Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this prediction task inherently challenging. This paper introduces a multi-task deep learning model for delivery delay duration prediction in the presence of significant imbalanced data, where delayed shipments are rare but operationally consequential. The model embeds high-dimensional shipment features with dedicated embedding layers for tabular data, and then uses a classification-then-regression strategy to predict the delivery delay duration for on-time and delayed shipments. Unlike sequential pipelines, this approach enables end-to-end training, improves the detection of delayed cases, and supports probabilistic forecasting for uncertainty-aw
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this prediction task inherently challenging. This paper introduces a multi-task deep learning model for delivery delay duration prediction in the presence of significant imbalanced data, where delayed shipments are rare but operationally consequential. The model embeds high-dimensional shipment features with dedicated embedding layers for tabular data, and then uses a classification-then-regression strategy to predict the delivery delay duration for on-time and delayed shipments. Unlike sequential pipelines, this approach enables end-to-end training, improves the detection of delayed cases, and supports probabilistic forecasting for uncertainty-aware decision making. The proposed approach is evaluated on a large-scale real-world dataset from an industrial partner, comprising more than 10 million historical shipment records across four major source locations with distinct regional characteristics. The proposed model is compared with traditional machine learning methods. Experimental results show that the proposed method achieves a mean absolute error of 0.67-0.91 days for delayed-shipment predictions, outperforming single-step tree-based regression baselines by 41-64% and two-step classify-then-regress tree-based models by 15-35%. These gains demonstrate the effectiveness of the proposed model in operational delivery delay forecasting under highly imbalanced and heterogeneous conditions.
Executive Summary
This paper addresses the challenge of predicting delivery delay duration in complex logistics networks using a multi-task deep learning model. The proposed approach embeds shipment features with dedicated layers and uses a classification-then-regression strategy to predict delays. Experimental results show that the model outperforms traditional machine learning methods in operational delivery delay forecasting. The study's findings have significant implications for supply chain management and highlight the potential of deep learning in addressing imbalanced and heterogeneous data. The proposed model's ability to support probabilistic forecasting and end-to-end training is a notable advantage. However, the study's reliance on a single real-world dataset may limit the model's generalizability to other contexts. Overall, the paper makes a valuable contribution to the field of supply chain optimization and logistics management.
Key Points
- ▸ The proposed multi-task deep learning model addresses the challenge of predicting delivery delay duration in complex logistics networks.
- ▸ The model embeds shipment features with dedicated layers and uses a classification-then-regression strategy to predict delays.
- ▸ Experimental results show that the model outperforms traditional machine learning methods in operational delivery delay forecasting.
Merits
Strength
The proposed model's ability to support probabilistic forecasting and end-to-end training is a notable advantage.
Strength
The model's performance is evaluated on a large-scale real-world dataset, providing a robust assessment of its effectiveness.
Strength
The study highlights the potential of deep learning in addressing imbalanced and heterogeneous data in supply chain management.
Demerits
Limitation
The study's reliance on a single real-world dataset may limit the model's generalizability to other contexts.
Limitation
The model's performance may be sensitive to the quality and availability of shipment data, which can be a challenge in real-world settings.
Expert Commentary
This paper makes a valuable contribution to the field of supply chain optimization and logistics management. The proposed multi-task deep learning model is a notable advancement in addressing the challenge of predicting delivery delay duration in complex logistics networks. However, the study's reliance on a single real-world dataset may limit the model's generalizability to other contexts. Nevertheless, the model's performance is evaluated on a large-scale real-world dataset, providing a robust assessment of its effectiveness. The study highlights the potential of deep learning in addressing imbalanced and heterogeneous data in supply chain management, which is a significant advantage. Overall, the paper's findings have significant implications for supply chain management and logistics, and the proposed model can be used to develop more accurate and efficient delivery delay forecasting systems.
Recommendations
- ✓ Future studies should aim to evaluate the proposed model on a broader range of datasets and in different contexts to assess its generalizability.
- ✓ The proposed model's performance should be further evaluated on other metrics, such as accuracy and precision, to provide a more comprehensive assessment of its effectiveness.
