The conditions for industrial companies are changing due to increasing customer demands for individualised
as well as sustainable products. Furthermore, companies are confronted with technological change by digital
transformation. Therefore, production planning has to address various structural, procedural and
organisational changes. Planning projects are often characterised by a high degree of complexity. In order
to master the associated challenges, simulation models are used in production planning. In contrast to
mathematical-analytical methods, simulation models examine and assess especially complex production
systems and support improvement measures. A major difficulty during the model initialisation and the
determination 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 with
a conflict of objectives between the reduction of the planning time and the development of reliable simulation
models. Process Mining (PM) can be used to capture data from central information systems and to uncover
social and organisational networks and map them in a process model. This can create a well-founded data
basis for simulation models.
To support simulation models within the planning process, a methodology linking process mining and
simulation has been developed. This methodology improves the database within the planning process and
renders it usable for rescheduling production systems. Potentials that can be achieved in the areas of data
acquisition, data quality and model building are systematically analysed. The approach is validated on the
basis of a use case from the pharmaceutical industry.
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