Identifying Errors in Supply Chain Processes with Process Mining

Seminar Paper , 2022 , 65 Pages , Grade: 1,3

Engineering - Industrial Engineering and Management

The following paper explains relevant fundamentals of the discussed topic with respect to process mining as well as for the understanding of supply chain processes. Consequently, in a second step, a literature review regarding the research field of process mining will be unfolded by retracing its design and conduction after elaborating a well-defined search phrase based on the research question. This paper then advances by summarizing the results of the literature review. The state-of-the-art research with respect to the specific topic of this very paper are summarized and commented subsequently, and if applicable, taken up in later course. In this regard, special challenges and approaches will be mentioned. The third part of this paper deals with the simulation as basis for conducting cross-organizational process mining. Here, the setting, the char acteristics, the simulation model, and process errors will be explained. The resulting event log is going to be analyzed and interpreted by comparing to the input model.

In the last part, the event data of the supply chain process simulation serves as input for cross organizational process mining. The results of the application will be analyzed and viewed at with specifically focusing context-related errors followed by evaluation of the process model considering the input-output relation with the event log from the simulated supply chain process. As a conclusion, the results will be discussed and compared to other research results and questions will be raised for further research.

• College: Technical University of Darmstadt (Production and Supply Chain Management)
• Course: Industrial Management
• Grade: 1,3
• Author: Max-Leonhard Böttrich (Author)
• Publication Year: 2022
• Pages: 65
• Catalog Number: V1254759
• ISBN (PDF): 9783346690968
• ISBN (Book): 9783346690975
• Language: English
• Tags: Process Mining, Supply Chain Management, Event Log, Data-Driven