# Forecasting Supply Chain Disruptions with Foresight Learning

arXiv: https://arxiv.org/abs/2604.01298 · HTML: https://mlsanity.com/paper/2604.01298/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 1 Apr 2026.

## Builds on (most cited first, 3 of 3)

- [SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions](https://mlsanity.com/paper/2408.05357/) (2024)
- [Future-as-Label: Scalable Supervision from Real-World Outcomes](https://mlsanity.com/paper/2601.06336/) (2026)
- [LLMs for Supply Chain Management](https://mlsanity.com/paper/2505.18597/) (2025)

## Built on by (most cited first, 1 of 1)

- [Training Large Language Models to Predict Clinical Events](https://mlsanity.com/paper/2605.12817/) (2026)

## Abstract (arXiv)

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