El impacto de la inteligencia artificial en la gestión de riesgos de la cadena de suministro: una revisión focalizada de la literatura
DOI:
https://doi.org/10.26439/ddee2026.n008.8777Palabras clave:
inteligencia artificial, gestión de riesgos de la cadena de suministro , aprendizaje automático , analítica predictivaResumen
La creciente complejidad y volatilidad de las cadenas de suministro globales ha intensificado la necesidad de estrategias sólidas de gestión de riesgos apoyadas en tecnologías emergentes de la Industria 5.0. En este contexto, la inteligencia artificial (IA) se ha consolidado como una herramienta capaz de fortalecer la capacidad predictiva, optimizar la toma de decisiones y mejorar la resiliencia organizacional en las redes de suministro. El presente estudio desarrolla una revisión rápida de literatura basada en las directrices PRISMA con el objetivo de analizar el impacto de la IA en la gestión de riesgos de la cadena de suministro (SCRM). La revisión sintetiza hallazgos recientes sobre aplicaciones de aprendizaje automático, procesamiento de lenguaje natural y analítica predictiva en procesos de identificación, evaluación y mitigación de riesgos. Los resultados muestran que las tecnologías basadas en IA contribuyen significativamente a mejorar la visibilidad de riesgos, aumentar la precisión de los pronósticos y reducir los tiempos de respuesta frente a disrupciones. No obstante, persisten desafíos relacionados con la calidad de los datos, la transparencia algorítmica, las consideraciones éticas y los costos de implementación. Finalmente, el estudio plantea implicancias prácticas para las organizaciones y propone líneas de investigación futura orientadas a fortalecer la integración de la IA en las estrategias de gestión de riesgos de las cadenas de suministro.
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Referencias
Allahham, M., Sharabati, A. A. A., Al-Sager, M., Sabra, S., Awartani, L., & Khraim, A. S. L. (2024). Supply chain risks in the age of big data and artificial intelligence: The role of risk alert tools and managerial apprehensions. Uncertain Supply Chain Management, 12(1), 399-406. https://doi.org/10.5267/j.uscm.2023.9.012
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202. https://doi.org/10.1080/00207543.2018.1530476
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333(2), 627-652. https://doi.org/10.1007/s10479-021-03956-x
Bidin, M. F., Espinosa-Jaramillo, M. T., Castillo Martínez, D. C., Nik Hashim, N. A. A., Chauhan, S., & Karmode, S. (2024). Adaptive supply chain risk management using AI: Mitigating disruptions and enhancing resilience in the post-pandemic era. Tuijin Jishu/Journal of Propulsion Technology, 45(2). https://doi.org/10.52783/tjjpt.v45.i02.6294
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small- and medium-sized enterprises. International Journal of Production Research, 62(15), 5417-5456. https://doi.org/10.1080/00207543.2023.2179859
Edhrabooh, K. M., & Al-Alawi, A. I. (2024). AI and ML applications in supply chain management field: A systematic literature review. In Proceedings of the 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) (pp. 202-206). IEEE. https://doi.org/10.1109/ICETSIS61505.2024.10459449
Ejjami, R., & Boussalham, K. (2024). Resilient supply chains in Industry 5.0: Leveraging AI for predictive maintenance and risk mitigation. International Journal for Multidisciplinary Research, 6(4). https://doi.org/10.36948/ijfmr.2024.v06i04.25116
Ganann, R., Ciliska, D., & Thomas, H. (2010). Expediting systematic reviews: Methods and implications of rapid reviews. Implementation Science, 5, Article 56. https://doi.org/10.1186/1748-5908-5-56
Ganesh, A. D., & Kalpana, P. (2022). Future of artificial intelligence and its influence on supply chain risk management – A systematic review. Computers & Industrial Engineering, 169, Article 108206. https://doi.org/10.1016/j.cie.2022.108206
Golan, M. S., Jernegan, L. H., & Linkov, I. (2020). Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic. Environment Systems and Decisions, 40(2), 222-243. https://doi.org/10.1007/s10669-020-09777-w
Haby, M. M., Chapman, E., Clark, R., Barreto, J., Reveiz, L., & Lavis, J. N. (2016). Designing a rapid response program to support evidence-informed decision-making in the Americas region: Using the best available evidence and case studies. Implementation Science, 11(1), Article 117. https://doi.org/10.1186/s13012-016-0472-9
Ismaeil, M. K. A., & Lalla, A. F. (2024). The role and impact of artificial intelligence on supply chain management: Efficiency, challenges, and strategic implementation. Journal of Ecohumanism, 3(4), 89-106. https://doi.org/10.62754/joe.v3i4.3461
Jackson, I., Ivanov, D., Dolgui, A., & Namdar, J. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120-6145. https://doi.org/10.1080/00207543.2024.2309309
Jahin, M. A., Naife, S. A., Saha, A. K., & Mridha, M. F. (2024). AI in supply chain risk assessment: A systematic literature review and bibliometric analysis (v. 4). arXiv. https://doi.org/10.48550/arXiv.2401.10895
Kelly, S. E., Moher, D., & Clifford, T. J. (2016). Quality of conduct and reporting in rapid reviews: An exploration of compliance with PRISMA and AMSTAR guidelines. Systematic Reviews, 5(1), Article 79. https://doi.org/10.1186/s13643-016-0258-9
Liu, Z., Costa, C., & Wu, Y. (2024). Leveraging data-driven insights to enhance supplier performance and supply chain resilience. World Journal of Innovation and Modern Technology, 7(5), 125-131. https://doi.org/10.53469/wjimt.2024.07(05).15
Modgil, S., Gupta, S., Stekelorum, R., & Laguir, I. (2022). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management, 52(2), 130-149. https://doi.org/10.1108/IJPDLM-12-2020-0434
Moher, D., Shamseer, L., Clarke, M., Ghersi, D., Liberati, A., Petticrew, M., Shekelle, P., Stewart, L. A., & PRISMA-P Group. (2015). Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Systematic Reviews, 4(1), Article 1. https://doi.org/10.1186/2046-4053-4-1
Mukherjee, S., Baral, M. M., Nagariya, R., Chittipaka, V., & Pal, S. K. (2023). Artificial intelligence-based supply chain resilience for improving firm performance in emerging markets. Journal of Global Operations and Strategic Sourcing, 17(3), 516-540. https://doi.org/10.1108/JGOSS-06-2022-0049
Narayanan, N. S. P., Ghapar, F., Chew, L. L., Kaliani Sundram, V. P., M. Naidu, B., Zulfakar, M. H., & Daud, A. (2024). Artificial intelligence-powered risk assessment in supply chain safety. Information Management and Business Review, 16(3S(I)a), 107-114. https://doi.org/10.22610/imbr.v16i3S(I)a.4124
Nayal, K., Raut, R., Priyadarshinee, P., Narkhede, B. E., Kazancoglu, Y., & Narwane, V. (2022). Exploring the role of artificial intelligence in managing agricultural supply chain risk to counter the impacts of the COVID-19 pandemic. The International Journal of Logistics Management, 33(3), 744-772. https://doi.org/10.1108/IJLM-12-2020-0493
Nezianya, M. C., Adebayo, A. O., & Ezeliora, P. (2024). A critical review of machine learning applications in supply chain risk management. World Journal of Advanced Research and Reviews, 23(3), 1554-1567. https://doi.org/10.30574/wjarr.2024.23.3.2760
Nnaji, U. O., Benjamin, L. B., Eyo-Udo, N. L., & Etukudoh, E. A. (2024). Advanced risk management models for supply chain finance. World Journal of Advanced Research and Reviews, 22(2), 612-618. https://doi.org/10.30574/wjarr.2024.22.2.1444
Polisena, J., Garritty, C., Umscheid, C. A., Kamel, C., Samra, K., Smith, J., & Vosilla, A. (2015). Rapid Review Summit: An overview and initiation of a research agenda. Systematic Reviews, 4, Article 137. https://doi.org/10.1186/s13643-015-0111-6
Rane, N. L., Desai, P., Rane, J., & Paramesha, M. (2024). Artificial intelligence, machine learning, and deep learning for sustainable and resilient supply chain and logistics management. In D. Patil, N. L. Rane, P. Desai, & J. Rane (Eds.), Trustworthy artificial intelligence in industry and society (pp. 156-184). Deep Science Publishing. https://doi.org/10.70593/978-81-981367-4-9_5
Rauf, M. A., Jim, M. M. I., Rahman, M. M., & Tariquzzaman, M. (2024). AI-powered predictive analytics for intellectual property risk management in supply chain operations: A big data approach. International Journal of Science and Engineering, 1(4), 32-46. https://doi.org/10.62304/ijse.v1i04.184
Riad, M., Naimi, M., & Okar, C. (2024). Enhancing supply chain resilience through artificial intelligence: Developing a comprehensive conceptual framework for AI implementation and supply chain optimization. Logistics, 8(4), Article 111. https://doi.org/10.3390/logistics8040111
Smela, B., Toumi, M., Świerk, K., Francois, C., Biernikiewicz, M., Clay, E., & Boyer, L. (2023). Rapid literature review: Definition and methodology. Journal of Market Access & Health Policy, 11(1), Article 2241234. https://doi.org/10.1080/20016689.2023.2241234
Sodiya, E. O., Jacks, B. S., Ugwuanyi, E. D., Adeyinka, M. A., Umoga, U. J., Daraojimba, A. I., & Lottu, O. A. (2024). Reviewing the role of AI and machine learning in supply chain analytics. GSC Advanced Research and Reviews, 18(2), 312-320. https://doi.org/10.30574/gscarr.2024.18.2.0069
Thenmozhi, V., & Krisknakumari, S. (2024). Artificial intelligence in enhancing operational efficiency in logistics and SCM. International Journal of Scientific Research in Science and Technology, 11(5), 316-323. https://doi.org/10.32628/ijsrst24115107
Vandana, M., Naveena, M., Ellaturu, N., Kumari, T. L., Bambuwala, S., & Rajalakshmi, M. (2024). Ai-driven solutions for supply chain management. Journal of Informatics Education and Research, 4(2), 861-868. https://doi.org/10.52783/jier.v4i2.849
Wong, L.-W., Tan, G. W.-H., Ooi, K.-B., Lin, B., & Dwivedi, Y. K. (2024). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research, 62(15), 5535-5555. https://doi.org/10.1080/00207543.2022.2063089
Yassin, M. (2023). Artificial intelligence and machine learning uses in supply chain risk management. PsyArXiv. https://doi.org/10.31234/osf.io/ke7jr
Zhang, D. (2024). AI integration in supply chain and operations management: Enhancing efficiency and resilience. Applied and Computational Engineering, 90(1), 8-13. https://doi.org/10.54254/2755-2721/90/2024melb0060
