A Bibliometric Analysis of Research on Big Data and the Supply Chain
| dc.creator | Duque Hurtado, Pedro Luis | |
| dc.creator | Giraldo Castellanos, José David | |
| dc.creator | Osorio Gómez, Iván Darío | |
| dc.date | 2023-05-30 | |
| dc.date.accessioned | 2025-10-01T23:49:00Z | |
| dc.description | As contemporary markets must manage large amounts of data, big data has become a crucial tool to address this need. In fact, competitive businesses are employing big data in various processes, including supply chain management. This paper analyzes existing scientific publications on the implementation of big data in the supply chain. To do so, a systematic literature review was conducted using the PRISMA methodology, and relevant documents were selected from the Scopus and Web of Science databases. Then, bibliometric techniques were applied; the documents were classified into three groups, representing the roots, trunk, and leaves of a knowledge tree; and research clusters were identified. The results revealed that using big data in the supply chain enhances decision-making, competitiveness, and logistics efficiency. It is concluded that this topic is receiving increasing attention from researchers, with China leading the way, and that strategic organizational changes are necessary. Although big data brings benefits in terms of efficiency and decision-making, it also faces challenges related to transition and resistance to change. The research clusters identified here have addressed aspects of big data such as performance, adaptability, management capacity, and connectivity. Finally, future research directions are proposed for big data in the areas of automation, the Internet of Things (IoT), and global challenges. | en-US |
| dc.description | Los mercados contemporáneos requieren la gestión de grandes cantidades de datos, por lo que el big data se ha convertido en una tecnología para responder a esta necesidad. En consecuencia, las empresas competitivas los emplean en diversos procesos, como la gestión de la cadena de suministro. En este contexto, el presente artículo tuvo como objetivo analizar la investigación existente sobre la implementación del big data en la cadena de suministro. Para ello, se realizó una revisión sistemática de la literatura utilizando la metodología PRISMA y seleccionando documentos de las bases de datos Scopus y Web of Science. Se aplicaron herramientas bibliométricas y se clasificaron los documentos en tres grupos: raíces, tronco y hojas, según la metáfora del árbol del conocimiento, y se identificaron los clústeres de investigación. Los resultados revelaron que el big data en la cadena de suministro permite mejorar la toma de decisiones, la competitividad y la eficiencia logística. Se concluye que es un tema con creciente interés investigativo, liderado por China; que requiere cambios organizacionales estratégicos. Aporta beneficios en eficiencia y toma de decisiones, pero enfrenta desafíos en transición y resistencia al cambio. Los clústeres abordan el rendimiento, la adaptabilidad, la capacidad de gestión y la conectividad. Se proponen líneas futuras de estudio relacionadas con problemáticas globales, automatización y IoT. | es-ES |
| dc.format | application/pdf | |
| dc.format | application/zip | |
| dc.format | text/xml | |
| dc.format | text/html | |
| dc.identifier | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448 | |
| dc.identifier | 10.22430/24223182.2448 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12622/7099 | |
| dc.language | spa | |
| dc.publisher | Institución Universitaria ITM | es-ES |
| dc.relation | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2900 | |
| dc.relation | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2948 | |
| dc.relation | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2949 | |
| dc.relation | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2959 | |
| dc.relation | https://revistas.itm.edu.co/index.php/revista-cea/article/view/2448/2901 | |
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| dc.rights | Derechos de autor 2023 Pedro Luis Duque Hurtado, José David Giraldo Castellanos, Iván Darío Osorio Gómez | es-ES |
| dc.rights | https://creativecommons.org/licenses/by-nc-sa/4.0 | es-ES |
| dc.source | Revista CEA; Vol. 9 No. 20 (2023); e2448 | en-US |
| dc.source | Revista CEA; Vol. 9 Núm. 20 (2023); e2448 | es-ES |
| dc.source | 2422-3182 | |
| dc.source | 2390-0725 | |
| dc.subject | Big data | en-US |
| dc.subject | supply chain | en-US |
| dc.subject | Logistics 4.0 | en-US |
| dc.subject | technology | en-US |
| dc.subject | Industry 4.0 | en-US |
| dc.subject | big data | es-ES |
| dc.subject | cadenas de suministros | es-ES |
| dc.subject | logística 4.0 | es-ES |
| dc.subject | tecnología | es-ES |
| dc.subject | industria 4.0 | es-ES |
| dc.title | A Bibliometric Analysis of Research on Big Data and the Supply Chain | en-US |
| dc.title | Análisis bibliométrico de la investigación en big data y cadena de suministro | es-ES |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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