El rol de la inteligencia artificial en la automatización y la gestión de la cadena de suministro
Palabras clave:
Inteligencia artificial; automatización; cadena de suministro; transformación digital; ingeniería industrial.
Resumen
El objetivo de este estudio es analizar el rol de la inteligencia artificial (IA) en la transformación digital de la ingeniería industrial, con un enfoque en la automatización y la gestión de la cadena de suministro. A través de una revisión sistemática de la literatura y un análisis bibliométrico, se identifican las tendencias de investigación, los beneficios clave y las tecnologías más influyentes en este campo. Los resultados muestran que la IA está revolucionando la ingeniería industrial al optimizar procesos, mejorar la eficiencia operativa y reducir costos. Sin embargo, su implementación también plantea desafíos relacionados con la capacitación del personal, la transparencia de los algoritmos y el impacto ambiental. El estudio concluye que, para aprovechar plenamente el potencial de la IA, las empresas deben adoptar un enfoque integral que considere no solo los aspectos tecnológicos, sino también los organizativos, éticos y ambientales.
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Alharbi, F., Gufran, K., Alqerban, A., Alqahtani, A. S., Asiri, S. N., & Almutairi, A. (2024). Evaluation of Compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Guidelines for Conducting and Reporting Systematic Reviews in Three Major Periodontology Journals. The Open Dentistry Journal, 18(1), e18742106327727. https://doi.org/10.2174/0118742106327727240905095525
AlRushood, M. A., Rahbar, F., Selim, S. Z., & Dweiri, F. (2023). Accelerating Use of Drones and Robotics in Post-Pandemic Project Supply Chain. Drones, 7(5). Scopus. https://doi.org/10.3390/drones7050313
Boujarra, M., Lechhab, A., Al Karkouri, A., Zrigui, I., Fakhri, Y., & Bourekkadi, S. (2024). REVOLUTIONIZING LOGISTICS THROUGH DEEP LEARNING: INNOVATIVE SOLUTIONS TO OPTIMIZE DATA SECURITY. Journal of Theoretical and Applied Information Technology, 102(4), 1593-1607. Scopus.
Chauhan, S., Singh, R., Gehlot, A., Akram, S. V., Twala, B., & Priyadarshi, N. (2023). Digitalization of Supply Chain Management with Industry 4.0 Enabling Technologies: A Sustainable Perspective. Processes, 11(1), Article 1. https://doi.org/10.3390/pr11010096
Chen, Y., Biswas, M. I., & Talukder, M. S. (2022). The role of artificial intelligence in effective business operations during COVID-19. International Journal of Emerging Markets, 18(12), 6368-6387. https://doi.org/10.1108/IJOEM-11-2021-1666
Dogru, A. K., & Keskin, B. B. (2020). AI in operations management: Applications, challenges and opportunities. Journal of Data, Information and Management, 2(2), 67-74. https://doi.org/10.1007/s42488-020-00023-1
Dong, Z., Liang, W., Liang, Y., Gao, W., & Lu, Y. (2022). Blockchained supply chain management based on IoT tracking and machine learning. EURASIP Journal on Wireless Communications and Networking, 2022(1), 127. https://doi.org/10.1186/s13638-022-02209-0
Fosso Wamba, S., Guthrie, C., Queiroz, M. M., & Minner, S. (2024). ChatGPT and generative artificial intelligence: An exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 62(16), 5676-5696. https://doi.org/10.1080/00207543.2023.2294116
Gezdur, A., & Bhattacharjya, J. (2025). Innovators and transformers: Enhancing supply chain employee training with an innovative application of a large language model. International Journal of Physical Distribution & Logistics Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/IJPDLM-12-2023-0492
Gobinath, T., Anitha Mary X, Maheshwari, S., Madhavi, N. B., Rafeeq, M., & Kannan, G. (2024). Improved Supply Chain Management in E-Pharmacy Supply Chain Using Machine Learning Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), Article 7s.
Hosseini, M.-S., Jahanshahlou, F., Akbarzadeh, M. A., Zarei, M., & Vaez-Gharamaleki, Y. (2024). Formulating research questions for evidence-based studies. Journal of Medicine, Surgery, and Public Health, 2, 100046. https://doi.org/10.1016/j.glmedi.2023.100046
Katiyar, S., & Farhana, A. (2021). Smart Agriculture: The Future of Agriculture using AI and IoT. Journal of Computer Science, 17(10), 984-999. https://doi.org/10.3844/jcssp.2021.984.999
Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021). Opportunities of Artificial Intelligence and Machine Learning in the Food Industry. Journal of Food Quality, 2021(1), 4535567. https://doi.org/10.1155/2021/4535567
Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276. https://doi.org/10.1016/j.jik.2022.100276
Mohammed, S., Fiaidhi, J., & Kudadiya, R. (2023). Integrating a PICO Clinical Questioning to the QL4POMR Framework for Building Evidence-Based Clinical Case Reports. 2023 IEEE International Conference on Big Data (BigData), 4940-4947. https://doi.org/10.1109/BigData59044.2023.10386854
Moskvichenko, I., Stadnik, V., & Kushnir, L. (2024). IMPROVEMENT OF THE QUALITY MANAGEMENT SYSTEM IN THE TRANSPORT AND LOGISTICS SECTOR. Baltic Journal of Economic Studies, 10, 301-309. https://doi.org/10.30525/2256-0742/2024-10-4-301-309
Oh, A.-S. (2019). Designing smart supplier chain management model under big data and internet of things environment. International Journal of Recent Technology and Engineering, 8(2 Special Issue 6), 290-294. Scopus. https://doi.org/10.35940/ijrte.B1055.0782S619
Oliveira, M., Chauhan, S., Pereira, F., Felgueiras, C., & Carvalho, D. (2023). Blockchain Protocols and Edge Computing Targeting Industry 5.0 Needs. Sensors, 23(22), Article 22. https://doi.org/10.3390/s23229174
Papagiannidis, S., Bourlakis, M., & See-To, E. (2019). Social media in supply chains and logistics: Contemporary trends and themes. International Journal of Business Science and Applied Management, 14, 17-34. https://doi.org/10.69864/ijbsam.14-1.133
Rakholia, R., Suárez-Cetrulo, A., Singh, M., & Carbajo, R. (2024). Advancing Manufacturing Through Artificial Intelligence: Current Landscape, Perspectives, Best Practices, Challenges and Future Direction. IEEE Access, PP, 1-1. https://doi.org/10.1109/ACCESS.2024.3458830
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 4. https://doi.org/10.3390/logistics8040111
S, D. H. K., Kotehal, P. U., Sandesh, M. M., Reddy, D. Y. M., Roopa, D. K., & R, D. L. G. (2024). Artificial Intelligence in Supply Chain Management: Trends and Implications. Nanotechnology Perceptions, 1113-1120. https://doi.org/10.62441/nano-ntp.vi.1574
Shamsuzzoha, A., & Pelkonen, S. (2025). A robotic process automation model for order-handling optimization in supply chain management. Supply Chain Analytics, 9, 100102. https://doi.org/10.1016/j.sca.2025.100102
Sharabati, A., Awawdeh, H., Sabra, S., Shehadeh, H., Allahham, M., & Ali, A. (2024). The role of artificial intelligence on digital supply chain in industrial companies mediating effect of operational efficiency. Uncertain Supply Chain Management, 12(3), 1867-1878.
Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from Artificial-Intelligence-based systems in professional service operations. Journal of Operations Management, 68(6-7), 592-618. https://doi.org/10.1002/joom.1215
Van der Elst, W. (2024). The R Programming Language. En W. Van der Elst (Ed.), Regression-Based Normative Data for Psychological Assessment: A Hands-On Approach Using R (pp. 21-43). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-50951-3_2
Wu, H., Liu, J., & Liang, B. (2024). AI-Driven Supply Chain Transformation in Industry 5.0: Enhancing Resilience and Sustainability. Journal of the Knowledge Economy. https://doi.org/10.1007/s13132-024-01999-6
Xie, Y., Zheng, J., Gou, A., Sattar, F., & Liao, L. (2025). Log End Face Feature Extraction and Matching Method Based on Swin Transformer V2. Forests, 16(1), Article 1. https://doi.org/10.3390/f16010124
Xu, L., Mak, S., & Brintrup, A. (2021). Will bots take over the supply chain? Revisiting agent-based supply chain automation. International Journal of Production Economics, 241, 108279. https://doi.org/10.1016/j.ijpe.2021.108279
Zdravković, M., Panetto, H., & Weichhart, G. (2022). AI-enabled Enterprise Information Systems for Manufacturing. Enterprise Information Systems, 16(4), 668-720. https://doi.org/10.1080/17517575.2021.1941275
Publicado
2025-02-28
Cómo citar
Burgos Zambrano , V. R., Zambrano Mieles, J. D., & Mieles Cevallos, D. (2025). El rol de la inteligencia artificial en la automatización y la gestión de la cadena de suministro. GADE: Revista Científica, 5(1), 390-414. Recuperado a partir de https://revista.redgade.com/index.php/Gade/article/view/607
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Derechos de autor 2025 Velasco Rigoberto Burgos Zambrano ,Jael Dolores Zambrano Mieles,Dolores Mieles Cevallos
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GADE: Revista Científica, eISSN: 2745-2891, Tel: (+57) 3143134045, E-mail: gaderevistacientifica@gmail.com
