Goal models are used in early development phases to specify the system under development and conduct early feasibility analyses. Therefore, goal models show an abstract representation of the system not detailing the system behavior, which is typically specified in later stages. In the development of robotic production systems, the execution order of different tasks is important and must be considered when deciding upon feasibility of approaches and weighing different solution alternatives. The order of production steps can be easily integrated into goal models, as this can be behavior specified on a very high and abstract level. However, integrating more and more complexity into a goal model bears the risk of reducing its comprehensibility. In this paper, we report a controlled experiment investigating the effect the introduction of process step execution orders within goal models of robotic production systems has on the models’ comprehensibility. Results show that the introduction of execution orders can even increase comprehensibility.