Prescriptive Analytics for Next-Gen Supply Chains: Integrating Causal AI with Digital Twin Technologies

In the face of escalating complexity and anticipation, modern supply chains require smart systems that are qualified to predict outcomes and prescribe useful interventions. This paper presents an integrated framework that connects Causal Artificial Intelligence (Causal AI) with Digital Twin (DT) technologies that help prescriptive analytics for next-generation supply chains. While DTs deliver high-fidelity, real-time simulations of physical methods, Causal AI models underlying cause-effect connections, allowing evaluation of alternative conclusions via counterfactual reasoning. Our framework implements causal system understanding and do-calculus into DT environments, letting decision-makers test interventions, determine core reasons for troubles, and optimize operational processes dynamically. We validate the framework utilizing a multi-echelon supply chain simulation, showing its dominance over traditional predictive measures in efficiency, resilience, and decision explainability. The outcomes show that our method improves strategic foresight and helps proactive threat mitigation and agile planning in volatile, fast-changing environments.