With relevance tree analysis, starting from a cleanly defined target state, the development phases can be derived retrogradely, describing a path from the current situation to the target state. Relevance tree analysis is also based on the development of SD models. Provided that interactions are represented, it is well suited for the treatment of complex problems. First, the effect variables that directly influence the target state are determined. Then, for each effect variable, it is determined which characteristics affect it, and so on. At the same time, the relevance of each effect variable on the target state is assessed, as well as how the characteristics must be pronounced in order to promote the achievement of the target state. The outer characteristics are the variables that can be directly influenced (influence variables), while the characteristics located further inside the relevance tree often merely indicate effects but cannot be directly influenced (indicator variables). Relevance tree analysis helps to focus on activities that contribute something to the achievement of the main objective and also helps to distinguish between important and less important activities and to increase both the effectiveness and efficiency of implementation projects.