Forecasting Supply Chain Disruptions with Foresight Learning

• URL: http://arxiv.org/abs/2604.01298v1
• Date: Wed, 01 Apr 2026 18:04:34 GMT
• Title: Forecasting Supply Chain Disruptions with Foresight Learning
• Abstract summary: We introduce an end-to-end framework that trains LLMs to produce calibrated probabilistic forecasts using realized disruption outcomes as supervision.<n>The resulting model substantially outperforms strong baselines on accuracy, calibration, and precision.<n>Results suggest a general pathway for training domain-specific forecasting models that produce decision-ready signals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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