Guardat en:
| Autors principals: | , , , , , |
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| Format: | Recurso digital |
| Idioma: | anglès |
| Publicat: |
Zenodo
2025
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| Matèries: | |
| Accés en línia: | https://doi.org/10.5281/zenodo.15537365 |
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- <p>This research addresses logistics optimization through the use of Big Data and machine learning, focusing on how these emerging technologies can transform traditional logistics processes. The increasing complexity and the need for efficiency in supply chains have led companies to adopt new technological tools to improve decision-making. The central problem investigates the challenges, barriers and benefits of implementing these technologies in logistics operations. The main objective is to analyze the impact of Big Data and machine learning on logistics optimization, offering an in-depth understanding of the advantages and limitations of these resources. The Gifetedean neoperspectivist paradigm was adopted, with its premises of coexistence of relative and absolute truths, and a focus on the theories of logistics optimization, machine learning and Big Data. The hypothetical-deductive method was used to test the hypotheses, conducting a narrative bibliographic and documentary review, based on high-impact articles and books. The databases consulted included Scopus, Web of Science and Google Scholar, using descriptors such as "logistics", "Big Data", "optimization" and "machine learning". The initial review resulted in 150 papers, of which 30 were analyzed in depth. The main findings indicated that the integration of Big Data and machine learning can significantly improve logistics efficiency, but faces challenges in terms of adaptation and infrastructure. The conclusions highlight that, although the technologies present positive results, there are gaps in their implementation in smaller companies. The research contributes theoretically, methodologically and empirically to the field of logistics, offering practical insights for the application of these technologies.</p>