Intelligent Agents and Generative Artificial Intelligence in Enterprise Supply Chains: Mechanisms for Enhancing System Resilience in the Digital Economy

DOI: https://doi.org/10.62517/jmsd.202512502

Author(s)

Feng Shi*

Affiliation(s)

Baise University, Baise, Guangxi, China *Corresponding Author

Abstract

With the rapid development of the digital economy, enterprise supply chains are facing increasing complexity and uncertainty, creating an urgent need to enhance system resilience through intelligent technologies. This study aims to explore the application and underlying mechanisms of intelligent agents and generative artificial intelligence in enterprise supply chains. The research adopts a theoretical analysis approach, integrating resilience theory, dynamic capability theory, and complex network theory to examine how intelligent technologies enhance supply chain resilience through four mechanisms: information processing, collaborative optimization, risk response, and learning and evolution. The findings indicate that intelligent agents and generative artificial intelligence, through multidimensional synergy, not only strengthen supply chain information visualization, decision-making agility, and risk response capacity, but also achieve long-term adaptability through continuous learning and system optimization. The proposed integrative analytical framework reveals the logical relationships among these mechanisms, providing theoretical support for understanding the systemic role of intelligent technologies in improving supply chain resilience. The study concludes that the synergistic application of intelligent technologies can offer practical guidance for enterprises to build efficient, robust, and adaptive supply chain systems in the digital economy, while also providing a theoretical foundation for future empirical research and cross-industry technological applications.

Keywords

Intelligent Agents; Generative Artificial Intelligence; Supply Chain Resilience; Information Processing; Collaborative Optimization; Risk Response; Learning and Evolution

References

[1] Lin, Y., & Yu, J. (2023). Study on the modernization of supply chain management of the luxury industry in the context of the digital economy. Academic Journal of Management and Social Sciences, 4(1), 5–11. [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, 1–43. [3] Qi, Y., Li, L., & Kong, F. (2023). Research on the improvement path of prefabricated buildings’ supply chain resilience based on structural equation modeling: A case study of Shenyang and Hangzhou, China. Buildings, 13(11), 2801. [4] Li, Z. (2023). Strategies on establishing supply chain resilience in the era of VUCA. Academic Journal of Business & Management, 5(14), 126–131. [5] Ramirez, E. A. B., & Esparrell, J. A. F. (2024). Artificial intelligence (AI) in education: Unlocking the perfect synergy for learning. Educational Process: International Journal, 13(1), 35–51. [6] Xu, L., Proselkov, Y., Schoepf, S., Minarsch, D., Minaricova, M., & Brintrup, A. (2023). Implementation of autonomous supply chains for digital twinning: A multi-agent approach. IFAC-PapersOnLine, 56(2), 11076–11081. [7] Xu, L., Mak, S., Minaricova, M., & Brintrup, A. (2024). On implementing autonomous supply chains: A multi-agent system approach. Computers in Industry, 161, 104120. [8] Ramdurai, B., & Adhithya, P. (2023). The impact, advancements and applications of generative AI. International Journal of Computer Science and Engineering, 10(6), 1–8. [9] Doanh, D. C., Dufek, Z., Ejdys, J., Ginevičius, R., Korzynski, P., Mazurek, G., ... & Ziemba, E. (2023). Generative AI in the manufacturing process: Theoretical considerations. Engineering Management in Production and Services, 15(4), 7–17. [10]Gregory, J. M., & Gupta, S. K. (2023). Opportunities for generative artificial intelligence to accelerate deployment of human-supervised autonomous robots. Proceedings of the AAAI Symposium Series, 2(1), 177–181. [11]Kaczorowska-Spychalska, D., Mazurek, G., Kotula, N., & Sukowski, U. (2024). Generative AI as source of change of knowledge management paradigm. Human Technology, 20(1), 97–116.