This study explores the integration of Large Language Models (LLMs) into the manufacturing sector, focusing on their potential to enhance efficiency, decision-making, and product quality. While existing literature emphasizes the conceptual benefits of LLMs, there is limited empirical evidence supporting these claims. The study uses a case study to examine five affordances of LLMs, including automating information processing and improving data quality, as well as four constraints, such as risks to job stability and data security. Key findings suggest that LLMs offer substantial opportunities for streamlining operations and reducing manual labor, yet challenges such as explainability and secure data management remain. The study contributes to both theory and practice by advancing the understanding of LLM integration in manufacturing through an affordance theory framework. This framework helps assess how LLMs influence operational processes and workforce dynamics. However, the study acknowledges its limitations due to the reliance on early stage data and a single case study, urging further research into diverse industrial settings and long-term effects.