Forecasting Supply Chain Disruptions with Foresight Learning
Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting where general-purpose models struggle without task-specific adaptation. We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision. The resulting model substantially outperforms strong baselines - including GPT-5 - on accuracy, calibration, and precision. We also show that training induces more structured and reliable probabilistic reasoning without explicit prompting. These results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals. To support transparency we open-source the evaluation dataset used in this study.
  Dataset: https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions
69cfc72be02fdd68b91b7ccc
Benjamin Turtel
claimed_verified
2026-04-07T08:44:34.975Z
631206b4c7722fdac9aa3e34
https://cdn-avatars.huggingface.co/v1/production/uploads/631206b4c7722fdac9aa3e34/QHFDYnlmBs3lPLIKnqQWG.jpeg
Ben
Bturtel
user
Bturtel
69cfc72be02fdd68b91b7ccd
Paul Wilczewski
69cfc72be02fdd68b91b7cce
Kris Skotheim
Forecasting Supply Chain Disruptions with Foresight Learning
Anticipating supply chain disruptions before they materialize is a core challenge for firms and policymakers alike. A key difficulty is learning to reason reliably about infrequent, high-impact events from noisy and unstructured inputs - a setting where general-purpose models struggle without task-specific adaptation. We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision. The resulting model substantially outperforms strong baselines - including GPT-5 - on accuracy, calibration, and precision. We also show that training induces more structured and reliable probabilistic reasoning without explicit prompting. These results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals. To support transparency we open-source the evaluation dataset used in this study.
  Dataset: https://huggingface.co/datasets/LightningRodLabs/supply-chain-predictions
Large language models can be trained to produce calibrated probabilistic forecasts for supply chain disruptions, outperforming existing baselines and enabling decision-ready predictions through domain-specific adaptation.
large language models probabilistic forecasts calibration supply chain disruptions domain-specific adaptation decision-ready signals