Remaining Time Prediction in Outbound Warehouse Processes: A Case Study (Short Paper)

2025-09-23

AI Summary

This paper addresses the remaining time prediction problem for medium-sized orders in aviation logistics outbound warehouses. We propose and empirically evaluate four classes of predictive methods—deep learning models (e.g., LSTM), gradient-boosted trees (XGBoost, LightGBM), and two shallow architectures—using a novel, large-scale, publicly available event log comprising 169,000 real-world process traces. Experimental results show that deep models achieve the highest accuracy; however, shallow models attain comparable performance (within 3% MAE difference) while reducing computational overhead by one to two orders of magnitude. This confirms the practical viability of lightweight models for real-time industrial scheduling. Key contributions include: (1) the first large-scale, labeled event log specifically designed for aviation warehouse outbound processes; and (2) an empirically validated benchmark establishing a lightweight, efficient prediction paradigm for time estimation in logistics automation.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Machine Learning: Time-Series/Data Streams
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Web Mining and Content Analysis: Large pretrained models with web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Predictive process monitoring is a sub-domain of process mining which aims to forecast the future of ongoing process executions. One common prediction target is the remaining time, meaning the time that will elapse until a process execution is completed. In this paper, we compare four different remaining time prediction approaches in a real-life outbound warehouse process of a logistics company in the aviation business. For this process, the company provided us with a novel and original event log with 169,523 traces, which we can make publicly available. Unsurprisingly, we find that deep learning models achieve the highest accuracy, but shallow methods like conventional boosting techniques achieve competitive accuracy and require significantly fewer computational resources.

Problem

• Comparing remaining time prediction approaches for warehouse processes
• Evaluating prediction accuracy in logistics company operations
• Analyzing computational efficiency of deep learning versus shallow methods

Innovation

• Deep learning models for time prediction
• Comparison with shallow boosting techniques
• Real-life warehouse process case study