Forecasting Supply Chain Disruptions with Foresight Learning

Ben, Paul Wilczewski, Kris Skotheim

Forecasting Supply Chain Disruptions with Foresight Learning: 6 upvotes on Hugging Face Daily Papers, #29 of 45 papers on 2026-04-03. Day-by-day upvote history.

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

Paper page on Hugging Face · arXiv

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