Themen für Abschlussarbeiten

Ideally, you have your own idea for a thesis in the subject area of my research field. 

In addition, you will find a current list of possible thesis topics (partly in co-operation with an industrial partner):

• Empirical causality analysis between logistics and economic performance 
  
• Development of an economic indicator using dynamic factor models 
  
• Modeling the impact of firm sustainabilty KPIs and business success 
  
• Development of a machine learning model for firm survival prediction 
  
• Comparison of Machine Learning Algorithms for Demand Forecasting in the Automotive Supply Chain
  
• Demand Forecasting of Forward and Backward Logistics in a Digital Twin Setup
  

You can write your thesis in English or German.

If you are interested, please send me an e-mail with sufficient lead time for initial consultation.

Teaching Areas

Winter Semester:

• Sustainability Management
  

Summer Semester:

• Digitalization of Business Processes
• Data Preparation and Processing
• Logistics Specialization: Forecasting, Machine Learning, and AI
• Business Analytics Specialization: Git and Version Control, Python and R Programming, Time Series Analysis, Forecasting

Research Areas

• Hierarchical Forecasting
  
• Forecast Reconciliation
  
• (Mixed Frequency) Dynamic Factor Models
  
• Foundations Models
  
• Digital Twins
  
• Sustainable Supply Chain Analytics
  
• Few Data Learning / Data Augmentation
  
• Production, Logistics, Supply Chain Management
  

Publications

• Beinert, D. und Menden, C. (2026). "Sparse versus Dense: Forecasting Germand GDP under Structural Breaks". Conference Proceedings, Würzburg International Business Proess, 2026, pp. 41-44.
  
• Beinert, D.; Bönke, T.; und Menden, C. (2025). "Tracking the Economic Performance of Germany’s SMEs in Real-Time“. Conference Proceedings, Würzburg International Business Press, 2025, 190 pp.
  
• Menden, C.; Müller-Steinfahrt, U.; Gölzer, P.; und Cato, P. (2025). "Forecasting the Demand for Recycled Plastics using a Digital Twin for Regional Plastic Cycles“. Conference Proceedings, Würzburg International Business Press, 2025, 190 pp.
  
• Menden, C. (2022): "Handling Data Problems in Machine Learning Applications in Supply Chain Management", in Pflaum, A.: Schriftenreihe Logistik und Informationstechnologien, Band 10, Fraunhofer Verlag, 2022
  
• Bärmann, A.; Mehringer, J.; Menden, C.; Neumann, U.; Schemm, J.; Schneider, O. et al. (2021): "Data Analytics in der Supply Chain: Fraunhofer IIS". Fraunhofer IIS Whitepaper.
  
• Menden, C.; Sonnleitner, B. (2020): "Towards a Forecasting Approach of Transportation Volume with Externally Augmented Data".
  
• Menden, C.; Mehringer, J.; Martin, A.; Amberg, M. (2019): "Vorhersage von Ersatzteilbedarfen mit Hilfe von Clusteringverfahren". In: HMD, Vol. 56 , No. 5, pp. 1000–1016.
  
• Menden, C.; Proaño, C. R. (2017): "Dissecting the financial cycle with dynamic factor models", Quantitative Finance, Vol. 17, No. 12, pp. 1965–1994.
  

Short CV

Christian Menden was appointed as a Research Professor for Sustainability and AI in Corporations and Supply Chains at the THWS Business School in October 2023.

After completing a bachelor’s degree in finance, he studied economics with a focus on statistics. He worked in banking in Frankfurt and London, and at Mountains of the Moon University in Uganda and the University of Bamberg. His doctoral research examined how data augmentation methods can be used to apply machine learning techniques in supply chain management when data is limited or difficult to obtain.

Before joining THWS, he worked as a research associate at the Chair of Applied Economic Research at the University of Bamberg. He then joined the Supply Chain Services (SCS) working group at the Fraunhofer Institute for Integrated Circuits IIS in Nuremberg as a data scientist in the Data Science and Optimization group, which he began leading in 2018.

In 2019, he took over as head of the Analytics division, comprising four groups: Process Intelligence, Data Science, Optimization, and Data-Efficient and Automated Learning.

Since 2017, he has taught as an adjunct lecturer at Technical University of Applied Sciences Nuremberg, THWS, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), and the University of Bamberg.

In his research, he investigates methods and applications of artificial intelligence for sustainability in production, logistics, and supply chain management.