Demand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet

Basit Öğe Kaydı
dc.authoridIFRAZ, Metin/0000-0001-7161-223X
dc.contributor.authorIfraz, Metin
dc.contributor.authorAktepe, Adnan
dc.contributor.authorErsoz, Suleyman
dc.contributor.authorcetinyokus, Tahsin
dc.date.accessioned2025-01-21T16:37:17Z
dc.date.available2025-01-21T16:37:17Z
dc.date.issued2023
dc.departmentKırıkkale Üniversitesi
dc.description.abstractForecasting the demand of spare parts of vehicles in bus fleets is a vital issue. Vehicles must operate effectively and must have a high availability rate in the fleet. In maintenance operations, faulty parts or parts that complete their lifetime must be replaced with a new one. Spare parts needed must be in inventories with the required amount on time. In this sector, there are thousands of spare parts to manage. The maintenance and repair department must operate effectively. In order to accomplish this, accurate forecast of spare parts is required. In this study, demand forecasting was carried out with regression-based methods (multivariate linear regression, multivariate nonlinear regression, Gaussian process regression, additive regression, regression by discretion, support vector regression), rule-based methods (decision table, M5Rule), tree-based methods (random forest, M5P, Random tree, REPTree) and artificial neural networks. The forecasting model developed in this study includes critical variables such as the number of vehicles in the fleet, the number of breakdowns that cause parts to change, the number of periodic maintenance, mean time between failure and demand quantity in previous years. The application was carried out with real data of eight (2013-2020) years. 2013-2019 data was used for training and 2020 data was used for testing. In forecasts, support vector regression among regression-based methods, decision table among rule-based methods, M5P among tree-based methods gave the best results. It has been observed that the artificial neural network produced more accurate forecasts than all other methods. Artificial neural network forecasts give the highest forecast accuracy rate and the least deviation.
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [MAG -221M438]
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) within the 1001-The Scientific and Technological Research Projects Funding Program [grant numbers: MAG -221M438] .
dc.identifier.doi10.1016/j.jer.2023.100057
dc.identifier.issn2307-1877
dc.identifier.issn2307-1885
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85165662362
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1016/j.jer.2023.100057
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24449
dc.identifier.volume11
dc.identifier.wosWOS:000976345400001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Publication Council
dc.relation.ispartofJournal of Engineering Research
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectArtificial intelligence; Demand forecasting; Machine learning; Maintenance management; Spare parts
dc.titleDemand forecasting of spare parts with regression and machine learning methods: Application in a bus fleet
dc.typeArticle

Dosyalar

Koleksiyon

WOS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu