# Reinforcement Learning for Autonomous Warehouse Orchestration in SAP Logistics Execution: Redefining Supply Chain Agility

arXiv: https://arxiv.org/abs/2506.06523 · HTML: https://mlsanity.com/paper/2506.06523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 6 Jun 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [AI-Driven Code Refactoring: Using Graph Neural Networks to Enhance Software Maintainability](https://mlsanity.com/paper/2504.10412/) (2025)
- [Code Reborn AI-Driven Legacy Systems Modernization from COBOL to Java](https://mlsanity.com/paper/2504.11335/) (2025)

## Abstract (arXiv)

In an era of escalating supply chain demands, SAP Logistics Execution (LE) is pivotal for managing warehouse operations, transportation, and delivery. This research introduces a pioneering framework leveraging reinforcement learning (RL) to autonomously orchestrate warehouse tasks in SAP LE, enhancing operational agility and efficiency. By modeling warehouse processes as dynamic environments, the framework optimizes task allocation, inventory movement, and order picking in real-time. A synthetic dataset of 300,000 LE transactions simulates real-world warehouse scenarios, including multilingual data and operational disruptions. The analysis achieves 95% task optimization accuracy, reducing processing times by 60% compared to traditional methods. Visualizations, including efficiency heatmaps and performance graphs, guide agile warehouse strategies. This approach tackles data privacy, scalability, and SAP integration, offering a transformative solution for modern supply chains.