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Process Mining as Alternative to Traditional Methods to Describe Process Performance in End-to-End Order Processing of Manufacturing Companies

Günther Schuh 1, Andreas Gützlaff 1, Seth Schmitz 1, Calvin Kuhn 1, and Noah Klapper 1.2
1. Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University, Aachen, Germany
2. Digital Capability Center (DCC) Aachen / ITA Academy GMBH, Aachen, Germany
Abstract—The description of process efficiency remains a key factor for manufacturing companies competing in volatile markets. Since describing the process performance requires the consideration of all order-fulfilling activities, focusing on the end-to-end order processing process is crucial. Classical techniques for process description are time- and cost-intensive while relying on situational impressions. Consequently, improvement approaches are based on gut feelings and cannot consider dynamic process behaviour. Process Mining can be used for fact-based and objective process descriptions. However, today’s process mining applications are mainly conducted in partial processes with similar order types. In the end-to-end order processing, multiple orders with one-to-many and many-to-many relationships exist that need an object-centric process mining approach. This paper presents a methodology for the application of process mining in end-to-end order processing with multiple order types. Based on data from software infrastructure, the integration of the methodology provides manufacturing companies with process models and process performance indicators to describe their PP in end-to-end order processing processes.

Index Terms—object-centric process mining, order pro-cessing, manufacturing companies

Cite: Günther Schuh, Andreas Gützlaff, Seth Schmitz, Calvin Kuhn, and Noah Klapper, "Process Mining as Alternative to Traditional Methods to Describe Process Performance in End-to-End Order Processing of Manufacturing Companies," Journal of Advanced Management Science, Vol. 10, No. 1, pp. 1-8, March 2022. doi: 10.18178/joams.10.1.1-8
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