Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning
#Multi-task deep learning #Delivery delay prediction #Supply chain optimization #Uncertainty-aware forecasting #Imbalanced data #Logistics networks #Probabilistic modeling
📌 Key Takeaways
- Researchers developed a multi-task deep learning model for delivery delay prediction
- The model addresses challenges in complex supply chains with imbalanced data
- The approach achieved 41-64% improvement over traditional methods
- The model was tested on 10+ million shipment records across four locations
- The method enables uncertainty-aware decision making through probabilistic forecasting
📖 Full Retelling
Researchers led by Stefan Faulkner, in collaboration with five other scientists, introduced a multi-task deep learning model for delivery delay duration prediction on February 23, 2026, addressing the growing challenge of forecasting delays in increasingly complex global supply chains where delayed shipments are rare but operationally consequential. The research team developed an innovative approach that handles the inherent difficulties in predicting delivery delays within modern logistics networks, which span multimodal transportation, cross-country routing, and exhibit significant regional variability. Their model specifically addresses the problem of imbalanced data in shipping operations, where delayed shipments are infrequent but carry substantial operational impacts. By embedding high-dimensional shipment features with dedicated embedding layers for tabular data, the researchers implemented a classification-then-regression strategy that enables end-to-end training and improves detection of delayed cases while supporting probabilistic forecasting for uncertainty-aware decision making. The proposed approach was rigorously 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. When compared with traditional machine learning methods, the deep learning model demonstrated significant improvements, achieving a mean absolute error of 0.67-0.91 days for delayed-shipment predictions, representing a substantial improvement over single-step tree-based regression baselines by 41-64% and two-step classify-then-regress tree-based models by 15-35%.
🏷️ Themes
Machine Learning, Supply Chain Management, Data Science
📚 Related People & Topics
Supply chain optimization
Methodology aiming to ensure the optimal operation of a supply chain
Supply-chain optimization (SCO) aims to ensure the optimal operation of a manufacturing and distribution supply chain. This includes the optimal placement of inventory within the supply chain, minimizing operating costs including manufacturing costs, transportation costs, and distribution costs. Opt...
Supply chain
System involved in supplying a product or service to a consumer
A supply chain is a complex logistics system that consists of facilities that convert raw materials into finished products and distribute them to end consumers or end customers, while supply chain management focuses on the optimization of the flow of goods within the supply chain's distribution chan...
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Original Source
--> Computer Science > Machine Learning arXiv:2602.20271 [Submitted on 23 Feb 2026] Title: Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning Authors: Stefan Faulkner , Reza Zandehshahvar , Vahid Eghbal Akhlaghi , Sebastien Ouellet , Carsten Jordan , Pascal Van Hentenryck View a PDF of the paper titled Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning, by Stefan Faulkner and 5 other authors View PDF HTML 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 tr...
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