Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Institut für Informatik

Abstract:
Process mining is a data-driven analytical approach used to understand and enhance business processes. In the manufacturing domain, it involves collecting and analyzing event data to uncover, examine, and improve the underlying manufacturing processes. This method provides valuable insights into process execution, performance, and compliance, enabling manufacturers to optimize operations, reduce costs, and enhance overall efficiency. Despite extensive research on process mining in manufacturing, its implementation still encounters several challenges. This presentation aims to shed light on the practical application of process mining in addressing real-world industrial issues within the manufacturing sector. It explores various application cases, such as quality management, yield management, logistics management, and digital twin generation, with a specific emphasis on process mining applications at manufacturing companies in South Korea, such as Samsung Electronics and POSCO. To effectively apply existing process mining techniques to authentic industrial problems, certain expansions are required. Therefore, this talk also outlines how process mining techniques can be utilized and extended within these applications.

Short Bio:
Dr. Minseok Song is a Full Professor in the Department of Industrial & Management Engineering at Pohang University of Science and Technology (POSTECH). He received his Ph.D. degree from POSTECH's Department of Industrial & Management Engineering in 2006. Prior to his current position, he served as a Postdoctoral Researcher in the Information Systems Group at the Department of Industrial Engineering & Innovation Sciences at Eindhoven University of Technology from 2006 to 2009. From 2010 to 2015, he held the positions of Assistant Professor and Associate Professor at the Ulsan National Institute of Science and Technology (UNIST). With over 130 scientific papers published in prestigious journals such as Decision Support Systems, Information Systems, Journal of Information Technology, International Journal of Medical Informatics, Journal of Biomedical Informatics, and IEEE Transactions on Semiconductor Manufacturing, he has made significant contributions to the field. He has also led more than 30 research projects, securing total funding of $6 million USD and applying industrial engineering principles and information technologies to address real-world problems. His notable research grants include the EU Horizon 2020 (RISE-BPM) project, collaborating with eight universities across Europe, Australia, South America, and Korea. Additionally, he has been the recipient of early-career and mid-career researcher grants from the National Research Foundation of Korea. The methodology and software systems he has developed are widely utilized in esteemed organizations such as Samsung Electronics, Samsung C&T, POSCO, and Samsung Heavy Industry.

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