The conditions for industrial companies are changing due to increasing customer demands for individualisedas well as sustainable products. Furthermore, companies are confronted with technological change by digitaltransformation. Therefore, production planning has to address various structural, procedural andorganisational changes. Planning projects are often characterised by a high degree of complexity. In orderto master the associated challenges, simulation models are used in production planning. In contrast tomathematical-analytical methods, simulation models examine and assess especially complex productionsystems and support improvement measures. A major difficulty during the model initialisation and thedetermination of the planning variables is the capture of data and the assurance of sufficient data quality.Both are associated with a high expenditure of time. At this point, manufacturing companies are faced witha conflict of objectives between the reduction of the planning time and the development of reliable simulationmodels. Process Mining (PM) can be used to capture data from central information systems and to uncoversocial and organisational networks and map them in a process model. This can create a well-founded databasis for simulation models.To support simulation models within the planning process, a methodology linking process mining andsimulation has been developed. This methodology improves the database within the planning process andrenders it usable for rescheduling production systems. Potentials that can be achieved in the areas of dataacquisition, data quality and model building are systematically analysed. The approach is validated on thebasis of a use case from the pharmaceutical industry.