PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS

Veronika Vyazovskaya; Olga Plyusnina; Oksana Falchenko

doi:10.29039/2409-6024-2026-14-9-244-264

VAK Russia 5.2.1 UDC 338.2 VAK Russia 5.2.3 VAK Russia 5.2.4 VAK Russia 5.2.5 VAK Russia 5.2.6 VAK Russia 5.2.7 UDC 658.7

8 more Collapse

PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS

Published in Russian Journal of Management · Volume 14, Issue 9, 2026 · Pages 244–264 · Rubric: Risk management

DOI: https://doi.org/10.29039/2409-6024-2026-14-9-244-264

Received: 06.10.2026 Accepted: 06.10.2026 Published: 20.10.2026

Authors

Veronika Vyazovskaya

Olga Plyusnina

Oksana Falchenko

Veronika Vyazovskaya

Academic degree: candidate of economic sciences

Ural'skiy gosudarstvennyy ekonomicheskiy universitet docent · kafedra regional'noy, municipal'noy ekonomiki i upravleniya · Russian Federation · Екатеринбург

Olga Plyusnina

Academic degree: candidate of economic sciences

Ural'skiy gosudarstvennyy ekonomicheskiy universitet docent · kafedra regional'noy, municipal'noy ekonomiki i upravleniya · Russian Federation · Екатеринбург

Oksana Falchenko

Academic degree: candidate of economic sciences

Ural'skiy gosudarstvennyy ekonomicheskiy universitet docent · kafedra regional'noy, municipal'noy ekonomiki i upravleniya · Russian Federation

ABSTRACT

The increasing complexity of international supply chains, the growing number of interdependent participants, geopolitical and regulatory changes, transport disruptions, climate-related events, and demand volatility limit the effectiveness of traditional risk management approaches in which managerial intervention often begins only after a disruption has occurred or reached a critical level. The purpose of this study is to develop methodological provisions for predictive risk management in international supply chains and to identify the conditions under which data-driven and algorithmic methods enable a transition from recording deviations to their early detection and prevention. The methodological framework combines a comparative analysis of academic publications, the systematization of empirical studies on the application of machine learning, Bayesian and graph-based models, the Internet of Things, and digital twins, as well as a comparison of the performance indicators of existing early warning systems. It is proposed to distinguish between monitoring, predictive, predictive and prescriptive management, since obtaining a forecast by itself does not provide risk reduction if the warning is not related to a specific time interval for making a decision. A five-stage predictive management model has been developed. The methods of measuring the efficiency of the predictive system are substantiated. Conclusions are drawn about the main limitations of predictive management.

KEYWORDS

international supply chains, risk management, predictive analytics, machine learning, artificial intelligence, early warning, digital twin, graph neural networks, supply chain resilience, cascading risk propagation

1. Baryannis G., Validi S., Dani S., Antoniou G. Supply chain risk management and artificial intelligence: state of the art and future research directions // International Journal of Production Research. 2019. Vol. 57. No. 7. P. 2179–2202. DOI:https://doi.org/10.1080/00207543.2018.1530476. URL: https://keele-repository.worktribe.com/output/416896/supply-chain-risk-management-and-artificial-intelligence-state-of-the-art-and-future-research-directions (data obrascheniya: 13.09.2026).

2. Toorajipour R., Sohrabpour V., Nazarpour A., Oghazi P., Fischl M. Artificial intelligence in supply chain management: A systematic literature review // Journal of Business Research. 2021. Vol. 122. P. 502–517. DOI:https://doi.org/10.1016/j.jbusres.2020.09.009. URL: https://www.sciencedirect.com/science/article/pii/S014829632030583X (data obrascheniya: 13.09.2026).

3. Yang M., Lim M. K., Qu Y., Ni D., Xiao Z. Supply chain risk management with machine learning technology: A literature review and future research directions // Computers & Industrial Engineering. 2023. Vol. 175. Art. 108859. DOI:https://doi.org/10.1016/j.cie.2022.108859. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC9715461/ (data obrascheniya: 13.09.2026).

4. Jia F., Hu S., He Q., Chen L., Xing X. The Role of Artificial Intelligence in Supply Chain Risk Management: A Literature Review and a Conceptual Framework // IEEE Transactions on Engineering Management. 2026. Vol. 73. P. 3264–3277. DOI:https://doi.org/10.1109/TEM.2026.3683693. URL: https://livrepository.liverpool.ac.uk/3199215/ (data obrascheniya: 13.09.2026).

5. Culot G., Podrecca M., Nassimbeni G. Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions // Computers in Industry. 2024. Vol. 162. Art. 104132. DOI:https://doi.org/10.1016/j.compind.2024.104132. URL: https://www.research.unipd.it/handle/11577/3547194 (data obrascheniya: 13.09.2026).

6. Brintrup A., Pak J., Ratiney D., Pearce T., Wichmann P., Woodall P., McFarlane D. Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing // International Journal of Production Research. 2020. Vol. 58. No. 11. P. 3330–3341. DOI:https://doi.org/10.1080/00207543.2019.1685705. URL: https://www.repository.cam.ac.uk/items/eb5de32e-7f80-4145-9dfc-9dcb5b0d583c (data obrascheniya: 13.09.2026).

7. Baryannis G., Dani S., Antoniou G. Predicting supply chain risks using machine learning: The trade-off between performance and interpretability // Future Generation Computer Systems. 2019. Vol. 101. P. 993–1004. DOI:https://doi.org/10.1016/j.future.2019.07.059. URL: https://keele-repository.worktribe.com/output/416890/predicting-supply-chain-risks-using-machine-learning-the-trade-off-between-performance-and-interpretability (data obrascheniya: 13.09.2026).

8. Tsang Y. P., Choy K. L., Wu C. H., Ho G. T. S., Lam C. H. Y., Koo P. S. An Internet of Things (IoT)-based risk monitoring system for managing cold supply chain risks // Industrial Management & Data Systems. 2018. Vol. 118. No. 7. P. 1432–1462. DOI:https://doi.org/10.1108/IMDS-09-2017-0384. URL: https://ira.lib.polyu.edu.hk/handle/10397/104473?mode=simple (data obrascheniya: 13.09.2026).

9. Qazi A., Dickson A., Quigley J., Gaudenzi B. Supply chain risk network management: A Bayesian belief network and expected utility based approach for managing supply chain risks // International Journal of Production Economics. 2018. Vol. 196. P. 24–42. DOI:https://doi.org/10.1016/j.ijpe.2017.11.008. URL: https://strathprints.strath.ac.uk/62225/ (data obrascheniya: 13.09.2026).

10. Hosseini S., Ivanov D. Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review // Expert Systems with Applications. 2020. Vol. 161. Art. 113649. DOI:https://doi.org/10.1016/j.eswa.2020.113649. URL: https://pmc.ncbi.nlm.nih.gov/articles/PMC7305519/ (data obrascheniya: 13.09.2026).

11. Cavalcante I. M., Frazzon E. M., Forcellini F. A., Ivanov D. A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing // International Journal of Information Management. 2019. Vol. 49. P. 86–97. DOI:https://doi.org/10.1016/j.ijinfomgt.2019.03.004. URL: https://www.researchgate.net/publication/332057326_A_supervised_machine_learning_approach_to_data-driven_simulation_of_resilient_supplier_selection_in_digital_manufacturing (data obrascheniya: 13.09.2026).

12. Modgil S., Singh R. K., Hannibal C. Artificial intelligence for supply chain resilience: learning from Covid-19 // The International Journal of Logistics Management. 2022. Vol. 33. No. 4. P. 1246–1268. DOI:https://doi.org/10.1108/IJLM-02-2021-0094. URL: https://rgu-repository.worktribe.com/output/2120330/artificial-intelligence-for-supply-chain-resilience-learning-from-covid-19 (data obrascheniya: 13.09.2026).

13. Aljohani A. Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility // Sustainability. 2023. Vol. 15. No. 20. Art. 15088. DOI:https://doi.org/10.3390/su152015088. URL: https://www.mdpi.com/2071-1050/15/20/15088 (data obrascheniya: 13.09.2026).

14. Cimino A., Longo F., Mirabelli G., Solina V. A cyclic and holistic methodology to exploit the Supply Chain Digital Twin concept towards a more resilient and sustainable future // Cleaner Logistics and Supply Chain. 2024. Vol. 11. Art. 100154. DOI:https://doi.org/10.1016/j.clscn.2024.100154. URL: https://www.sciencedirect.com/science/article/pii/S2772390924000167 (data obrascheniya: 13.09.2026).

15. Ogunsoto O. V., Olivares-Aguila J., ElMaraghy W. A conceptual digital twin framework for supply chain recovery and resilience // Supply Chain Analytics. 2025. Vol. 9. Art. 100091. DOI:https://doi.org/10.1016/j.sca.2024.100091. URL: https://www.sciencedirect.com/science/article/pii/S2949863524000347 (data obrascheniya: 13.09.2026).

16. Hezam I. M., Ali A. M., Sallam K. et al. Digital twin and fuzzy framework for supply chain sustainability risk assessment and management in supplier selection // Scientific Reports. 2024. Vol. 14. Art. 17718. DOI:https://doi.org/10.1038/s41598-024-67226-z. URL: https://www.nature.com/articles/s41598-024-67226-z (data obrascheniya: 13.09.2026).

17. Yang S., Ogawa Y., Ikeuchi K., Shibasaki R., Okuma Y. Post-hazard supply chain disruption: Predicting firm-level sales using graph neural network // International Journal of Disaster Risk Reduction. 2024. Vol. 110. Art. 104664. DOI:https://doi.org/10.1016/j.ijdrr.2024.104664. URL: https://www.sciencedirect.com/science/article/pii/S2212420924004266 (data obrascheniya: 13.09.2026).

18. Xi D., Nie V., Li W. Application of the LightGBM algorithm in cross-border supply chain risk management: prediction and mitigation strategy development // Scientific Reports. 2026. Vol. 16. Art. 16194. DOI:https://doi.org/10.1038/s41598-026-47327-7. URL: https://www.nature.com/articles/s41598-026-47327-7 (data obrascheniya: 13.09.2026).

19. Liu Z., Chen Y. Construction and Empirical Evidence of Supply Chain Disruption Risk Early Warning Model Based on Machine Learning Algorithms // Procedia Computer Science. 2026. Vol. 279. P. 1260–1269. DOI:https://doi.org/10.1016/j.procs.2026.03.345. URL: https://www.sciencedirect.com/science/article/pii/S1877050926009683 (data obrascheniya: 13.09.2026).

20. Zhao T., Chen G., Busababodhin P. An intelligent expert system for logistics disruption prediction and mitigation in global supply chains: Integrating graph-based risk inference with ensemble forecasting // Scientific Reports. 2026. DOI:https://doi.org/10.1038/s41598-026-61617-0. URL: https://www.nature.com/articles/s41598-026-61617-0 (data obrascheniya: 13.09.2026).

21. Khan M. R. I. AI-powered predictive analytics for enhancing supply chain resilience and delivery outcomes // Discover Analytics. 2026. Vol. 4. Art. 13. DOI:https://doi.org/10.1007/s44257-026-00065-6. URL: https://link.springer.com/article/10.1007/s44257-026-00065-6 (data obrascheniya: 13.09.2026).

22. Mirgorodskaya M. G., Polyanskiy O. V., Kapustina N. V. Antikrizisnyy menedzhment i upravlenie riskami preryvaniya cepey postavok // Vestnik Evraziyskoy nauki. 2025. T. 17. № S6. URL: https://cyberleninka.ru/article/n/antikrizisnyy-menedzhment-i-upravlenie-riskami-preryvaniya-tsepey-postavok (data obrascheniya: 13.09.2026).

23. OECD. OECD Supply Chain Resilience Review: Navigating Risks. Paris: OECD Publishing, 2025. 184 p. DOI:https://doi.org/10.1787/94e3a8ea-en. URL: https://www.oecd.org/content/dam/oecd/en/publications/reports/2025/06/oecd-supply-chain-resilience-review_9930d256/94e3a8ea-en.pdf (data obrascheniya: 13.09.2026).

24. World Economic Forum. The Global Risks Report 2026. 21st ed. Geneva: World Economic Forum, 2026. 102 p. URL: https://www.weforum.org/publications/global-risks-report-2026/ (data obrascheniya: 13.09.2026).

25. Arvis J.-F., Ojala L., Shepherd B., Ulybina D., Wiederer C. Connecting to Compete 2023: Trade Logistics in an Uncertain Global Economy. The Logistics Performance Index and Its Indicators. Washington, DC: World Bank, 2023. URL: https://lpi.worldbank.org/sites/default/files/2023-04/LPI_2023_report_with_layout.pdf (data obrascheniya: 13.09.2026).

This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International

JATS XML

Chief editor

Aleksandr Suglobov

a_suglobov@mail.ru

Deputy Editor-in-chief

Aleksey S. Mindrin

Publishing editor

Nadezhda Troitskaya

nn2209@mail.ru +7(915)259-03-86

This work is licensed under Creative Commons Attribution-NonCommercial 4.0 International Creative Commons

GOST

Vyazovskaya V. V., Plyusnina O. M., Falchenko O. D. PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS // Russian Journal of Management. 2026. no. 9. pp. 244-264. DOI: https://doi.org/10.29039/2409-6024-2026-14-9-244-264. (Date of access 22.10.2026).

MLA

Veronika Vyazovskaya, Plyusnina O. M., Falchenko O. D.. "PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS." Russian Journal of Management 14.9 (2026): 244-264. DOI: https://doi.org/10.29039/2409-6024-2026-14-9-244-264.

APA

Vyazovskaya V. V., Plyusnina O. M., Falchenko O. D. (2026). PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS. Russian Journal of Management, 14(9), 244-264. https://doi.org/10.29039/2409-6024-2026-14-9-244-264

Chicago

Veronika Vyazovskaya, Plyusnina O. M., Falchenko O. D.. "PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS." Russian Journal of Management 14, no. 9 (2026): 244-264. https://doi.org/10.29039/2409-6024-2026-14-9-244-264.

WIKI

{{cite journal | author = Vyazovskaya V. V., Plyusnina O. M., Falchenko O. D. | title = PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS | journal = Russian Journal of Management | year = 2026 | volume = 14 | issue = 9 | pages = 244-264 | doi = 10.29039/2409-6024-2026-14-9-244-264 }}

Copy

Export: BibTeX EndNote RefMan RusMARC

PREDICTIVE RISK MANAGEMENT IN INTERNATIONAL SUPPLY CHAINS

Confirmation