Although automation is often seen as an efficient way to achieve cost-efficient production and to relieve humans from heavy or dangerous tasks, it also has its drawbacks. Earlier research has shown that increasing levels of automation in unforeseen production situations can be related to production disturbances. The human operator that can handle those unforeseen situations does not always have the ability to interpret present and future production situations, based on available information from the production system. The aim of this thesis is to theoretical and practicable development of the concept of Levels of Automation (LoA) in production systems and to improve the distribution of functions and tasks between humans and automation. This research also concluded and verified that the two reference scales presented for levels of automation are applicable to production tasks and that the level of automation in production systems can be assessed, measured and analysed with the DYNAMO methodology.