies
dc.subject Stockout risk estimation en_US
dc.subject Management
dc.subject Repair expediting en_US
dc.title Stockout Risk Estimation and Expediting for Repairable Spare Parts en_US
dc.type Article en_US
dc.wos.citedbyCount 4
dspace.entity.type Publication
relation.isAuthorOfPublication 533132ce-5631-4068-91c5-2806df0f65bb
relation.isAuthorOfPublication.latestForDiscovery 533132ce-5631-4068-91c5-2806df0f65bb
relation.isOrgUnitOfPublication 28868d0c-e9a4-4de1-822f-c8df06d2086a
relation.isOrgUnitOfPublication.latestForDiscovery 28868d0c-e9a4-4de1-822f-c8df06d2086a

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