Resilient Supply Chains under Tariff Volatility: A Digital Twin-Enabled FLEX Model for Strategic Sourcing and Risk Mitigation

In the current uncertain global trade climate, supply chains endure greater pressures from volatile tariff regimes and changing geopolitics. Traditional risk-reducing and sourcing strategies prove inadequate in coping with such dynamic disruptions, especially when relying on foreign suppliers. This paper presents an innovative FLEX supply chain model—Flexible sourcing, Localized manufacturing, Elastic contracts, and Extra inventory buffers—that is intended to create increased resilience, agility, and profitability in the environment of tariff uncertainty. The model is then enhanced with the incorporation of Digital Twin (DT) technologies for real-time simulation, predictive analytics, and scenario planning. Based on cross-disciplinary literature in supply chain risk management, contract theory, and digital technologies, the research formulates a conceptual model and tests it in simulation-based case scenarios of on-again/off-again tariff events. The findings show that the FLEX+DT model is far better compared to conventional methods in terms of minimizing lead time variability, minimizing cost shocks, and maintaining operational continuity in global supply networks. Through the dynamic redistribution of resources, policy simulation, and contingency planning, the model provides a strategic toolkit for decision-makers managing tariff-induced disruptions. The work presents two key contributions: (1) it formalizes an end-to-end, modular supply chain design for tariff volatility, and (2) it takes Digital Twins from operational optimization to macro-level mitigation of trade risk. The results provide actionable insights to supply chain executives, policymakers, and global manufacturers in developing resilient, future-ready operations. The model can be taken forward in empirical industrial environments and synergistic links with AI-driven procurement systems and blockchain-secured smart contracts in future work.