AI-Driven Supply Chain Management Systems for Resilient and Sustainable Operations: The Role of Digital Twins, IoT, and Predictive Analytics

Global supply chains are passing through a period of unusual volatility, in which lean, efficiency-first models have repeatedly failed to absorb systemic shocks such as pandemics and geopolitical disruption. This paper reviews how artificial intelligence (AI) and three of its closest enabling technologies, namely the Internet of Things (IoT), digital twins, and predictive analytics, are being used to build supply chains that are both more resilient and more sustainable. Drawing on peer-reviewed journal research published within the last five years, the study brings together streams of work that are usually examined separately and asks how they fit together within the emerging vision of Industry 5.0 and increasingly autonomous supply chains. The synthesis indicates that AI shifts supply chain management from a reactive posture to an anticipatory one. IoT supplies the real-time visibility that supports proactive risk management, predictive and prescriptive analytics turn that data into early warning of demand shifts and disruption and into recommended action, digital twins allow managers to stress-test recovery options before acting on them, and blockchain adds a layer of trust to the data exchanged between partners. The review finds that the value of these technologies appears when they are integrated into a coherent, layered architecture rather than adopted in isolation, and that their contribution to resilience runs through underlying capabilities such as visibility, flexibility, and collaboration rather than through the technology alone. It also examines the obstacles that continue to slow adoption, including implementation cost, cybersecurity exposure, a persistent digital skills gap, interoperability with legacy systems, and the limited interpretability of complex models. The paper proposes a layered reference architecture for autonomous supply chains and sets out recommendations for practitioners and policymakers seeking a technology-driven, human-centric, and environmentally responsible supply network.

Artificial Intelligence; Digital Twins; Internet of Things; Predictive Analytics

1. Aljohani, A. (2023). Predictive analytics and machine learning for real-time supply chain risk mitigation and agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088 [Google Scholar] [Crossref]

2. Babai, M. Z., Boylan, J. E., & Rostami-Tabar, B. (2022). Demand forecasting in supply chains: A review of aggregation and hierarchical approaches. International Journal of Production Research, 60(1), 324–348. [Google Scholar] [Crossref]

3. Burgos, D., & Ivanov, D. (2021). Food retail supply chain resilience and the COVID-19 pandemic: A digital twin-based impact analysis and improvement directions. Transportation Research Part E: Logistics and Transportation Review, 152, 102412. https://doi.org/10.1016/j.tre.2021.102412 [Google Scholar] [Crossref]

4. Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132 [Google Scholar] [Crossref]

5. Deiva Ganesh, A., & Kalpana, P. (2022). Future of artificial intelligence and its influence on supply chain risk management: A systematic review. Computers & Industrial Engineering, 169, 108206. [Google Scholar] [Crossref]

6. Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovation, 7(5), 93. https://doi.org/10.3390/asi7050093 [Google Scholar] [Crossref]

7. Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., & Foropon, C. (2022). Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view. International Journal of Production Economics, 250, 108618. [Google Scholar] [Crossref]

8. Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., Foropon, C., & Papadopoulos, T. (2023). Dynamic digital capabilities and supply chain resilience: The role of government effectiveness. International Journal of Production Economics, 258, 108790. [Google Scholar] [Crossref]

9. El Baz, J., & Ruel, S. (2021). Can supply chain risk management practices mitigate the disruption impacts on supply chains’ resilience and robustness? Evidence from an empirical survey in a COVID-19 outbreak era. International Journal of Production Economics, 233, 107972. [Google Scholar] [Crossref]

10. Guo, D., & Mantravadi, S. (2025). The role of digital twins in lean supply chain management: Review and research directions. International Journal of Production Research, 63(5), 1851–1872. [Google Scholar] [Crossref]

11. Hu, H., Xu, J., Liu, M., & Lim, M. K. (2023). Vaccine supply chain management: An intelligent system utilizing blockchain, IoT and machine learning. Journal of Business Research, 156, 113480. https://doi.org/10.1016/j.jbusres.2022.113480 [Google Scholar] [Crossref]

12. Iftikhar, A., Ali, I., Arslan, A., & Tarba, S. (2024). Digital innovation, data analytics, and supply chain resiliency: A bibliometric-based systematic literature review. Annals of Operations Research, 333(2), 825–848. [Google Scholar] [Crossref]

13. Ivanov, D. (2023). The Industry 5.0 framework: Viability-based integration of the resilience, sustainability, and human-centricity perspectives. International Journal of Production Research, 61(5), 1683–1695. [Google Scholar] [Crossref]

14. Ivanov, D. (2024). Two views of supply chain resilience. International Journal of Production Research, 62(11), 4031–4045. [Google Scholar] [Crossref]

15. Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450 [Google Scholar] [Crossref]

16. Jackson, I. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62, 6120–6145. https://doi.org/10.1080/00207543.2024.2309309 [Google Scholar] [Crossref]

17. Kouhizadeh, M., Saberi, S., & Sarkis, J. (2021). Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. International Journal of Production Economics, 231, 107831. [Google Scholar] [Crossref]

18. Marinagi, C., Reklitis, P., Trivellas, P., & Sakas, D. (2023). The impact of Industry 4.0 technologies on key performance indicators for a resilient supply chain 4.0. Sustainability, 15(6), 5185. https://doi.org/10.3390/su15065185 [Google Scholar] [Crossref]

19. Nayal, K., Raut, R. D., Queiroz, M. M., Yadav, V. S., & Narkhede, B. E. (2021). Are artificial intelligence and machine learning suitable to tackle the COVID-19 impacts? An agriculture supply chain perspective. The International Journal of Logistics Management, 34(2), 304–335. [Google Scholar] [Crossref]

20. Nimmy, S. F., Hussain, O. K., Chakrabortty, R. K., Hussain, F. K., & Saberi, M. (2022). Explainability in supply chain operational risk management: A systematic literature review. Knowledge-Based Systems, 235, 107587. [Google Scholar] [Crossref]

21. Nozari, H., Szmelter-Jarosz, A., & Ghahremani-Nahr, J. (2022). Analysis of the challenges of Artificial Intelligence of Things (AIoT) for the smart supply chain (case study: FMCG industries). Sensors, 22(8), 2931. https://doi.org/10.3390/s22082931 [Google Scholar] [Crossref]

22. Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250 [Google Scholar] [Crossref]

23. Rahman, T., Paul, S. K., Shukla, N., Agarwal, R., & Taghikhah, F. (2022). Supply chain resilience initiatives and strategies: A systematic review. Computers & Industrial Engineering, 170, 108317. https://doi.org/10.1016/j.cie.2022.108317 [Google Scholar] [Crossref]

24. 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), 111. https://doi.org/10.3390/logistics8040111 [Google Scholar] [Crossref]

25. Riahi, Y., Saikouk, T., Gunasekaran, A., & Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. Expert Systems with Applications, 173, 114702. https://doi.org/10.1016/j.eswa.2021.114702 [Google Scholar] [Crossref]

26. Richey, R. G., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532–549. https://doi.org/10.1111/jbl.12364 [Google Scholar] [Crossref]

27. Roman, E.-A., Stere, A.-S., Roşca, E., Radu, A.-V., Codroiu, D., & Anamaria, I. (2025). State of the art of digital twins in improving supply chain resilience. Logistics, 9(1), 22. https://doi.org/10.3390/logistics9010022 [Google Scholar] [Crossref]

28. Sallam, K., Mohamed, M., & Wagdy Mohamed, A. (2023). Internet of Things (IoT) in supply chain management: Challenges, opportunities, and best practices. Sustainable Machine Intelligence Journal, 2(3), 1–32. https://doi.org/10.61185/SMIJ.2023.22103 [Google Scholar] [Crossref]

29. Samuels, A. (2025). Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review. Frontiers in Artificial Intelligence, 7, 1477044. https://doi.org/10.3389/frai.2024.1477044 [Google Scholar] [Crossref]

30. Saurabh, S., & Dey, K. (2021). Blockchain technology adoption, architecture, and sustainable agri-food supply chains. Journal of Cleaner Production, 284, 124731. https://doi.org/10.1016/j.jclepro.2020.124731 [Google Scholar] [Crossref]

31. Shahzadi, G., Jia, F., Chen, L., & John, A. (2024). AI adoption in supply chain management: A systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125–1150. https://doi.org/10.1108/JMTM-09-2023-0431 [Google Scholar] [Crossref]

32. Smyth, C., Dennehy, D., Fosso Wamba, S., Scott, M., & Harfouche, A. (2024). Artificial intelligence and prescriptive analytics for supply chain resilience: A systematic literature review and research agenda. International Journal of Production Research, 62(23), 8537–8561. https://doi.org/10.1080/00207543.2024.2341415 [Google Scholar] [Crossref]

33. Stewart, M., & Ivanov, D. (2022). Design redundancy in agile and resilient humanitarian supply chains. Annals of Operations Research, 319(1), 633–659. [Google Scholar] [Crossref]

34. Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., & Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009 [Google Scholar] [Crossref]

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