Automated supply chain risk monitor — live APIs, risk scoring, Google Sheets integration, Gmail alerts via GitHub Actions

A Graph Neural Network (GNN) based system for supply chain risk assessment and early warning. This project leverages multi-layer graph architectures and ensemble GNN models (GCN, GAT, and Temporal GNN) to predict supplier risks, simulate disruption cascades, and provide actionable insights for supply chain resilience.

Applies invariant relational operators directly to the supply network state field, detecting cascade onset 88 steps before failure by observing topological asymmetry the moment a node begins to drain.

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Given what we know about a part at planning time, predicting whether it is at high risk of going on backorder soon, so planners can intervene before customers are impacted.

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Sentinel - Supply Chain Early Warning System

PharmaRisk Intel — Supply Chain Disruption Early Warning System for Amgen Global Supply Chain

This project solves : Predicting and identifying potential supply chain disruptions early using logistics , economic and geopolitical datasets , so organizations can take proactive actions instead of reacting to failures.

GDELT-BASED EARLY WARNING SYSTEM FOR SUPPLY CHAIN DISRUPTIONS

AI-powered early warning system that predicts supply chain bottlenecks using shipping data, geopolitical indicators, and social media signals

An Explainable Multimodal Early Warning System for Supply Chain Disruption Risk Prediction Using Large Language Models, Deep Time Series Learning, and Graph Neural Networks

🐦 Early warning system monitoring 22 global indicators for financial, supply chain, and geopolitical risks. Real-time alerts help families prepare before disruptions impact daily life.

A GDELT-based early warning system monitors global news and events to detect risks like conflicts, strikes, and policy shifts that threaten supply chains. Using NLP and predictive analytics, it delivers real-time alerts, dashboards, and forecasts to help organizations act proactively and stay resilient.

1. Loads the DataCo Smart Supply Chain dataset from Kaggle, 2. Engineers disruption indicators from order, delivery & shipping data, 3. Trains models to predict whether an order will be "late" , 4. Analyses which supplier/product/region combinations are highest risk, 5. Builds a risk heat-map and disruption early-warning dashboard