The attempt of using lumped or agent-based simulation models to support operations management in production systems puts action modelling to the fore. To fill the gap of classical decision-support systems ignoring human agents' practices, a modelling framework of action at operations level is proposed. This framework aims at answering two questions: How to represent action? How to represent the management of action? Every action (i.e., what is actually done by an agent) is represented by a binary function of time governed by events detected upon processes of various kinds: artefacts (clocks or schedules), external processes occurring in the environment, other actions. In turn, every action exerts its effect on target processes. This modelling framework allows one to simulate the interpretation of ongoing actions by using temporal or propositional logics and operations management behaviors through plan specification and execution, action composition, and resource allocation to concurrent actions. It enables complex activity systems to be represented and management options to be tested by simulation. These capacities are illustrated on the example of a farming system. The main benefits and issues raised by this dynamical system approach close to the 'situated' (vs. 'planned') action paradigm are discussed in the light of related works in Artificial intelligence. Future directions of research are drawn, namely that of how to scale up this lower-level representation of action to the higher-level representation of agents endowed with skills relevant at the level of the individual (e.g., anticipation).