Integrating digital twins and AI-augmented predictive analytics for resilient, demand-driven global supply chain orchestration under volatility

Global supply chains are increasingly exposed to volatility arising from geopolitical tensions, climate disruptions, fluctuating consumer demand, and pandemic-induced shocks. Conventional supply chain planning frameworks, reliant on static forecasting and linear optimization, are inadequate for capturing the complexities of real-time disruptions and dynamic market uncertainties. In response, the convergence of digital twin technologies and artificial intelligence (AI)-augmented predictive analytics has emerged as a transformative strategy for achieving resilience and demand-driven orchestration. Digital twins virtual replicas of physical supply networks enable continuous synchronization between operational processes and market realities, while predictive analytics powered by machine learning provides foresight into demand fluctuations, supplier reliability, and transportation risks. This research examines how integrating digital twins with AI-augmented analytics enhances proactive decision-making by simulating multiple disruption scenarios, optimizing inventory buffers, and reallocating resources dynamically. Advanced methods such as reinforcement learning for adaptive logistics routing, graph neural networks for supplier interdependency analysis, and probabilistic forecasting models are incorporated to anticipate and mitigate volatility. The framework emphasizes demand-driven orchestration, ensuring responsiveness not only to historical data patterns but also to real-time signals from IoT sensors, trade flows, and customer behaviors. Key contributions of this study include a roadmap for scalable implementation across global enterprises, guidelines for integrating heterogeneous data sources, and resilience metrics that balance cost efficiency with operational continuity. Despite challenges such as computational complexity, interoperability issues, and governance of cross-border data, the fusion of digital twins and AI offers an intelligent, adaptive infrastructure for re-engineering global supply chains into more resilient, agile, and demand-driven systems.