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, scenario stress-testing,
ethics framework, and managerial roadmap for enterprise deployment.