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Due to global competition and increasingly complex business models, the pressure on companies is constantly increasing. Value chains are becoming longer, delivery bottlenecks have to be taken into account and customers' demands for high product quality are increasing. Apart from the requirements resulting from the different business areas, information systems are becoming more and more important in companies and business processes are increasingly relying on the data generated by such information systems.
The aim of this thesis is to answer the question of how business processes can be created in a data-driven manner using process mining in order to gain a better insight into the company-wide target processes. In particular, the points that need to be considered when introducing data-driven process models and how such a process model can look in practice were discussed. Apart from the realization of such process models, they were compared with conventional process modeling and the corresponding advantages were worked out.
The process model was carried out on the basis of a fictitious implementation using the example of an order-to-cash process. Important topics in connection with the actual implementation were worked out and presented. In this case, the scenario describes a concrete application example.
In order to assess whether process mining actually provides added value compared to conventional process modeling methods, challenges in this context were identified by means of a literature review. Subsequently, suggestions were made as to how these challenges can be solved using process mining.
