Supply Chain Disruption Forecasting

Predict supply chain disruptions before they happen

Turn operational metrics and news into early-warning prediction models. By treating future disruption events as labels, models learn which signals matter — without any manual annotation of historical incidents.

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34%
more accurate than GPT-5 on supply chain predictions
↗ arXiv paper
59%
better calibrated than GPT-5
↗ arXiv paper

Example prediction questions

The kinds of questions a model trained on your data can answer.

Key results

Benchmark comparisons against frontier models

Calibration vs. GPT-5 on Supply Chain Predictions

The trained model closely tracks the perfect-calibration diagonal — when it says 30% probability, roughly 30% of disruptions materialize. GPT-5 and the base model severely under-predict high-risk events. Precision@10% improves 4× (35% vs. 9%).

Reliability diagram showing the trained model (yellow) tracking perfect calibration while GPT-5 and gpt-oss-120b deviate significantly from the diagonal
↗ arXiv paper

Explore

Primary write-ups and artifacts for this solution.

Case study From Metric to Prediction: Forecasting Supply Chain Disruptions with Foresight Learning → Paper Forecasting Supply Chain Disruptions with Foresight Learning → Dataset supply-chain-predictions → Model foresight-32B →

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