1421 Business Analytics II
Martin Hrusovsky, Ph.D.
  • LV-Typ
  • Semesterstunden
  • Unterrichtssprache
03.10.2019 bis 07.10.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Bachelor
Wochentag Datum Uhrzeit Raum
Mittwoch 30.10.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 06.11.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 13.11.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 04.12.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 11.12.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 18.12.2019 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 08.01.2020 15:00 - 18:00 LC.2.064 Raiffeisen Kurslabor
Mittwoch 15.01.2020 08:00 - 09:30 LC.-1.038

Inhalte der LV

The course builds upon methods and tools introduced in Business Analytics 1. It deepens the skills of the students by applying the tools to problems within the supply chain management area. Beginning with basic data handling skills and progressing through statistical and operations research methods, students get to understand the complexity of business decisions and dependencies between different stages of forecasting and planning processes.  Students have to apply the tools to an integrated real-world case study covering all topics included in the course. In this context, identification of input factors and underlying assumptions as well as correct interpretation of results are of high importance. Topics include:

1. Introduction to Supply Chain Management

2. Basic Data Handling

3. Basic Data Processing

4. Data visualization

5. Hypothesis Tests

6. Analysis of Variance and Regression

7. Forecasting

8. Optimization

9. Simulation

Lernergebnisse (Learning Outcomes)

After attending this course, students will be able to understand and apply the principles, methods and tools of business analytics to problems in the supply chain management area. This includes how to:

  •        Handle big data files in R and Excel
  •        Use visualization tools to identify patterns and trends
  •        Formulate and test hypothesis, and interpret their results in a business context
  •        Apply analysis of variance and regression analysis, and interpret the results of such analyses to support data driven decision-making in a business context
  •        Forecast based on historical data
  •        Develop and apply simulation models for decision support
  •        Formulate and solve decision problems in transport and production context as linear programs
  •        Interpret the results at each stage and use them for the subsequent planning stages
  •        Visualize results on a map

Regelung zur Anwesenheit

Attendance requirement is met if a student is present for at least 80% of the lectures.


The course is taught using a combination of lectures, class discussions, in-class assignments and practical application of the tools and methods to an integrated case study and other examples.

Leistung(en) für eine Beurteilung

  •        Homework exercises, 30 points (6 homeworks)
  •        In-class assignments, 30 points
  •        Final exam, 40 points


In order to pass the class, you need attend at least 80 % of all classes. If you fulfill these criteria, the following grading scale will be applied:

  •        Excellent (1): 87.5% - 100.0%
  •        Good (2): 75.0% - <87.5%
  •        Satisfactory (3): 62.5% - <75.0%
  •        Sufficient (4): 50.0% - <62.5%
  •        Fail (5): <50.0%
Zuletzt bearbeitet: 10.07.2019