1. Introduction to the course, concepts of business analytics, introduction to methods used in the course.
2. Problems in linear programming
3. Optimization and algorithms on networks.
4. Problems with integer variables.
5. Applications of statistical inference.
6. Generative artificial intelligence in business analytics.

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:

• Choose the suitable optimization tool for real-world business problems.
• Formulate and solve decision problems in production, finance, and marketing as linear programs.
• Implement and solve a linear programming model through a spreadsheet editor and a Simplex solver.
• Learn the terminology of graphs and networks.
• Design and solve three algorithms on networks (shortest path, minimum spanning tree, and project scheduling).
• Recognize and formulate three integer problems frequently arising in business settings (knapsack problem, set overing problem, and traveling salesman problem)
• Solve a concrete optimization problem on data coming from a real-world setting.
• Read data with R
• Visualize results on a geographical map.
• Perform an inference analysis and fit a linear regression model in R.
• Have a critical view on the potentials and limitations of generative AI for optimization and business analytics.
• Learn how to prompt a generative AI tool to model mathematically an optimization problem.

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 assignments.

In-class assignments 25 points (to be solved in class, 6 assignments, 5 points each, 5 best scores are counted towards the grade)
Written Exam 40 points
Oral Exam 35 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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