Alexander Rochlitzer ist seit September 2021 PhD-Doktorand:in in den Bereichen Data Science und Prozessanalytik an der Kühne Logistics University unter der Betreuung von Prof. Dr. Henrik Leopold und Prof. Dr. André Ludwig. Er hat seinen M.Sc. in Global Logistics and Supply Chain Management an der KLU erworben und einen Bachelor-Abschluss (B.A.) in Business Administration mit den Schwerpunkten Operations und Supply Chain Management an der Berlin School of Economics and Law absolviert. Alexander forscht an der Schnittstelle zwischen Geschäftsprozessmanagement und künstlicher Intelligenz. Konkret konzentriert er sich darauf, Technologien wie maschinelles Lernen zu nutzen, um Feedback aus Social-Media-Beiträgen effizient in Geschäftsprozesse zu integrieren. Sein Interesse daran, das Potenzial digitaler Technologien für die Prozessoptimierung zu untersuchen, wurde geweckt, als er an der Einführung eines ERP-Systems bei der ESYS GmbH mitwirkte.
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. |
Publikationen
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.
