Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience
[EN] In an evolving landscape shaped by Industry 4.0 and the emerging paradigms of Industry 5.0, the importance of resilience in supply chains should be emphasised. Resilience is the ability to avoid and anticipate disruptive events and, when their occurrence is certain, the capacity of recovering n...
| Autores: | , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universitat Politècnica de València (UPV) |
| Repositorio: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
| Idioma: | inglés |
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| Acesso em linha: | https://riunet.upv.es/handle/10251/235341 |
| Access Level: | acceso abierto |
| Palavra-chave: | Meta-review Supply chain resilient Industry 4.0 Industry 5.0 Enhancing |
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Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain ResilienceBuritica, Luz MilenyCampuzano-Bolarín, FranciscoSanchis, R.|||0000-0002-5495-3339Díaz-Madroñero Boluda, Francisco Manuel|||0000-0003-1693-2876Meta-reviewSupply chain resilientIndustry 4.0Industry 5.0Enhancing[EN] In an evolving landscape shaped by Industry 4.0 and the emerging paradigms of Industry 5.0, the importance of resilience in supply chains should be emphasised. Resilience is the ability to avoid and anticipate disruptive events and, when their occurrence is certain, the capacity of recovering normal supply chain operation. Data science can play a crucial role in enhancing this resilience. Based on this, the main objective of this article is to conduct a meta-review on enhancing resilience in supply chains 4.0 and 5.0, focusing on data science-based approaches to offer a comprehensive overview and high-level synthesis of the current state of knowledge. Our research has shown that the majority of studies employ broad criteria for publication classification and analysis, concentrating on factors such as publication years, academic disciplines, journals, geographical distribution, and research types. However, our approach takes a more specific methodology, by emphasising context, intervention, mechanism, and outcome elements. While the prevailing focus of existing literature is on Industry 4.0-based supply chain contexts, with limited attention to Industry 5.0, the most analysed technologies include blockchain, industrial internet of things, internet of things, cloud comput-ing, digital twins, among others. Notably, resilience enhancement in the reviewed studies predominantly relies on artificial intelligence, machine learning, data ana-lytics, big data, and, to a lesser extent, deep reinforcement learning and predictive analysis.SpringerDepartamento de Organización de EmpresasCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-05-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/235341reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reserva de todos los derechoshttp://rightsstatements.org/vocab/InC/1.0/info:eu-repo/semantics/openAccessoai:dnet:riunet______::161b1e25173998425eadd01e1ed60b262026-06-13T07:49:27Z |
| dc.title.none.fl_str_mv |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| title |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| spellingShingle |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience Buritica, Luz Mileny Meta-review Supply chain resilient Industry 4.0 Industry 5.0 Enhancing |
| title_short |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| title_full |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| title_fullStr |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| title_full_unstemmed |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| title_sort |
Meta-review of Data Science in Industry 4.0/5.0 for Enhancing Supply Chain Resilience |
| dc.creator.none.fl_str_mv |
Buritica, Luz Mileny Campuzano-Bolarín, Francisco Sanchis, R.|||0000-0002-5495-3339 Díaz-Madroñero Boluda, Francisco Manuel|||0000-0003-1693-2876 |
| author |
Buritica, Luz Mileny |
| author_facet |
Buritica, Luz Mileny Campuzano-Bolarín, Francisco Sanchis, R.|||0000-0002-5495-3339 Díaz-Madroñero Boluda, Francisco Manuel|||0000-0003-1693-2876 |
| author_role |
author |
| author2 |
Campuzano-Bolarín, Francisco Sanchis, R.|||0000-0002-5495-3339 Díaz-Madroñero Boluda, Francisco Manuel|||0000-0003-1693-2876 |
| author2_role |
author author author |
| dc.contributor.none.fl_str_mv |
Departamento de Organización de Empresas Centro de Investigación en Gestión e Ingeniería de Producción Escuela Politécnica Superior de Alcoy Repositorio Institucional de la Universitat Politècnica de València Riunet |
| dc.subject.none.fl_str_mv |
Meta-review Supply chain resilient Industry 4.0 Industry 5.0 Enhancing |
| topic |
Meta-review Supply chain resilient Industry 4.0 Industry 5.0 Enhancing |
| description |
[EN] In an evolving landscape shaped by Industry 4.0 and the emerging paradigms of Industry 5.0, the importance of resilience in supply chains should be emphasised. Resilience is the ability to avoid and anticipate disruptive events and, when their occurrence is certain, the capacity of recovering normal supply chain operation. Data science can play a crucial role in enhancing this resilience. Based on this, the main objective of this article is to conduct a meta-review on enhancing resilience in supply chains 4.0 and 5.0, focusing on data science-based approaches to offer a comprehensive overview and high-level synthesis of the current state of knowledge. Our research has shown that the majority of studies employ broad criteria for publication classification and analysis, concentrating on factors such as publication years, academic disciplines, journals, geographical distribution, and research types. However, our approach takes a more specific methodology, by emphasising context, intervention, mechanism, and outcome elements. While the prevailing focus of existing literature is on Industry 4.0-based supply chain contexts, with limited attention to Industry 5.0, the most analysed technologies include blockchain, industrial internet of things, internet of things, cloud comput-ing, digital twins, among others. Notably, resilience enhancement in the reviewed studies predominantly relies on artificial intelligence, machine learning, data ana-lytics, big data, and, to a lesser extent, deep reinforcement learning and predictive analysis. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025-05-01 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 VoR http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://riunet.upv.es/handle/10251/235341 |
| url |
https://riunet.upv.es/handle/10251/235341 |
| dc.language.none.fl_str_mv |
Inglés eng |
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Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 Reserva de todos los derechos http://rightsstatements.org/vocab/InC/1.0/ |
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info:eu-repo/semantics/openAccess |
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open access http://purl.org/coar/access_right/c_abf2 Reserva de todos los derechos http://rightsstatements.org/vocab/InC/1.0/ |
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openAccess |
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application/pdf application/pdf |
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Springer |
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Springer |
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reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname:Universitat Politècnica de València (UPV) |
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Universitat Politècnica de València (UPV) |
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RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
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RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia |
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