Multi-Tier Supply Chain Risk & Capital Optimization Engine

Modern global supply chain networks face severe systemic vulnerabilities resulting from cascading disruption propagation, non-linear demand shocks, and opaque multi-tier vendor dependencies. Traditional Business Intelligence (BI) systems, which rely heavily on descriptive dashboards and correlational predictive analytics, fail to uncover the underlying causal mechanisms driving enterprise risk. Consequently, executive decision-makers lack actionable prescriptive guidance for dynamic capital allocation and operational intervention under compound market volatility. This paper introduces an Explainable Causal Graph Decision Intelligence (ECG-DI) framework that combines structural causal modeling (SCM), temporal graph neural networks (T-GNN), and multi-objective deep reinforcement learning (MODRL). Using an extensive simulated enterprise dataset (N = 2,500 global manufacturing nodes across Tier-1 to Tier-4 supplier networks), we evaluate the framework against traditional machine learning and enterprise risk management approaches. The proposed ECG-DI architecture achieves superior predictive performance (ROC-AUC = 0.948, F1-score = 0.923, RMSE = 0.041) while reducing counterfactual decision error by 34.2%. Furthermore, the integrated decision optimization model yields a 28.4% improvement in capital allocation efficiency during simulated systemic supply chain shocks. We provide an exhaustive empirical validation, sensitivity analysis, stress-testing scenario, ethics framework, and managerial roadmap for enterprise deployment.