Abstract:

As the cornerstone of enterprise digital transformation, the governance quality of Material Master Data (MMD) directly impacts an enterprise’s operational efficiency and decision-making accuracy. When dealing with massive and heterogeneous datasets, traditional MMD governance methods generally face challenges like low automation, inefficiency, and high governance costs. To address these problems, this paper proposes an innovative framework integrating large language models and retrieval-augmented generation technology, conjunction with the actual business context of National Energy Group Materials Co., Ltd... Built on a local computing architecture, the framework designs a clear technical implementation path and establishes a four-tier technical architecture, including computing infrastructure layer, model layer, data layer, and application capability hub layer. It achieves three core functions: duplicate detection for legacy materials, intelligent classification with context-aware recommendations, and automated parameter validation. In specific data governance scenarios, the solution significantly improves the accuracy rate and processing efficiency of data governance while effectively controlling governance costs. It provides solid technical support for the enterprise’s digital transformation and aligns with the future trend of MMD management towards intelligence and automation.

Key words: material master data, large language model, retrieval-augmented generation, data governance, intelligent classification