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Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns

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dc.contributor.authorOlmo, Manuel del
dc.contributor.authorDomingo Navas, María Rosario
dc.date.accessioned2026-02-25T12:33:32Z
dc.date.available2026-02-25T12:33:32Z
dc.date.issued2026-02-22
dc.descriptionThe registered version of this article, first published in “Computers & Industrial Engineering Vol. 214, págs. 111917", is available online at the publisher's website: https://doi.org/10.1016/j.cie.2026.111917
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en “Computers & Industrial Engineering Vol. 214, págs. 111917", está disponible en línea en el sitio web del editor: https://doi.org/10.1016/j.cie.2026.111917
dc.description.abstractDemand forecasting of aerospace spare parts has a high impact on aircraft maintenance operations, aircraft serviceability and companies’ profitability. Traditional forecasting methods used in the industry utilise past consumption for forecasting future demand, often overlooking operational data. Incorporating fleet usage-data for capturing service variability in demand forecasting methods is crucial in operations with unpredictable flight patterns, like military aircrafts, business jets, and different air services, like air ambulances, search and rescue or policing operations. In this paper, we present the development of a machine learning (ML) framework for the forecasting of aerospace rotable components, generally life limited or inspected regularly. Different traditional and ML-based forecasting methods are reviewed, the impact of different service-related features is analysed, and a framework for addressing the potential service variability of an asset during its lifetime is proposed. The framework is validated with historical spare parts consumption of a European maintenance, repair and overhaul (MRO) service centre, achieving the most accurate demand forecast in 99.2% of the stock keeping units (SKUs) analysed compared to traditional baselines.en
dc.description.provenanceMade available in DSpace on 2026-02-25T12:33:32Z (GMT). No. of bitstreams: 1 Domingo_Rosario_Intermittent demand forecasti_MARIA ROSARIO DOMING.pdf: 3514078 bytes, checksum: fa109632e8deebff3f799d1c1e52bf36 (MD5) Previous issue date: 2026-02-22en
dc.description.versionversión publicada
dc.identifier.citationManuel del Olmo, Rosario Domingo (2026). Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns. Computers & Industrial Engineering Vol. 214, págs. 111917. https://doi.org/10.1016/j.cie.2026.111917
dc.identifier.doihttps://doi.org/10.1016/j.cie.2026.111917
dc.identifier.eissn1879-0550
dc.identifier.issn0360-8352
dc.identifier.urihttps://hdl.handle.net/20.500.14468/31954
dc.journal.titleComputers & Industrial Engineering
dc.journal.volume214
dc.language.isoen
dc.publisherElsevier
dc.relation.centerEscuela Técnica Superior de Ingenieros Industriales
dc.relation.departmentIngeniería de Construcción y Fabricación
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/deed.es
dc.subject3305 Tecnología de la construcción
dc.subject.keywordsIntermittent demand forecastingen
dc.subject.keywordsRotablesen
dc.subject.keywordsSpare partsen
dc.subject.keywordsAerospaceen
dc.subject.keywordsAircraften
dc.subject.keywordsMachine learningen
dc.subject.odsODS 9 - Industria, innovación e infraestructura
dc.titleIntermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patternsen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublication.latestForDiscovery6b34d535-7b88-48ed-9206-435d40b0b5b5
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