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TU Berlin

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Quantitative Decision Making for Business Operations

Summer University Term 3: July 23rd - August 16th


Course price: 1850 Euros

18 hours of class a week, 5 ECTS, max. 24 participants


Data Science as well as predictive analytics are the baseline of Artificial Intelligence. Understanding how machines learn and analyze data will be a major driver of the future job market. We will get a peek into how automated and model based problem solving works, as applied by machines, and the flaws these decisions might have.

We want to analyze examples from operations management considering supply chains, as well as production and service environments in a quantitative manner. Students should be able to work with real world data, analyze, visualize, draw conclusions and come up with optimal management decisions. Students will be given a short introduction into analyzing and visualizing data using Excel spreadsheets, if time allows using the R and Julia programming languages.

They will be able to draw conclusions using different kinds of regression and smoothing methods on the data provided.

To handle uncertainty in real world environments students will cover methods like simulation and different models of stochastic programming to include uncertainty into their decision making process.

This course is aimed at participants interested in quantitative modeling (including the math and probabilistic background) who are motivated to learn programming to apply the newly learned methods.

Learning Goals & Syllabus


After the course students will be able to:

  • Analyze real data, identify and weight influence factors as well as trends.
  • Simulate outcomes and develop models to base their decision on in deterministic as well as stochastic environments.


Please find the schedule for this course here: Course Syllabus. 

You may find the syllabus useful when discussing with your home University whether the ECTS credits attainable for this course are accepted by them.

Course components

  • Interactive classroom-based lectures and discussions
  • Group work projects and presentations
  • Class room competitions on supply chain management and production management
  • Quantitative modeling sessions using Excel spreadsheets as well as R or Julia (if time allows)
  • Guest lectures e.g. from academia or industry in related data-driven topics
  • Excursions to industrial and logistics sites in and around Berlin.


The general prerequisites of the TU Berlin Summer University are the following: at least one year of university experience + English level B2 or equivalent.


Tino Herden (M.Sc.)

Lissy Langer (M.Sc.)

as well as guest lecturers

Lecturers are research associates at the School of Economics and Management at TU Berlin.

Tino Herden is affiliated with the Chair of Logistics and gives lectures on Information, Identification and Automating Technologies in Logistics as well as Data Analytics.


Lissy Langer is affiliated with the Chair of Operations Management and gives lectures on Production and Service Management as well as Stochastic Models in Operations Management.

Please direct questions about the course to the TU Berlin Summer University Team at: . We will answer your questions and direct specific queries regarding course content to the course lecturers where necessary.

Brochure & Poster Download

Check out our Brochure and Poster for Summer 2018! 

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Zusatzinformationen / Extras

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Auxiliary Functions


Beth Sibly, Acting Director
+49 30 44 72 02 30