Supply Chain Analytics

Postgraduate course

Course description

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
    • Heuristics
  • 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.

Learning Outcomes

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

General competence

  • 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

ECTS Credits


Level of Study


Semester of Instruction


Place of Instruction

Required Previous Knowledge
Recommended Previous Knowledge
INF170 and either INF100 or INFO132
Credit Reduction due to Course Overlap
Access to the Course
The course has limited space. If there are more qualified applicants who wants to take the course than there are vacancies, the applicants will be prioritized as follows: 1. Students with the right to study Information Technology and Economics, Integrated Master's, 5 years. 2. Other students with the right to study at Faculty of Mathematics and Natural Sciences or Faculty of Social Sciences will be prioritized according to completed credits in recommended prior knowledge. 3. Other students with study rights at Faculty of Mathematics and Natural Sciences or Faculty of Social Sciences will be prioritized according to completed credits.
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
The forms of assessment are:
  • Project report (70%)
  • Oral exam (30%)
Grading Scale
Graded A-F
Assessment Semester
Assessment in teaching semester. Only students who have a valid document of absence will be entitled to take a new exam the following semester. 
Reading List
The reading list will be ready before June 1 for the autumn semester and December 1 for the spring semester. 
Course Evaluation
All courses are evaluated according to UiB's system for quality assurance of education. ¿  
Examination Support Material
Programme Committee
The Programme Committee is responsible for the content, structure and quality of the study programme and courses.  
Course Coordinator
Course coordinator and administrative contact person can be found on Mitt UiB. 
Course Administrator
The Department of Informatics at the Faculty of Mathematics and Natural Sciences has the administrative responsibility for the course.