EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks
Summary: arXiv:2604.05254v1 Announce Type: new
Abstract
Modern logistics networks generate rich operational data streams at every warehouse node and transportation lane — from order timestamps and routing records to shipping manifests — yet predicting delivery delays remains predominantly reactive. Existing predictive approaches typically treat this problem either as a tabular classification task, ignoring network topology, or as a time-series anomaly detection task, overlooking the spatial dependencies of the supply chain graph.
To bridge this gap, we propose a hybrid deep learning framework for proactive supply chain risk management. The proposed method jointly models temporal order-flow dynamics via a lightweight Transformer patch encoder and inter-hub relational dependencies through an Edge-Aware Graph Attention Network (E-GAT), optimized via a multi-task learning objective.
Methodology
The EAGLE framework integrates two main components: a Transformer-based encoder and an Edge-Aware Graph Attention Network. This combination allows for effective modeling of both temporal dynamics and spatial relationships within the logistics network. Key features of the framework include:
- Transformer Patch Encoder: Captures order-flow dynamics over time, enabling the model to understand the sequence of events leading to delivery delays.
- Edge-Aware Graph Attention Network (E-GAT): Incorporates the spatial dependencies that exist between different nodes in the supply chain, enhancing the model’s predictive capabilities.
- Multi-Task Learning Objective: Optimizes the learning process by tackling different aspects of the delivery delay prediction task simultaneously, ensuring a comprehensive understanding of the underlying data.
Results
The EAGLE framework was evaluated using the real-world DataCo Smart Supply Chain dataset, demonstrating significant improvements over baseline methods. The results are as follows:
- F1-score: 0.8762
- AUC-ROC: 0.9773
- Cross-seed F1 Standard Deviation: 0.0089
These metrics highlight a 3.8 times improvement in predictive accuracy and training stability compared to the best ablated variant, showcasing the effectiveness of the EAGLE framework.
Conclusion
The EAGLE framework presents a novel approach to proactive delivery delay prediction in smart logistics networks by leveraging advanced deep learning techniques. By effectively modeling both temporal and spatial dimensions of the supply chain, this hybrid approach not only enhances predictive accuracy but also offers a robust solution to supply chain risk management.
As logistics networks continue to evolve and generate vast amounts of data, frameworks like EAGLE will be crucial in ensuring timely deliveries and optimizing operational efficiency.