GRA 4141 Supply Chain Analytics
GRA 4141 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.
- 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.
- Students will learn how to create mathematical programming models for various types of production and transportation problems.
- Students will learn how to take into account stochastic parameters and analyze the effect of various buffering techniques through the use of discrete event simulation models.
- Students will learn how different functions and stages in a supply chain can be coordinated through the use of optimization models for integrated decision making.
- Students will obtain an overview of challenges and solutions for supply chain analytics.
- Based on this course, the students will be better equipped to reflect upon and evaluate various types of analytical software offered to decision makers in a given practical setting.
- 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
Software:
- AMPL or similar mathematical programming software.
- AnyLogic or similar discrete event simulation software.
- Python or similar general programming software.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on itslearning or text book.
All parts of the assessment must be passed in order to get a grade in the course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
This course requires that students have completed the course in Business Optimisation or an equivalent course.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 30 Grouping: Group/Individual (1 - 3) Duration: 7 Week(s) Comment: Written examination under supervision. Exam code: GRA 41411 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 70 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: GRA 41412 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.