Agentic LLM-based Contingency Management in Production Control

With the rapid rise of Large Language Models capable of representing intricate multi-modal data, decision-makers facing contingencies can be contextually supported with real-time data, dynamic simulations and actionable knowledge insights. This can address critical challenges in production planning and control, particularly in managing stochastic, unforeseen or uninformed changes, i.e. during contingencies. A framework of localized LLM agents for a specific manufacturing environment and system is used to collect past and current decision reasonings, sensory and simulation data to propose contingency management reactions. This concept is implemented within a small-scale semiconductor manufacturing system, demonstrating a unique opportunity for the successful digital transformation of production planning and control.