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.
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Global supply chains have been subjected to an unprecedented sequence of shocks over the past several years, from the COVID-19 pandemic to geopolitical conflict, trade policy uncertainty, and extreme-weather events, exposing the fragility of lean, just-in-time operating models an…