This article analyzes the challenges of supply chain management under conditions of geopolitical instability in the global economy and proposes a new conceptual approach to address them. While existing research primarily focuses on disruption forecasting and risk identification, the capabilities for autonomous decision-making remain insufficiently developed. In this study, an "Autonomous Self-Healing Supply Chain" model is developed, integrating big data analytics, causal modeling, and network-based approaches into a unified system. The results demonstrate that the proposed model not only enables early detection of disruptions but also allows for their automatic mitigation. This significantly enhances the resilience and adaptability of supply chains in a rapidly changing global environment.

Big Data, supply chain, geopolitical risk

Aamer A., Eka Yani L.P., Alan Priyatna I.M. Data analytics in the supply chain management: Review of machine learning applications in demand forecasting // Operations and Supply Chain Management: An International Journal. – 2020. – Vol. 14, No. 1. – P. 1–13.

Agrawal N., Cohen M.A., Deshpande R., Deshpande V. How machine learning will transform supply chain management // Harvard Business Review. – 2024. – Vol. 102, No. 2. – P. 66–75.

Aljohani A. Predictive analytics and machine learning for real-time supply chain risk mitigation and agility // Sustainability. – 2023. – Vol. 15, No. 20. – Article 15088.

Magazzino C., Gattone T., Giolli L. Dynamic interactions between oil prices and renewable energy production in Italy amid the COVID-19 pandemic: wavelet and machine learning analyses // Energy, Ecology and Environment. – 2024. – Vol. 9, No. 5. – P. 502–520.

Wang Y. Comparative analysis of AI-driven risk prediction methods in retail supply chain disruption management: A multi-enterprise study // Journal of Advanced Computing Systems. – 2024. – Vol. 4, No. 4. – P. 36–48.

Tandon P., Prasad Yadav M., Shore A., Fosso-Wamba S. Examining the Role of Artificial Intelligence in Predicting the Supply Chain Stocks During Crises Using Quantile VAR: A Way to Enhance Supply Chain Resilience // International Journal of Production Economics. – 2024. – Vol. 268.

Wang H., Sua L.S. Urban resilience amid supply chain disruptions: A causal and cointegration-based risk model for G-7 cities post-COVID-19 // Urban Science. – 2024. – Vol. 8, No. 4. – Article 223.

Nezianya M.C., Adebayo A.O., Ezeliora P. A critical review of machine learning applications in supply chain risk management // World Journal of Advanced Research & Reviews. – 2024. – Vol. 23, No. 3. – P. 1554–1567.

Ghadge A., Wurtmann H., Seuring S. Managing climate change risks in global supply chains: A review and research agenda // International Journal of Production Research. – 2020. – Vol. 58, No. 1. – P. 44–64.

Nnabueze S., Ike P. N., Olatunde-Thorpe J. et al. Supply Chain Disruption Forecasting Using Network Analytics // Journal of Frontiers in Multidisciplinary Research. – 2022. – Vol. 3, № 2. – P. 193–203.

Stanojević N. Leveraging Big Data Analytics to Strengthen Global Value Chains amidst Geopolitical Crises // Diplomacy and International Relations Review. – 2024. – Vol. 6, № 2. – P. 100–112.

Huang S. AI-Driven Early Warning Systems for Supply Chain Risk Detection: A Machine Learning Approach // Academic Journal of Computing & Information Science. – 2025. – Vol. 8, Issue 9. – P. 92–107.

Akinola O. Causal Machine Learning and Advanced Data Analytics for Supply Chain Disruption Analysis and Decision-Making // International Journal of Software Engineering & Applications. – 2025. – Vol. 14, № 6. – P. 143–154.

Lutfullayeva S.A. Katta ma'lumotlar xalqaro bozorda ta'minot zanjiri uzilishlarini prognozlash // International Scientific Journal: Learning and Teaching. – 2025. – Vol. 2, Issue 8. – B. 47–53.

https://www.worldbank.org/en/data/interactive/2025/04/08/globalsupply-chain-stress-index.