<p>This study presents an innovative cloud-based digital twin architecture that enables intelligent supply chain management in dynamic and uncertain conditions by utilizing real-time modeling, fuzzy multi-objective analysis, and a cognitive decision support engine. This architecture provides a live view of the operational status by directly connecting to real and simulated data and has the ability to analyze normal, disruption, and peak load scenarios in an integrated manner. The results show that the developed model is able to generate accurate, stable, and uncertainty-adapted Pareto fronts and adjust decision paths to balance cost, time, quality, and resilience objectives. Performance evaluation in a cloud environment also proves that the proposed architecture is not only industrially deployable, but can also be a foundation for the next generation of intelligent decision support systems in supply chains.</p>