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

Demand 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. Inc...

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Detalles Bibliográficos
Autores: Olmo, Manuel del, Domingo Navas, María Rosario
Tipo de recurso: artículo
Fecha de publicación:2026
País:España
Institución:Universidad de Cantabria (UC)
Repositorio:e-spacio (DSpace). Repositorio Institucional de la UNED
Idioma:inglés
OAI Identifier:oai:e-spacio.uned.es:20.500.14468/31954
Acceso en línea:https://hdl.handle.net/20.500.14468/31954
Access Level:acceso abierto
Palabra clave:3305 Tecnología de la construcción
Intermittent demand forecasting
Rotables
Spare parts
Aerospace
Aircraft
Machine learning
ODS 9 - Industria, innovación e infraestructura
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oai_identifier_str oai:e-spacio.uned.es:20.500.14468/31954
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spelling Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patternsOlmo, Manuel delDomingo Navas, María Rosario3305 Tecnología de la construcciónIntermittent demand forecastingRotablesSpare partsAerospaceAircraftMachine learningODS 9 - Industria, innovación e infraestructuraDemand 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.Elseviere-Spacio UNED20262026-02-2520262026-02-2220262026-02-22journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/31954reponame:e-spacio (DSpace). Repositorio Institucional de la UNEDinstname:Universidad de Cantabria (UC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/319542026-06-15T06:41:27Z
dc.title.none.fl_str_mv Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
title Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
spellingShingle Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
Olmo, Manuel del
3305 Tecnología de la construcción
Intermittent demand forecasting
Rotables
Spare parts
Aerospace
Aircraft
Machine learning
ODS 9 - Industria, innovación e infraestructura
title_short Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
title_full Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
title_fullStr Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
title_full_unstemmed Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
title_sort Intermittent demand forecasting of aerospace rotable parts. A framework for unpredictable flight patterns
dc.creator.none.fl_str_mv Olmo, Manuel del
Domingo Navas, María Rosario
author Olmo, Manuel del
author_facet Olmo, Manuel del
Domingo Navas, María Rosario
author_role author
author2 Domingo Navas, María Rosario
author2_role author
dc.contributor.none.fl_str_mv e-Spacio UNED
dc.subject.none.fl_str_mv 3305 Tecnología de la construcción
Intermittent demand forecasting
Rotables
Spare parts
Aerospace
Aircraft
Machine learning
ODS 9 - Industria, innovación e infraestructura
topic 3305 Tecnología de la construcción
Intermittent demand forecasting
Rotables
Spare parts
Aerospace
Aircraft
Machine learning
ODS 9 - Industria, innovación e infraestructura
description Demand 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.
publishDate 2026
dc.date.none.fl_str_mv 2026
2026-02-25
2026
2026-02-22
2026
2026-02-22
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.14468/31954
url https://hdl.handle.net/20.500.14468/31954
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/4.0/deed.es
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
http://creativecommons.org/licenses/by-nc/4.0/deed.es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:e-spacio (DSpace). Repositorio Institucional de la UNED
instname:Universidad de Cantabria (UC)
instname_str Universidad de Cantabria (UC)
reponame_str e-spacio (DSpace). Repositorio Institucional de la UNED
collection e-spacio (DSpace). Repositorio Institucional de la UNED
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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