For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model's application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system.