Alexander Rochlitzer is a PhD candidate in the fields of Data Science and Process Analytics at Kühne Logistics University under the supervision of Prof. Dr. Henrik Leopold and Prof. Dr. André Ludwig since September 2021. He received his M.Sc. degree in Global Logistics and Supply Chain Management from KLU and completed a Business Administration programme (B.A.) with a focus on Operations and Supply Chain Management at the Berlin School of Economics and Law. Alexander is conducting research at the intersection of business process management and artificial intelligence. Specifically, he focuses on leveraging technologies such as machine learning to efficiently integrate customer feedback described in social media posts into business processes. He became interested in examining the potential of digital technologies for process improvement when he was involved in implementing an ERP system at ESYS GmbH.
Contact
Networks
Professional Experience
| 2019 | Junior-Analyst, Miebach Consulting GmbH, Frankfurt am Main, Germany. |
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| 2019 | Intern, Miebach Consulting GmbH, Frankfurt am Main, Germany. |
| 2018 | Intern, VR Equitypartner GmbH, Frankfurt am Main, Germany. |
| 2018 | Intern, Ernst & Young GmbH, Eschborn, Germany |
| 2017 | Intern, BDO AG, Berlin, Germany |
| 2015 - 2016 | Intern / Consultant, ESYS GmbH, Berlin, Germany |
| 2013 - 2014 | Intern, Möller Ventures GmbH, Berlin, Germany |
Education
| Since 2021 | PhD Candidate in Data Science and Process Analytics at the Kühne Logistics University, Hamburg, Germany. |
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| 2019 - 2021 | Master of Science in Global Logistics and Supply Chain Management, Kühne Logistics University, Hamburg, Germany. |
| 2018 | Master of Science, 1st Quarter, Finance, Frankfurt School of Finance & Management, Frankfurt am Main, Germany. |
| 2013 - 2017 | Bachelor of Arts in Business Administration, Berlin School of Economics and Law, Berlin, Germany. |
Publications
A key business challenge of process mining is to appeal to decision-makers who seek to differentiate,
with the ambition to go beyond operational optimization. One way to position process mining as a
differentiator is to integrate operational process and experience journey perspectives, with the ultimate
goal to better align operations with the needs of customers and other external stakeholders. To exemplify
this direction, this demonstration presents SAP’s journey-to-process analytics capabilities that fuse
experience with process data, allowing organizations to generate insights about how operations affect
experience.
A key business challenge of process mining is to appeal to decision-makers who seek to differentiate, with the ambition to go beyond operational optimization. One way to position process mining as a differentiator is to integrate operational process and experience journey perspectives, with the ultimate goal to better align operations with the needs of customers and other external stakeholders. To exemplify this direction, this demonstration presents SAP’s journey-to-process analytics capabilities that fuse experience with process data, allowing organizations to generate insights about how operations affect experience.
GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.
GPT-3 and several other language models (LMs) can effectively address various natural language processing (NLP) tasks, including machine translation and text summarization. Recently, they have also been successfully employed in the business process management (BPM) domain, e.g., for predictive process monitoring and process extraction from text. This, however, typically requires fine-tuning the employed LM, which, among others, necessitates large amounts of suitable training data. A possible solution to this problem is the use of prompt engineering, which leverages pre-trained LMs without fine-tuning them. Recognizing this, we argue that prompt engineering can help bring the capabilities of LMs to BPM research. We use this position paper to develop a research agenda for the use of prompt engineering for BPM research by identifying the associated potentials and challenges.
