This research paper should investigate how Artificial Intelligence (AI) is transforming warehouse operations within the field of Operations Management (OM). Through a systematic literature review of academic and industry sources, the study should explore how AI technologies—such as machine learning, robotics, computer vision, and reinforcement learning—enhance core warehouse functions including order picking, inventory control, routing, and quality assurance.
Grounded in Organizational Information Processing Theory (OIPT), the paper should reveal that AI not only improves operational outcomes (e.g., accuracy, efficiency, downtime reduction) but also increases the organization’s ability to process complex and dynamic information. The findings should highlight AI’s role in reducing uncertainty, enabling real-time decision-making, and fostering lateral information flow across warehouse systems.
Ultimately, this research should offer a robust theoretical and practical framework for understanding AI’s integration into warehouse management, providing implications for managers, system designers, and OM scholars alike.
Links:
- (PDF) AI-driven warehouse automation: A comprehensive review of systems
- [2312.16026] Dynamic AGV Task Allocation in Intelligent Warehouses
- [2408.01656] Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
- [2408.16890] Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search
- [2408.16633] Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
- Intelligent Warehouse in Industry 4.0—Systematic Literature Review – PMC
- The Role of AI in Warehouse Digital Twins: Literature Review
Use other links as appropriate