Build a supply chain risk monitoring system for international trade disruptions using network analysis and NLP for news sentiment
This project develops a comprehensive supply chain risk monitoring system that analyzes international trade disruptions and their potential impact on global supply networks. The system combines network analysis, real-time data monitoring, and natural language processing to provide early warning signals for supply chain risks.
Comprehensive supply chain and logistics data
International trade flows and import/export data
Real-time shipping and maritime data (if available)
News APIs and social media for disruption detection
Semiconductor and technology supply chains
Textile and apparel manufacturing networks
Healthcare and medical supply chains
Vehicle manufacturing and parts supply networks
Choose target industry (electronics, fashion, or pharmaceuticals)
Collect trade flow data (e.g., chips from Taiwan to India/US)
Build network graphs showing suppliers, hubs, and customers
Overlay risk factors (geopolitical conflicts, delays, fuel price spikes)
Use NLP to scrape and analyze news on supply disruptions
Build Tableau dashboard with live supply chain risk heatmaps
Develop mitigation strategies (e.g., "Diversify sourcing from country X")
Network visualization showing supplier relationships and risk propagation paths
Real-time Tableau dashboard with live supply chain risk heatmaps
Strategic recommendations for risk mitigation and supply chain resilience
Proactive identification and mitigation of supply chain disruptions
Optimize supply chain costs through strategic sourcing recommendations
Real-time alerts for potential supply chain disruptions
Build more resilient and diversified supply chain networks