Predictive Resilience in Cross-Border Logistics: A Cloud-Native Machine Learning Architecture for Supply Chain Disruption Optimization
Global value chains are increasingly susceptible to systemic disruptions, ranging from geopolitical conflicts to climate anomalies. Traditional supply chain management frameworks rely heavily on reactive mitigation strategies and fragmented, low-velocity data silos, leading to severe logistical bottlenecks during crises. This paper proposes a proactive, cloud-native architectural solution utilizing Amazon Web Services to construct a real-time predictive resilience pipeline for cross-border logistics. By deploying asynchronous Python middleware integrated with eXtreme Gradient Boosting and dynamic graph routing algorithms, the proposed system programmatically ingests multi-modal supply chain data, identifying disruption vectors and autonomously rerouting logistical flows before compounding failures occur. Preliminary architectural evaluations demonstrate that migrating supply chain analytics to a distributed, high-frequency serverless infrastructure significantly reduces data latency, providing enterprise operations with a deterministic, highly scalable tool for optimizing supply chain resilience and minimizing systemic friction.