The identification and raising of potentials in complex systems, such as spare parts logistics, are becoming more and more important for supply chain management. The current increase in automation and mechanisation of production lines in the processing industry leads to considerable financial losses, due to line failure, if production is interrupted. In order to minimise this risk, more and more production companies are switching to storing their spare parts directly on site. However, companies will experience complexity in their spare parts logistics if the storage of spare parts is allowed to proliferate unchecked as the result of the quantities of parts and articles involved, taking account – firstly – of their relationships with their installation positions storage locations and – secondly – taking account of how they relate to each other. However, a spare parts logistics system that is effective and efficient in terms of complexity will secure not only low capital tie-up but also greater line availability and lower scrapping costs. This objective can be achieved subject to developing a theoretical model which points the way forward in terms of unlocking the potential for simplification of a spare parts logistics system by applying a complexity management process. Proceeding from the starting point of the theory of variant management and logistics – especially spare parts logistics and materials management – and applying the resultant theoretical knowledge, we prepare an optimisation model with specific recommendations for action, and we successfully optimise the level of complexity in the spare parts logistics system whilst fulfilling the stipulated level of service. The specific actions that are recommended (and correspondingly applied) effectively define the procedure for "recording, reducing, mastering and avoiding" system complexity within existing spare parts stocks. In the practical evaluation taken as the basis for this paper, a defined variant spectrum from two systems is taken as a reality pick and is loaded into the optimisation model, followed by the application – in co-operation with with materials management operators, with servicing providers and with wholesalers/manufacturers – of the best solutions from the specific procedural recommendations for each respective article, and this in turn provides the basis for setting the system’s optimised TARGET complexity as derived from the consideration of ACTUAL complexity. Finally, success indicators are used to show how a complex structure has been improved in correlation to the existing value thanks to connectivity and variety along the individual optimisation steps.