Forecasting Supply Chain Disruptions with Foresight Learning

arXiv cs.LG / 4/3/2026

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Key Points

The paper proposes an end-to-end framework that trains LLMs to output calibrated probabilistic forecasts of supply chain disruptions using realized outcomes as supervision.
It argues that reliable reasoning about rare, high-impact events from noisy, unstructured data is a weakness of general-purpose models without task-specific adaptation.
Experiments report that the trained model substantially outperforms strong baselines, including GPT-5, across accuracy, calibration, and precision.
The authors claim the training leads to more structured and reliable probabilistic reasoning even without explicit prompting.
For transparency, the study opens an evaluation dataset on Hugging Face to support further validation and research.

Abstract

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