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 the planning process.  Students have to apply the tools to an integrated real-world case study covering the 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, Basic Data Processing.

3. Data visualization. Geographical Information Systems and maps.

4. Hypothesis Tests, and Regression Analysis.

5. Linear Programming, Integer Linear Programming.

6. Applications of Integer Linear Programming in the context of Supply Chain Management.

7. Use of black-box solvers for real-world optimization.

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 data files in R.
•        Use visualization tools to identify patterns and trends.
•        Formulate and test hypotheses, and interpret their results in a business context.
•        Apply regression analysis, and interpret the results of such analyses to support data driven decision-making in a business context.
•        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.
•        Model optimization problems as integer linear programming (ILP) models.
•        Solve the ILP model through a state-of-the-art solver.
•        Interpret the results with regards to solution quality, bounds, optimality gap.
•        Understand the value and leverage potential of optimization from a business point of view.
•        Solve a concrete optimization problem on data coming from a real-world setting.

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

The course is taught using a combination of frontal lectures, class discussions, class exercises and in-class assignments for which students have to apply the used tools to solve the tasks. In addition, group homework assignments are based on an integrated case study where again the tools have to be applied.

•       Homework exercises, 30 points (5 homework assignments)
•       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%

According to WU's examination regulations, §3, Abs. 9, the following rule applies:

If a student does not attend the first session of a PI course without giving prior notice of his/her absence, he/she will be unsubscribed from the course. The next (present!) student from the waiting list will be enrolled thereafter.

There can only be as many students enrolled in the course as the maximum allowed number of participants.

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Basic knowledge of statistical regression and linear programming. The student must be familiar with coding in R.