Data Skeptic Optimizing Supply Chains with GNN
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Jan 15, 2025 Thibaut Vidal, a professor at Polytechnique Montreal, specializes in using advanced algorithms and machine learning for supply chain optimization. He discusses how graph-based methods can revolutionize logistics by improving routing and decision-making. The conversation highlights the effectiveness of Graph Neural Networks in predicting delivery costs for companies like UPS and Amazon. Thibaut also emphasizes the potential of these advanced techniques to cut costs, enhance efficiency, and create better working conditions through smarter route planning.
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Data-Driven Supply Chains
- Data-driven supply chains leverage rich data flows for better decisions.
- This approach combines prediction and optimization, integrating data like forecasts and real-time disruptions.
Supply Chain Data
- Supply chain data includes supply and demand forecasts, real-time disruptions, and equipment health profiles.
- This data enables better routing, maintenance, and handling of uncertainty.
Supply Chain Optimization Methods
- Two main supply chain optimization methods exist: sequential prediction and optimization, and end-to-end learning.
- End-to-end learning prioritizes good decisions over accurate predictions, considering downstream impacts.