GRA 4138 Business Simulation Analysis

GRA 4138 Business Simulation Analysis

Course code: 
GRA 4138
Accounting and Operations Management
Course coordinator: 
Mehdi Sharifyazdi
Erna Engebrethsen
Course name in Norwegian: 
Business Simulation Analysis
Product category: 
MSc in Business Analytics
2021 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

The course objective is to familiarize the students with computer-based simulation methodologies and tools used in business settings characterized by uncertainty and variability. The main focus will be on discrete-event, agent-based and Monte Carlo simulations.

The course is mostly held in the form of learning-by-doing.

Learning outcomes - Knowledge
  • Knowledge of discrete-event simulation methodology and relevant background theories
  • Knowledge of Monte-Carlo simulation methodology and relevant background theories
  • Basic knowledge of agent-based simulation
Learning outcomes - Skills
  • Modelling and analysis of various systems using discrete-event simulation, agent-based simulation and Monte-Carlo techniques and tools
  • Effective interpretation and demonstration of model outputs
  • Sensitivity analysis, optimization and calibration by using simulation
  • Building and visualizing of simulation models and experiments in AnyLogic
General Competence
  • To identify real-world problems to which simulation methods are useful and relevant
  • To understand and appreciate the way uncertain and variable factors affect performance of various systems and how simulation helps to model, design, improve and analyse such systems
Course content

The course participants will learn how to model various systems, and study the effect on changes and uncertainties on the systems in order to identify bottlenecks and improvement areas. Examples of application areas of simulation include inventory and transportation systems, production environment, investments evaluation, queuing systems etc.  The course will focus on modelling and analyzing systems using Discrete Event Simulation, Agent-Based Simulation and Random event generators (Monte-Carlo simulations). The students will exercise on using simulation tools and on interpreting, presenting and animating the results. In addition, the students will learn to design and run various simulation experiments such as optimization, parameter variation and calibration.

Teaching and learning activities

The course consists of lectures, exercises, presentations and discus sions.

There will be guest lectures by practitioners about real-world applications of simulation. 

The following software tools are to be used during the course: 

  • Anylogic
  • Excel and relevant add-ins


Software tools
Software defined under the section "Teaching and learning activities".
Additional information

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.



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.


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.

Required prerequisite knowledge
  • Basics of probability and statistics theory
  • Basics of Microsoft Excel
Exam category: 
Form of assessment: 
Written submission
Group (1 - 3)
1 Semester(s)
Term paper (Project)
Groups with fewer than 3 members will be allowed only in exceptional cases.
Exam code: 
GRA 41381
Grading scale: 
Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Sum workload: 

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.