A Retrieval-Augmented Generation (RAG)-Based LLM for Modern Warehouse Automation and Management

Robotics automation and integration is quickly becoming a pillar of Industry 4.0 in modern warehouse management systems, but existing solutions fail to seamlessly integrate autonomous robotics into operational workflows, that leads to inefficiencies and scalability challenges. As the dependency on robotics grows in warehouses, there is the need for more reliable data and adaptable systems. The traditional system is not flexible enough to support dynamically evolving warehouse infrastructure that leads to inefficiencies and operational challenges. To address these issues, an efficient and trustworthy automation system is highly needed. This paper proposes a Modern Warehouse Management System (MWMS) framework that integrates Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs). The system uses an advanced framework to make it possible for robots to scan inventory on their own, sort packages in real time, plan their paths, assist people in pickup and place items, and to work together on tasks. A mock-up warehouse environment is developed to test and demonstrate the system’s capabilities in robotics assistance in a dynamic environment. The framework is evaluated using three LLMs, demonstrating its ability to interact with real-time data and enhance operational efficiency, accuracy, and trustworthiness.