Forecasting Supply Chain Disruptions with Foresight Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908931930980352 |
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| author | Turtel, Benjamin Wilczewski, Paul Skotheim, Kris |
| author_facet | Turtel, Benjamin Wilczewski, Paul Skotheim, Kris |
| contents | 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 |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_01298 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Forecasting Supply Chain Disruptions with Foresight Learning Turtel, Benjamin Wilczewski, Paul Skotheim, Kris Machine Learning 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 |
| title | Forecasting Supply Chain Disruptions with Foresight Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2604.01298 |