Uncertainty-Aware Delivery Delay Duration Prediction via Multi-Task Deep Learning

TL;DR

This paper presents a multi-task deep learning model that accurately predicts delivery delay durations in complex, imbalanced logistics data, enhancing operational efficiency and customer satisfaction.

Contribution

It introduces an end-to-end multi-task deep learning approach with probabilistic forecasting for delivery delay prediction in heterogeneous supply chain data.

Findings

Achieves a mean absolute error of 0.67-0.91 days for delayed shipments.

Outperforms traditional machine learning methods by 41-64%.

Effective in highly imbalanced and regional variability conditions.

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.