Lectures
Description
For a detailed description, dates and updates visit the course site from the chair for business journalism. Further information can be found at http://wiwi-trifft-praxis.de/.
Please contact the office of the Chair of Logistics and Quantitative Methods (bwl11-office@uni-wuerzburg.de) if you have any further questions.
Registration
Please register through wuestudy.
Contact
Dr. Richard Pibernik, Professor (richard.pibernik@uni-wuerzburg.de)
Prof. Dr. Otto (kim.otto@uni-wuerzburg.de)
Dr. Christian Klöcker (christian.kloecker@uni-wuerzburg.de)
Registration
You have to register through the VHB website (search for Data-Driven Supply Chain Management), not through WueStudy!
The course registration is typically open until the final weeks of the respective regular lecture period.
Dates
This course is offered every summer and winter term.
ECTS
5 CPs.
Description
A detailed description of the course content and structure is available on the VHB page for our course (search for Data-Driven Supply Chain Management). A demo of the course can be found here.
This course provides students with a very practical, hands-on introduction to Data-driven Supply Chain Management (DSCM) using Machine-Learning (ML) techniques. Based on a specific example and dataset from practice, students will learn how simple and more advanced ML techniques (e.g. Neural Networks, Random Forests) can support decision makers in using extensive data to come up with better decisions in Supply Chain Management.
The course is structured around a single case example and a single set of data and will gradually introduce participants to fundamental and more advanced concepts of DSCM. In particular, students will learn how to build, employ, and evaluate simple and more advanced ML-models that can be directly used in practice. The individual lectures will introduce participants to the (Python-) code of the relevant ML-models, explain the workings of the code and interpret the outcomes from a managerial perspective. Students will be able to observe how different ML-models can be employed, how they make use of the data available to the decision maker, where they fail and where they provide useful decision support.
Within this course we make use of novel teaching formats: Each of the core sessions provides students with a presentation and accompanying video, as well as a Jupyter Notebook that allows for a (real-time) step-by-step replication and execution of the Python code that is underlying the models and individual calculations. Each of the core sessions concludes with an online assignment in which students use the Jupyter Notebooks to solve practically relevant problems.
The course is designed in such a way that students do not need prior experience in coding in Python. Our method of instruction will ensure that students understand the code and can execute the code – even without prior experience. Students who are more versed in coding and/or want to delve deeper into the implementation of the models may do so – we provide extensive supplemental material and tools for this purpose.
Contact
Nikolai Stein (nikolai.stein@uni-wuerzburg.de)
