Content
The course offers a practical introduction to Data-Driven Supply Chain Management (DSCM) using machine learning methods. Based on an end-to-end case study and realistic data, students learn how both simple and advanced models (e.g., random forests or neural networks) can be used to support business decisions. The focus is on the step-by-step development, application, and evaluation of ML models. The content is conveyed using Python code, which is reviewed together and interpreted from a management perspective. This illustrates how different models use data, where their limitations lie, and what added value they offer for decision-making processes. The course combines presentations, videos, and interactive Jupyter Notebooks, allowing participants to conduct the analyses independently. Each unit is rounded off with practice-oriented tasks. Prior knowledge of Python is not required.
Structure
The course consists of an e-learning program provided by the Virtuelle Hochschule Bayern (vhb) for self-study and concludes with a written exam (60 min.).
Examination and Exam Numbers
Exam: 75021 (Written exam, 60 min.); 5 ECTS
If you wish to take the exam, you must register independently during the respective registration periods via both the vhb and campo. This ensures that our colleagues at the University of Würzburg are informed about your participation in the exam and that we at FAU can enter your grades. For FAU students, the exam will take place in person in Würzburg. Further information regarding the time and location of the exam will be announced after the exam registration period ends.
Access to the Course Platform (vhb course)
The course platform (wuecampus) can be accessed via the vhb course from the start of the lecture period. Link to the vhb course program.
Information
- Winter and summer term
- Language: German
- Registration via vhb
- Supervised by: Timo Mayer