As the environment of manufacturing enterprises is becoming more complex and dynamic this requires powerful tools for the support of decisions in manufacturing and production planning. Simulation as one of those tools has been widely applied in manufacturing and logistics. However, the models used for simulation are incomplete because the human factor in terms of its decision making and behaviour is considered only rudimentary. This leads to suboptimal decisions and to a gap when implementing solutions in practice. The paper presents an approach for enriching the common (technical) simulation models with need controlled human agents. The agent is based on a formalised motivation theory. In a feasibility study several typical settings, like ramp up curves and learning effects, were tested. The approach is unique in the field of simulation with a big potential for higher quality in analysing and designing production systems.