LLMs as Data Preprocessors for Natural Language Processing in Manufacturing

Due to the growing connectivity capabilities of manufacturing equipment, an increasing number of machines and systems are being integrated into corporate networks, where they continuously transmit data. As manufacturing processes become more complex, they generate extensive volumes of domain-specific data, necessitating intelligent and adaptive data processing solutions. AI applications in manufacturing need to be developed to effectively process this data. In recent years, Large Language Models (LLMs) have received considerable attention, particularly in fields such as software engineering, data engineering, and computer simulation. However, their application in manufacturing contexts remains in its infancy, both in industrial practice and academic research. This paper explores approaches to leveraging textual data within manufacturing, specifically by comparing traditional Natural Language Processing (NLP) pipelines with LLM-based pre-processing techniques. Initially, a classical NLP pipeline approach is analyzed to establish a baseline. Subsequently, an LLM-driven method aimed at improving contextual processing is examined. The findings present a simplified and practical framework for employing LLMs in industrial data science projects. Our results indicate that using LLMs for various data pre-processing tasks significantly enhances machine learning performance metrics while concurrently reducing the effort and the level of data science expertise required.