Understanding Process Mining for Data-Driven Optimization of Order Processing

Abstract The ability to design agile and efficient business processes is a decisive success factor for companies in increasingly dynamic and complex business environments. End-to-end order processing is of particular importance in this context. Traditional approaches to optimize the order processing are often one-time snapshots in subjective workshops that require high manual efforts. A modern approach for process optimization is process mining. However, process mining has so far only been applied to specific parts of the end-to-end order processing. Companies have not yet succeeded in extending the application of process mining to end-to-end order processing because they lack the necessary know-how about the method. A process mining demonstrator can help to explain the functionality, potential and limitation of this method to employees by visualizing the connection between the physical process and its digital image. The aim of this paper is to develop a process mining demonstrator for learning factories. The parallel experience of physical process runs and the creation of their digital images on end-to-end process level helps to teach the functionality of process mining to employees in a learning factory environment.