Strengthening U.S. Supply Chain Resilience Through Data-Driven Reverse Logistics Optimization: A Framework for Resource Sovereignty, Defense Readiness, and Economic Stability

Pandemic, geopolitical conflict, trade restrictions and resource shortages have highlighted the fragility of the global supply chain. The United States, however, has begun to emphasize supply chain resilience as one of the issues of economic security and national defense. Most resilience programs are targeted at the forward logistics operations, while reverse logistics is still a largely untapped strategic capability. This paper explores how data-driven reverse logistics optimization can help improve U.S. supply chain resilience through improved resource recovery, defense readiness and economic stability. This study introduces a holistic framework to increase the efficiency and effectiveness of reverse logistics networks by combining predictive analytics, AI, intelligent routing systems, remanufacturing operations, and closed-loop supply chain concepts. The framework places a focus on domestic sourcing of critical minerals and strategic materials from end-of-life products, minimizing reliance on foreign supplies and promoting national resource sovereignty. Moreover, the study illuminates the importance of reverse logistics, which can enhance the Defense Industrial Base by supporting the harvesting, refurbishment, and remanufacturing of components to increase operational readiness. Moreover, the research shows how a reverse logistics optimized system can serve as an economic buffer when the supply chain system is disrupted, ensuring product availability and reducing the risk of inflation. The proposed framework is consistent with federal efforts to ensure critical supply chains and increase national resilience. The results indicate that the reverse logistics should not be considered simply waste management or cost-cutting but as a strategic national asset that can be used to achieve long-term national strategic goals in economic competitiveness, industrial sustainability, and national security. The study adds to an emerging body of research on resilient supply chains by offering a national-interest analysis of the challenges and opportunities of optimizing reverse logistics, and how it can bolster the strategic infrastructure of the United States.

Reverse Logistics, Supply Chain Resilience, Critical Material Recovery, Defense Industrial Base, Circular Economy.

1. Bressanelli, G., Adrodegari, F., Perona, M., & Saccani, N. (2021). Exploring how usage-focused business models enable circular economy through digital technologies. Sustainability, 13(6), 3323. [Google Scholar] [Crossref]

2. Choi, T. M. (2022). Data-driven supply chain resilience and robustness in the COVID-19 era. Transportation Research Part E: Logistics and Transportation Review, 158, 102619. [Google Scholar] [Crossref]

3. Dolgui, A., Ivanov, D., & Sokolov, B. (2020). Reconfigurable supply chain: The X-network. Omega, 95, 102122. [Google Scholar] [Crossref]

4. Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., Papadopoulos, T., & Luo, Z. (2021). Antecedents of resilient supply chains. International Journal of Production Research, 59(12), 3532–3558. [Google Scholar] [Crossref]

5. Ellen MacArthur Foundation. (2019). Completing the picture: How the circular economy tackles climate change. Ellen MacArthur Foundation. [Google Scholar] [Crossref]

6. Fleischmann, M., Bloemhof-Ruwaard, J. M., Dekker, R., van der Laan, E., van Nunen, J. A. E. E., & Van Wassenhove, L. N. (1997). Quantitative models for reverse logistics: A review. European Journal of Operational Research, 103(1), 1–17. [Google Scholar] [Crossref]

7. Govindan, K., & Soleimani, H. (2017). A review of reverse logistics and closed-loop supply chains. Journal of Cleaner Production, 142, 371–384. [Google Scholar] [Crossref]

8. Guide, V. D. R., & Van Wassenhove, L. N. (2009). The evolution of closed-loop supply chain research. Operations Research, 57(1), 10–18. [Google Scholar] [Crossref]

9. International Energy Agency. (2024). Global critical minerals outlook 2024. IEA Publications. [Google Scholar] [Crossref]

10. Ivanov, D. (2021). Supply chain viability and the COVID-19 pandemic: A conceptual and formal generalisation of four major adaptation strategies. International Journal of Production Research, 59(12), 3535–3552. [Google Scholar] [Crossref]

11. Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915. [Google Scholar] [Crossref]

12. OECD. (2024). Global material resources outlook to 2060: Economic drivers and environmental consequences. Organisation for Economic Co-operation and Development. [Google Scholar] [Crossref]

13. Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2022). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 319, 115–146. [Google Scholar] [Crossref]

14. Rogers, D. S., & Tibben-Lembke, R. S. (1999). Going backwards: Reverse logistics trends and practices. Reverse Logistics Executive Council. [Google Scholar] [Crossref]

15. Sharma, R., Kamble, S., Gunasekaran, A., Kumar, V., & Kumar, A. (2022). Artificial intelligence adoption for supply chain resilience and logistics optimization. Computers & Industrial Engineering, 168, 108123. [Google Scholar] [Crossref]

16. Srivastava, S. K. (2008). Network design for reverse logistics. Omega, 36(4), 535–548. [Google Scholar] [Crossref]

17. U.S. Department of Commerce. (2024). Supply chain assessment report. U.S. Government Publishing Office. [Google Scholar] [Crossref]

18. U.S. Department of Defense. (2024). Industrial capabilities report. Office of Industrial Policy, Department of Defense. [Google Scholar] [Crossref]

19. U.S. Department of Energy. (2024). National blueprint for lithium batteries 2024–2030. U.S. Department of Energy. [Google Scholar] [Crossref]

20. U.S. Geological Survey. (2025). Mineral commodity summaries 2025. U.S. Geological Survey. [Google Scholar] [Crossref]

21. Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2021). Big data analytics and supply chain performance: Effects on resilience and sustainability. International Journal of Information Management, 57, 102231. [Google Scholar] [Crossref]

22. White House Council on Supply Chain Resilience. (2024). White House council on supply chain resilience report. Executive Office of the President. [Google Scholar] [Crossref]

23. World Economic Forum. (2023). Global risks report 2023. World Economic Forum. [Google Scholar] [Crossref]

24. The White House. (2021). Executive Order 14017: America's supply chains. Federal Register, 86(39), 11849–11854. [Google Scholar] [Crossref]

25. Wang, Y., Zhang, D., & Liu, Q. (2024). Artificial intelligence-enabled reverse logistics optimization for sustainable supply chains. Journal of Cleaner Production, 435, 140214. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study