Automated process improvement based on process mining and unstructured data

Over the past few years, the technology of process mining has become the backbone of many organizations’ process improvement initiatives. At its core, the technology automatically reconstructs how a process was executed by analyzing data readily available in today’s information systems. However, inferring what kind of problems exist in a process and where these exist still requires the extensive involvement of domain experts. In our research, we aim to address this issue by developing a process mining technique that leverages unstructured data (e.g., social media posts) and machine learning to automatically identify and eliminate process weaknesses.

Alexander Rochlitzer is a PhD candidate in the fields of Data Science and Process Analytics at the Kühne Logistics University under the supervision of Prof. Dr. Henrik Leopold and Prof. Dr. André Ludwig since September 2021. His research focuses on the interplay between business processes, information systems and artificial intelligence. Alexander received his B.A. in Business Administration from the Berlin School of Economics and Law and his M.Sc. in Global Logistics and Supply Chain Management from the Kühne Logistics University.