Modern smart factories often face challenges with scattered data and information silos. Although sensors, SCADA, MES, and other systems continuously generate vast amounts of data, the lack of integration makes it difficult to quickly identify the root causes of anomalies. Large language models (LLMs) are emerging as a key solution by enabling cross-system data retrieval and analysis through natural language queries. Acting as “semantic coordinators” within multi-agent manufacturing systems, LLMs can dynamically adjust production schedules and resource allocation in real time. The article references examples from Microsoft Azure AI, AWS, Schaeffler, and Siemens to demonstrate how LLMs help reduce downtime, increase transparency, and improve decision-making efficiency. Finally, it highlights that as LLMs integrate with automation and visualization technologies, manufacturing will enter the “semantic era,” where operators can access production line insights conversationally and instantly, driving smart manufacturing toward greater flexibility and intelligence.
