Big Data-Driven Approaches to Geopolitical Risk and MNC De-risking Strategies

Authors

DOI:

https://doi.org/10.31181/jscda31202563

Keywords:

Big Data Analytics (BDA) , Geopolitical Risk, De-risking Strategies;, Global Value Chains (GVCs), Multinational Companies (MNCs); , Supply Chain Resilience , Predictive Analytics

Abstract

The transition to a multipolar world has intensified geopolitical risks, challenging multinational companies (MNCs) to protect and adapt their global value chains (GVCs). These risks—ranging from resource scarcity and energy insecurity to techno-nationalism, cyber threats, and disrupted transport corridors—have prompted a shift toward proactive de-risking strategies. This study investigates how Big Data Analytics (BDA) supports such strategies, enabling MNCs to enhance visibility, resilience, and strategic agility. Using a conceptual-analytical method, the research first classifies geopolitical risks into three core categories: resource access disruptions, technological isolation, and transportation vulnerabilities. These risks are mapped to specific GVC activities to identify critical points of exposure. The analysis then explores the role of BDA in mitigating these risks, focusing on three core capabilities: real-time decision-making, predictive analytics, and scenario modelling. Key BDA-enabled strategies are identified across four domains: supplier and market diversification, dynamic risk assessment and contingency planning, real-time visibility, and local adaptation. Tools such as Resilinc, FourKites, Riskmethods, and digital twins are examined to demonstrate their relevance to specific geopolitical threats. The findings show that while BDA is commonly applied for efficiency and forecasting, its potential as a strategic enabler for geopolitical risk mitigation remains underutilised. By aligning BDA functionalities with core MNC de-risking strategies, this study offers a practical framework for integrating digital solutions into GVC risk management. It also highlights how the convergence of BDA with other Industry 4.0 technologies—such as IoT, blockchain, and GIS—can further enhance resilience. The results contribute to both academic discourse and managerial practice by demonstrating that BDA is not merely a supportive tool, but a central component of strategic planning in an increasingly unstable global environment. 

Downloads

Download data is not yet available.

References

Stanojević, N. (2024). Leveraging Big Data Analytics to Strengthen Global Value Chains Amidst Geopolitical Crises. 6th Virtual International Conference Path to a Knowledge Society – Managing Risks and Innovation (PaKSoM 2024), Matematički institut SANU, October 21–22, 2024, pp. 87–94. https://doi.org/10.5281/zenodo.14693425

Rodrik, D. (2017). Straight talk on trade: Ideas for a sane world economy. Princeton University Press.

Gereffi, G. (2020). What does the COVID-19 pandemic teach us about global value chains? The case of medical supplies. Journal of International Business Policy, 3(3), 287–301.

Baldwin, R., & Freeman, R. (2021). Risks and global supply chains: What we know and what we need to know. NBER Working Paper No. 29444. http://www.nber.org/papers/w29444

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275

Stanojević, N. (2023). Western Balkans trade with Russia and EU amid the Ukrainian crisis – threats and opportunities. Review of International Affairs, 74(1187), 5–29. https://doi.org/10.18485/iipe_ria.2023.74.1187.1

Teece, D. J. (1984). Technology transfer by multinational firms: The resource cost of transferring technological know-how. The Economic Journal, 94(374), 242–261.

Tushman, M., & Anderson, P. (1986). Technological discontinuities and organizational environments. Administrative Science Quarterly, 31(3), 439–465.

U.S. Department of the Treasury. (2023). Treasury hardens sanctions with 130 new Russian evasion and military-industrial targets. https://home.treasury.gov/news/press-releases/jy1871

World Economic Forum (WEF). (2024). Why transport and supply chain ecosystems need to be cyber secured (Van Gogh, M., Beato, F., & Rohland, L., Eds.).

Synai. (2024). Attacks on the Red Sea: How does this impact global shipping? https://sinay.ai/en/impact-of-the-red-sea-attacks-on-shipping/

Christopher, M. (2005). Logistics and supply chain management: Creating value-adding networks (3rd ed.). Prentice Hall Financial Times.

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, 57(3), 829–846.

Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308–317.

Bag, S. (2017). Big data and predictive analysis is key to superior supply chain performance: A South African experience. International Journal of Information Systems and Supply Chain Management, 10(2), 66–84. https://doi.org/10.4018/IJISSCM.2017040104

Sang, G. M., Xu, L., & de Vrieze, P. (2021). A predictive maintenance model for flexible manufacturing in the context of Industry 4.0. Frontiers in Big Data, 4, Article 663466. https://doi.org/10.3389/fdata.2021.663466

Addo-Tenkorang, R., & Helo, P. T. (2016). Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528–543.

Chen, M. (2022). The influence of big data analysis of intelligent manufacturing under machine learning on start-ups enterprise. Enterprise Information Systems, 16(2), 347–316. https://doi.org/10.1080/17517575.2019.1694180

IBM. (2024). Leaning on automation and analytics to keep cyberthreats at bay 24x7. https://www.ibm.com/case-studies/askari-bank

Maersk. (2024). Technology at Maersk: Supply chain technology driving a paradigm shift. https://www.maersk.com/about/technology

Siemens. (2023). Predictive maintenance in ‘real-life’. https://blog.siemens.com/2023/08/predictive-maintenance-in-real-life/

Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 25, 443. https://doi.org/10.1111/poms.12838

Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: New trends and research directions. International Journal of Production Research, 59(1), 102–109. https://doi.org/10.1080/00207543.2021.1840148

Published

2025-07-19

How to Cite

Stanojevic, N. (2025). Big Data-Driven Approaches to Geopolitical Risk and MNC De-risking Strategies. Journal of Soft Computing and Decision Analytics, 3(1), 112-122. https://doi.org/10.31181/jscda31202563