Mr. Sebastian Paull, University of Mannheim, Germany
Sebastian Paull is a highly qualified researcher in supply chain science, with expertise in probabilistic forecasting, stochastic optimization, and big data analytics. His research has practical relevance, offering decision-making improvements beyond traditional forecasting methods. His industry experience and leadership further validate his contributions. However, enhancing publication records and expanding interdisciplinary impact would further strengthen his candidacy.
Publication Profile:
Orcid
Education & Training 🎓
Sebastian Paull is currently a doctoral candidate at the University of Mannheim (2020 – Present), focusing on integrating machine learning-based probabilistic forecasting and stochastic optimization into supply chain planning. He holds a Master of Science in Business Informatics (1.9) from DHBW Karlsruhe & EnBW (2016 – 2018) and a Bachelor of Science in Business Informatics (2.2) from the same institution (2012 – 2015).
Work Experience 💼
Sebastian has extensive experience in supply chain analytics, optimization, and IT risk assurance. Currently, he is a Lead Supply Chain Scientist at Aioneers (October 2023 – Present), where he leads an international data engineering team, facilitates cross-regional collaboration, and develops technology solutions for supply chain optimization. Previously, he worked as a Senior Supply Chain Scientist (April 2020 – September 2023), conducting demand forecasting, cluster analysis, and production optimization. His expertise in dashboarding and supply chain data modeling has helped organizations improve inventory management and reduce operational costs.,Earlier, he served as a Supply Chain Analyst at EY Mannheim (2020 – Present), focusing on demand forecasting, portfolio analysis, and IT risk assessment. Before that, as an IT Risk Assurance Consultant at EY Frankfurt (2018 – 2020), he optimized IT processes, ensured regulatory compliance, and advised businesses on secure technology adoption.
Research Focus 🔬🧪
Sebastian’s research bridges theory and practice in supply chain management, emphasizing ML-based probabilistic forecasting and stochastic optimization to address demand uncertainties. His work demonstrates the advantages of neural networks over traditional point forecasts, particularly in managing complex demand patterns. He provides practical, adaptable approaches to supply chain practitioners, advocating for a gradual transition from simpler models to probabilistic forecasting techniques to improve real-world decision-making.
Awards and Recognitions:
Sebastian has contributed to multiple high-impact projects in supply chain optimization and data science. His work has been recognized for enhancing supply chain performance, improving IT risk assurance, and developing advanced forecasting methodologies. His academic research and professional contributions position him as a leader in data-driven supply chain optimization.
Skills :
Sebastian specializes in probabilistic forecasting, stochastic optimization, and big data processing. He has extensive experience with PySpark on Databricks, Pyomo for optimization modeling, and machine learning techniques for demand forecasting. His expertise extends to dashboarding and Business Intelligence tools, enabling data-driven decision-making in supply chain management.,Additionally, he is fluent in German (native), English (native), Swedish (C1), Spanish (B1), and French (A2).
Publication Top Notes
Machine Learning for Master Production Scheduling: Combining probabilistic forecasting with stochastic optimisation
Spinning gold from straw – evaluating the flexibility of data centres on power markets