## 1 Introduction  
Modern manufacturing enterprises operate across complex, layered systems that range from equipment-level automation to enterprise-wide planning and control. While innovations in robotics, Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA), Manufacturing Execution Systems (MESs), and Enterprise Resource Planning (ERP) have enabled greater throughput and coordination [1], inventory search and management remain a persistent operational challenge, especially in legacy environments. In particular, inventory search systems within industrial settings struggle to keep pace with the evolving complexity and scale of manufacturing data. Despite technological advancements, legacy inventory systems continue to be a persistent bottleneck in manufacturing. Over time, organizations accumulate vast amounts of data across changing software, naming conventions, and reference structures. This evolution often renders historical data unstructured, inconsistent, and difficult to query. Inventory systems, in particular, suffer from fragmentation, making it hard to interpret part names, categories, and usage history without extensive programming or insider knowledge. To address this critical issue, we present an effective, secure, and intelligent inventory management and recommendation system that has been developed and rigorously tested in a real-world manufacturing setting. Traditional systems rely on keyword-based queries and static filters, forcing users to adjust search terms to locate parts or components iteratively. These methods fall short in handling semantic variability, complex queries, or incomplete input. This paper introduces a novel framework that leverages Large Language Models (LLMs) to enable intelligent, context-aware inventory search and recommendations. By integrating state-of-the-art techniques, such as Retrieval-Augmented Generation (RAG), which incorporates concepts like data vectorization and vector search, the proposed system significantly enhances both the relevance and efficiency of inventory access. Furthermore, in response to growing concerns over data confidentiality in industrial AI applications, our solution includes robust privacy-preserving mechanisms to secure sensitive information throughout the search process. LLMs have shown transformative potential across various industries by enhancing communication, streamlining workflows, and supporting data-driven decision-making. In manufacturing, LLMs can interpret ERP and internal database queries more intelligently, addressing persistent communication challenges such as order inconsistencies and feedback loops between customers and producers [2]. They also support predictive maintenance by classifying work orders, estimating duration, and identifying key failure factors, thereby reducing downtime and improving resource allocation [3]. Additionally, LLMs help address challenges in data preparation, a significant barrier to the adoption of ML in manufacturing. The labor-intensive nature of data wrangling often limits the scalability of ML solutions. LLMs, however, can automate aspects of this process, enabling non-experts to engage with data science workflows and improving interdisciplinary collaboration [4]. Their ability to parse complex data and derive actionable insights supports the broader vision of intelligent manufacturing and data-centric operations [5]. In summary, our contribution is an LLM-enhanced, privacy-aware inventory search and recommendation system specifically designed to address the challenges of legacy industrial data. The solution is validated in a manufacturing context and is extensible to other domains requiring intelligent inventory or product search. The rest of this article is organized as follows: Section 2 reviews related work relevant to the methods presented in this study. Section 3 details the methodology. Section 4 provides the performance evaluation metrics, while Section 5 provides the implementation of the proposed methods and the results.