Supply Chain Analytics
- ECTS credits
- Teaching semesters
- Course code
- Number of semesters
Objectives and Content
The course aims to give the students
- an overview of supply chain management,
- understanding of how to use advanced optimization techniques and artificial intelligence (AI) algorithms to solve and analyze decision problems, and
- ability to solve decision problems occurring in different segments of a supply chain, with a focus on the transportation and logistics industry.
The topics that are covered in this course includes (but are not limited to):
- Demand forecasting
- Time Series analysis (Cumulative, Naïve, Moving Average, Exponential Smoothing)
- Regression analysis
- Inventory Management
- Economic Order Quantity (EOQ)
- Single period inventory models
- Probabilistic inventory models
- Production and Scheduling
- Optimization models
- Supply Chain Network Design
- Network optimization
- Facility location problems
- Covering problems
- Freight Transportation:
- Last mile delivery (case studies)
- Maritime transportation (case studies)
- Future of delivery systems
The course contains a wide range of practical optimization problems in supply chain as case studies.
On completion of the course, the student should have the following learning outcomes defined in terms of knowledge, skills and general competence:
- The student has a basic understanding of the optimization and decision making problems that exist in a supply chain
- The student has a basic understanding of optimization and artificial intelligence algorithms
- The student is able to apply an optimization/artificial intelligence algorithm to solve a wide range of problems in supply chain including (but not limited to) supply chain network design, freight transportation problems, production and scheduling
- The student is able to combine techniques from optimization and AI with insight in economics (acquired in other courses), to improve a company's logistics solutions
- The student is able to build mathematical models for simple network optimization problems as well as production and scheduling problems
- The student is able to apply regression and time series models on historical data to forecast the future demand
- The student is able to make decisions on simple inventory replenishment problems
- The student is able to discuss successful examples of how optimization and artificial intelligence algorithms can be used in making better decisions in a supply chain
- The student is able to distinguish between different optimization techniques
Level of Study
Semester of Instruction
Place of Instruction
Required Previous Knowledge
Credit Reduction due to Course Overlap
Access to the Course
Teaching and learning methods
The teaching is given in terms of lectures and group sessions.
Lectures / 4 hours per week.
Group sessions / 2 hours per week.
Compulsory Assignments and Attendance
Compulsory assignments and a project.
Compulsory assignments are valid for one subsequent semesters.
Forms of Assessment
- Project report (70%)
- Oral exam (30%)