Supply Chain Intelligence from Production Network Data

Modern supply chains are opaque. Companies and governments know their direct suppliers but rarely have visibility into the deeper network of dependencies that determine vulnerability to disruption. Our data makes this network visible.

The AIPNET production network maps directed input-output relationships across 5,000+ products, revealing which inputs are critical bottlenecks and how disruptions in one product category propagate to others. We have validated these network effects using natural experiments, demonstrating that production network structure predicts the real-world propagation of trade shocks.

Our Machinery of Progress dataset adds a complementary lens — tracking 50 million capital equipment transactions across the US economy, covering heavy machinery, vehicles, robotics, and IT equipment. Together, these datasets enable logistics and supply chain teams to assess concentration risk, model disruption scenarios, and identify strategic alternatives for critical inputs.