Supply Chain Analytics
Efficient management of modern supply chains requires data-driven decision making. In this course, students will learn how to create separate optimization models to support decision making for transportation and production planning, as well as integrated models for coordinated decision making across multiple stages in a supply chain. Most of the deterministic modelling will be performed through the use of linear, mixed-integer and non-linear programming models, which are solved by standard solvers. However, also some basic heuristic methods are discussed in the course.
- Overview of supply chain optimization models
- Supply network design
- Production planning
- Production scheduling
- Transportation planning
- Vehicle routing
- Safety stock analysis using simulation models
- Multi-level inventory management
- Sales and operations planning
- Integrated decision making in supply chains
Learning outcome knowledge
- Students will develop knowledge in quantitative modelling to support decision making in supply chain design, planning and control.
- Students will gain basic knowledge about the design of heuristic methods for problem solving.
- Students will acquire knowledge about different types of supply chain uncertainty and how these can be handled in an analytical manner.
- Written assignment: 30%
- Written exam: 70%