[2604.01298] Forecasting Supply Chain Disruptions with Foresight Learning

Computer Science > Machine Learning arXiv:2604.01298 (cs) [Submitted on 1 Apr 2026] Title:Forecasting Supply Chain Disruptions with Foresight Learning Authors:Benjamin Turtel, Paul Wilczewski, Kris Skotheim View a PDF of the paper titled Forecasting Supply Chain Disruptions with Foresight Learning, by Benjamin Turtel and 2 other authors View PDF HTML (experimental) 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: this https URL Subjects: Machine Learning (cs.LG) Cite as: arXiv:2604.01298 [cs.LG]   (or arXiv:2604.01298v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2604.01298 Focus to learn...