Aplicación de la inteligencia artificial en la optimización de la logística y la cadena de suministro en el sector manufacturero
| datacite.rights | http://purl.org/coar/access_right/c_f1cf | |
| dc.contributor.advisor | Cervantes Atia, Viviana | |
| dc.contributor.author | Santos Ortiz, Yassadis Michel | |
| dc.contributor.author | Quant Reales, Jefferson Rafael | |
| dc.date.accessioned | 2025-12-02T22:36:09Z | |
| dc.date.available | 2025-12-02T22:36:09Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | En un mundo caracterizado por la digitalización acelerada, la globalización de los mercados y la creciente incertidumbre económica, las organizaciones enfrentan el desafío de transformar sus modelos operativos para mantener su competitividad y sostenibilidad. En este contexto, la inteligencia artificial (IA) se ha posicionado como una herramienta estratégica fundamental para la gestión eficiente de los procesos logísticos y de la cadena de suministro, especialmente en el sector manufacturero, donde la optimización de recursos, la agilidad operativa y la capacidad de respuesta son determinantes para el éxito empresarial. Este trabajo de investigación tuvo como propósito analizar la aplicación de la inteligencia artificial en la optimización de la logística y la cadena de suministro en el sector manufacturero colombiano, explorando las principales aplicaciones, beneficios y desafíos asociados con su implementación. | spa |
| dc.description.abstract | In a world characterized by accelerated digitalization, market globalization, and increasing economic uncertainty, organizations face the challenge of transforming their operational models to maintain competitiveness and sustainability. In this context, artificial intelligence (AI) has emerged as a fundamental strategic tool for the efficient management of logistics and supply chain processes, particularly in the manufacturing sector, where resource optimization, operational agility, and responsiveness are decisive factors for business success. The purpose of this research was to analyze the application of artificial intelligence in the optimization of logistics and the supply chain in the Colombian manufacturing sector, exploring the main applications, benefits, and challenges associated with its implementation. | spa |
| dc.format.mimetype | ||
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/17150 | |
| dc.language.iso | spa | |
| dc.publisher | Ediciones Universidad Simón Bolívar | spa |
| dc.publisher | Facultad de Administración y Negocios | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | eng |
| dc.rights.accessrights | info:eu-repo/semantics/embargoedAccess | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Inteligencia artificial | spa |
| dc.subject | Logística | spa |
| dc.subject | Cadena de suministro | spa |
| dc.subject | Manufactura | spa |
| dc.subject | Transformación digital | spa |
| dc.subject | Competitividad | spa |
| dc.subject.keywords | Artificial intelligence | eng |
| dc.subject.keywords | Logistics | eng |
| dc.subject.keywords | Supply chain | eng |
| dc.subject.keywords | Manufacturing | eng |
| dc.subject.keywords | Digital transformation | eng |
| dc.subject.keywords | Competitiveness | eng |
| dc.title | Aplicación de la inteligencia artificial en la optimización de la logística y la cadena de suministro en el sector manufacturero | spa |
| dc.type.driver | info:eu-repo/semantics/other | |
| dc.type.spa | Trabajo de grado - pregrado | |
| dcterms.references | AI-Driven Supply Chains: 3 Cases | Center for Transportation and Logistics. (s. f.). https://ctl.mit.edu/events/thu-02292024-0800/ai-driven-supply-chains-3- cases?utm_source=chatgpt.com | spa |
| dcterms.references | Alicke, K., & Foster, T. (2024, 14 octubre). Supply chains: Still vulnerable. McKinsey & Company. https://www.mckinsey.com/capabilities/operations/our-insights/supplychain-risk-survey?utm_source=chatgpt.com | spa |
| dcterms.references | Ballou, R. H. (2004). Logistics: Supply chain management. Pearson. | spa |
| dcterms.references | Bostrom, N. (2014). Superintelligence: Paths, dangers, strategies. Oxford University Press. | spa |
| dcterms.references | Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company. | spa |
| dcterms.references | Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., ... & Trench, M. (2017). Artificial intelligence: The next digital frontier? McKinsey Global Institute. https://www.mckinsey.com | spa |
| dcterms.references | Chamorro-Premuzic, T., Wade, M., & Jordan, J. (2019). How AI is changing HR. Harvard Business Review. https://hbr.org | spa |
| dcterms.references | Chae, B., & Olson, D. L. (2013). Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making, 12(1), 9–26. https://doi.org/10.1142/S0219622013500016 | spa |
| dcterms.references | Chopra, S., & Meindl, P. (2021). Supply Chain Management: Strategy, Planning, and Operation (7th ed.). Pearson. https://www.pearson.com/en-us/subjectcatalog/p/supply-chain-management-strategy-planning-andoperation/P200000005863/9780137502844 | spa |
| dcterms.references | Christopher, M. (2016, 23 marzo). Logistics and Supply Chain Management. Google Books. https://books.google.com/books/about/Logistics_and_Supply_Chain_Management.h tml?id=NIfQCwAAQBAJ | spa |
| dcterms.references | Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly. https://www.mckinsey.com | spa |
| dcterms.references | Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116. | spa |
| dcterms.references | Domingos, P. (2015). The master algorithm: Howthe quest forthe ultimate learning machine will remake our world. Basic Books. | spa |
| dcterms.references | Dubey, R., Gunasekaran, A., Childe, S. J., Papadopoulos, T., & Wamba, S. F. (2017). The impact of big data on world-class sustainable manufacturing. The International Journal of Advanced Manufacturing Technology. | spa |
| dcterms.references | Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z | spa |
| dcterms.references | Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5 | spa |
| dcterms.references | Gattorna, J. (2015). Dynamic supply chains. Pearson Education. | spa |
| dcterms.references | González Fabre, R., & Cárdenes Doctor, J. (2022). La aplicación de Big Data e Inteligencia Artificial en logística y transporte para la optimización de procesos en empresas. Universidad Pontificia Comillas. | spa |
| dcterms.references | González-Coronado, R., Martínez, J., & Pérez, G. (2019). Tipologías de revisión de literatura científica: Un enfoque para investigaciones en educación. Revista Electrónica Educare, 23(3), 1–23. https://doi.org/10.15359/ree.23-3.15 | spa |
| dcterms.references | Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. | spa |
| dcterms.references | Hernández, R., Fernández, C., & Baptista, P. (2014). Metodología de la investigación (6.ª ed.). McGraw-Hill. | spa |
| dcterms.references | Ibarra-Peña, K. A., Morán-Murillo, P. N., & Rodríguez-Sares, E. A. (2024). Inteligencia artificial y Big Data en la optimización de cadenas de suministro internacionales: hacia una logística predictiva y sostenible. Revista UGC. | spa |
| dcterms.references | Informe de OMC examina efecto de inteligencia artificial en comercio mundial. (2024, 21 noviembre). Diario Digital Nuestro País. https://www.elpais.cr/2024/11/21/informede-omc-examina-efecto-de-inteligencia-artificial-en-comerciomundial/?utm_source=chatgpt.com | spa |
| dcterms.references | Ivanov, D. (2020). Viable Supply Chain Model: Integrating Agility, Resilience and Sustainability Perspectives—Lessons from and Thinking beyond the COVID-19 Pandemic. Annals of Operations Research, 319, 1411–1431. https://doi.org/10.1007/s10479-020-03640-6 | spa |
| dcterms.references | Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research. | spa |
| dcterms.references | Ivanov, D., Dolgui, A., & Sokolov, B. (2019). Digital supply chain twins: Managing the ripple effect, resilience, and disruption risks through the combination of digital twins and artificial intelligence. IFAC-PapersOnLine, 52(13), 33–38. https://doi.org/10.1016/j.ifacol.2019.11.193 | spa |
| dcterms.references | Ivanov, D., Dolgui, A., Das, A., & Sokolov, B. (2019). Digitalsupply chain twins: Managing the ripple effect, resilience, and disruption risks by data-driven optimization, simulation, and visibility. Transportation Research Part E: Logistics and Transportation Review, 136, 101922. https://doi.org/10.1016/j.tre.2019.101922 | spa |
| dcterms.references | Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1, 389–399. https://doi.org/10.1038/s42256-019-0088- 2 | spa |
| dcterms.references | Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of AI. Business Horizons, 62(1), 15–25. https://doi.org/10.1016/j.bushor.2018.08.004 | spa |
| dcterms.references | Krauss, C., Do, X. A., & Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689–702. https://doi.org/10.1016/j.ejor.2016.10.031 | spa |
| dcterms.references | Leung, S. C. H., Zhang, J., Lai, K. K., & Liu, N. (2018). A B2C e-commerce intelligent order fulfillment system for improving customer satisfaction. Computers & Industrial Engineering, 115, 459–471. https://doi.org/10.1016/j.cie.2017.12.021 | spa |
| dcterms.references | McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence. AI Magazine. | spa |
| dcterms.references | McKinsey & Company. (2023). El estado de la IA en 2023: El año clave de la IA generativa. https://www.mckinsey.com/featured-insights/destacados/el-estado-de-la-ia-en-2023- el-ano-clave-de-la-ia-generativa/es | spa |
| dcterms.references | Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1– 25. https://doi.org/10.1002/j.2158-1592.2001.tb00001.x | spa |
| dcterms.references | Mercojuris. (2024, 28 noviembre). OMC – Nuevo informe examina cómo la inteligencia artificial puede configurar el futuro del comercio internacional. Mercojuris. https://mercojuris.com/omc-nuevo-informe-examina-como-la-inteligencia-artificialpuede-configurar-el-futuro-del-comercio-internacional/?utm_source=chatgpt.com | spa |
| dcterms.references | Ministerio de Ciencia, Tecnología e Innovación. (2024). Hoja de Ruta: Adopción Ética y Sostenible de Inteligencia Artificial en Colombia. https://inteligenciaartificial.minciencias.gov.co/wp-content/uploads/2024/02/Hojade-Ruta-Adopcion-Etica-y-Sostenible-de-Inteligencia-Artificial-Colombia-1.pdf | spa |
| dcterms.references | Min, H. (2010). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications. | spa |
| dcterms.references | Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2). https://doi.org/10.1177/2053951716679679 | spa |
| dcterms.references | Organización Marítima Internacional. (2025). IA en puertos: la clave para una logística más eficiente. El Universal. https://www.eluniversal.com.co/economica/2025/03/31/inteligencia-artificial-asiimpulsa-la-logistica-portuaria/ | spa |
| dcterms.references | Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192– 210. https://doi.org/10.5465/amr.2018.0072 | spa |
| dcterms.references | Riascos Guerrero, J. A., Bravo Arroyave, J. S., & Galván Colonia, E. (2024). Estrategias basadas en inteligencia artificial para la gestión de inventarios en la cadena de suministro. Universidad Cooperativa de Colombia. | spa |
| dcterms.references | Riascos, J. A., Bravo, J. S., & Galván Colonia, E. (2025). Estrategias basadas en inteligencia artificial para la gestión de inventarios en la cadena de suministro. | spa |
| dcterms.references | Rodríguez Mayorga, S. (2022). El impacto de la inteligencia artificial en la sostenibilidad de la cadena de suministro: una revisión de literatura. Universidad El Bosque. | spa |
| dcterms.references | Rushton, A., Croucher, P., & Baker, P. (2017). The handbook of logistics and distribution management (5th ed.). Kogan Page. | spa |
| dcterms.references | Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson. | spa |
| dcterms.references | Salazar, C. L., & Cárdenas, P. (2020). La revisión de literatura como etapa fundamental en el proceso investigativo. Revista Arbitrada Interdisciplinaria Koinonía, 5(9), 33–49. https://doi.org/10.35381/rikk.v5i9.864 | spa |
| dcterms.references | Sebastian, R. M. (2022). El impacto de la inteligencia artificial en la sostenibilidad de la cadena de suministro: una revisión de literatura. https://repositorio.unbosque.edu.co/items/09f98f81-c545-40fb-8d94-ca8612de344c | spa |
| dcterms.references | Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699– 1710. https://doi.org/10.1016/j.jclepro.2008.04.020 | spa |
| dcterms.references | Tamayo Contreras, P., Maldonado Alcaraz, S., & Gutiérrez Rodríguez, Á. (2024). La inteligencia artificial y su impacto en la gestión de inventarios en la cadena de suministro. | spa |
| dcterms.references | Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006 | spa |
| dcterms.references | Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research. | spa |
| dcterms.references | Wamba, S. F., Gunasekaran, A., Dubey, R., Ngai, E. W. T., & Papadopoulos, T. (2020). Big data analytics and supply chain ambidexterity: Moderating role of environmental dynamism. Information & Management, 57(1), 103–117. https://doi.org/10.1016/j.im.2018.09.004 | spa |
| dcterms.references | Wieland, A. (2021). Dancing the supply chain: Toward transformative supply chain management. Journal of Supply Chain Management, 57(1), 58–73. https://doi.org/10.1111/jscm.12248 | spa |
| dcterms.references | World Bank. (s. f.). Informe anual 2023: Una nueva era de desarrollo. World Bank. https://documentos.bancomundial.org/es/publication/documentsreports/documentdetail/099405110112326811/secbos198ad43a01b00b143eb19e841 dfefb5df959e | spa |
| oaire.version | info:eu-repo/semantics/acceptedVersion | |
| sb.investigacion | Desarrollo organizacional | |
| sb.programa | Administración de Empresas | spa |
| sb.sede | Sede Barranquilla | spa |
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