There is an increasing interest from manufacturing industry and academia to improve operational efficiency with advanced AI systems. One such innovation is the Retrieval-Augmented Generation (RAG) system, which integrates a Large Language Model (LLM) to deliver customized recommendations and question-answering based on historical maintenance data and machine operational manuals. Previous studies highlight the effectiveness of LLMs in processing unstructured text, but limited research exists on their application within RAG frameworks for industrial maintenance use cases. The key challenge is efficiently retrieving relevant quality data and providing recommendations that are both contextually appropriate and implementable by maintenance repairmen and technicians. This paper presents a case study where a RAG system, trained on data from a Computerized Maintenance Management System and one operational manual provide recommendations/question-answers to support decision-making on the factory floor. The methodology involves embedding all the data using LLM, followed by conducting a similarity search to identify relevant information. Recommendations are then generated using the LLM and subsequently validated through expert review by subject matter experts. The results indicate that the RAG system facilitates faster and more streamlined decision-making in maintenance tasks by efficiently retrieving and contextualizing contextual maintenance records. This approach not only makes it easier for operators, repairmen and technicians to access expert knowledge, but it also shows how RAG systems can help simplify operations in industries that depend on complicated maintenance processes. The study highlights how RAG-based LLM systems can improve maintenance work by using AI to provide helpful insights.