Data Science in Supply Chain Management (2023/2024)

Embedded in the theoretical context of supply chain management, this course deals with the fundamentals and methods of data science and analytics. By the end of the course, students should be able to independently work on a small data science project in the field of supply chain management in the form of group work. In doing so, they will go through all the steps of the data science pipeline and implement them in practice using R or Python.

Key learning content includes:

Introduction to the fundamentals of data science and analytics in the context of supply chain management Examination of relevant methods, particularly in the fields of artificial intelligence and machine learning Comprehensive explanation of the entire data science pipeline, from the formalization of a use case to data import, data visualization, data cleansing, the application of various analysis methods, and the development of user interfaces and the deployment of corresponding solutions on a cloud infrastructure Treatment of various application examples from the field of supply chain management (e.g., demand forecasts, price forecasts) from the literature Joint work on a case study on the use of data science and analytics methods in supply chain management Current guest lecture from the field; the respective topic will be announced separately in the lecture

Translated with DeepL.com (free version)