Forecasting Supply Chain Disruptions with Foresight Learning
arXiv:2604.01298v1 Announce Type: new 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
arXiv:2604.01298v1 Announce Type: new 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
Executive Summary
This study presents an end-to-end framework for training large language models (LLMs) to produce calibrated probabilistic forecasts for supply chain disruptions. The framework utilizes realized disruption outcomes as supervision and outperforms strong baselines, including GPT-5, on accuracy, calibration, and precision. The results demonstrate the potential for training domain-specific forecasting models that produce decision-ready signals. The study also opens up the evaluation dataset for transparency.
Key Points
- ▸ The study proposes an end-to-end framework for training LLMs to forecast supply chain disruptions.
- ▸ The framework uses realized disruption outcomes as supervision and produces calibrated probabilistic forecasts.
- ▸ The results show that the framework outperforms strong baselines, including GPT-5, on accuracy, calibration, and precision.
Merits
Strengths in Model Performance
The study demonstrates significant improvements in model performance, outperforming strong baselines on accuracy, calibration, and precision.
Adaptability to Domain-Specific Tasks
The framework shows potential for adapting to domain-specific tasks, such as supply chain disruption forecasting.
Increased Transparency
The study opens up the evaluation dataset for transparency, promoting reproducibility and further research.
Demerits
Limited Generalizability
The study focuses on supply chain disruption forecasting and its limitations in generalizability to other domains remain to be explored.
Dependence on High-Quality Supervision
The framework relies on high-quality supervision, which may not be readily available in all scenarios.
Expert Commentary
This study marks a significant step forward in the development of predictive analytics for supply chain management. The framework's ability to adapt to domain-specific tasks and produce calibrated probabilistic forecasts is a notable achievement. However, the limitations of the study, such as its focus on supply chain disruption forecasting and dependence on high-quality supervision, should be acknowledged. The study's implications for firms and policymakers are substantial, and its findings may inform policy decisions on supply chain management and resilience. As the field continues to evolve, it is essential to explore the generalizability of the framework to other domains and the potential for integrating it with other predictive analytics techniques.
Recommendations
- ✓ Future research should explore the generalizability of the framework to other domains and the potential for integrating it with other predictive analytics techniques.
- ✓ The study's framework should be further evaluated and refined to address potential limitations, such as dependence on high-quality supervision.
Sources
Original: arXiv - cs.LG
