EBA 3610 Decision Modelling Using Spreadsheet

EBA 3610 Decision Modelling Using Spreadsheet

Course code: 
EBA 3610
Department: 
Accounting and Operations Management
Credits: 
7.5
Course coordinator: 
Erna Engebrethsen
Atle Nordli
Course name in Norwegian: 
Decision Modelling Using Spreadsheet
Product category: 
Bachelor
Portfolio: 
Bachelor of Data Science for Business - Programme Courses
Semester: 
2024 Autumn
Active status: 
Active
Level of study: 
Bachelor
Teaching language: 
English
Course type: 
One semester
Introduction

The course combines consideration of realistic decision-making problems with theory and methodologies in building and solving spreadsheet models. Examples from finance, marketing, supply chain and other business areas illustrate management science applications and solutions methods using spreadsheet tools widely used in most of the organizations.

Learning outcomes - Knowledge

During the course, students should have acquired:

  • Basic knowledge in Spreadsheet Modeling to be able to formulate a model based on a decision problem
  • Identify and apply the right method
  • Interpret and analyze the results
  • Basic knowledge of Optimization with Linear, as well as Mixed-Integer Optimization Programming Models with a goal of finding an optimal solution that mimimizes the costs, emissions, distance or maximizes the profit or other objectives.
Learning outcomes - Skills

After completing the course, students shall be able to:

  • Build models in spreadsheet and analyse the results
  • Identify and apply modelling approach to solve decision problems
  • Use spreadsheets for various business applications
  • Model and solve linear and Mixed-Integer Optimization problems and perform sensitivity analysis
  • Model decisions under uncertainty.
General Competence

The students will learn how to build a model, analyze a decision problem, evaluate the data and how to select a modelling and solution approach.

Course content
  • Introduction to Spreadsheet Modeling.
  • Introduction to Optimization and Linear Programming Models.
  • Modeling and solving LP problems and sensitivity analysis.
  • Network Models.
  • Optimization Models with Integer Variables.
  • Mixed-Integer Optimization 
  • Solution methods: Simplex and Evolutionary Solver.
  • Decision Making Under Uncertainty
  • Introduction to Simulations
  • Multiobjective Decision Making.
  • Inventory and Supply Chain Models.
Teaching and learning activities

The course will include/entail a combination of Lectures and exercises using Excel.

Students are expected to solve a number of spreadsheet exercises, which will be given after each lecture.

The students are recommended to use a PC, not Mac during the course.

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

.

Qualifications

Higher Education Entrance Qualification

Disclaimer

Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Required prerequisite knowledge

EBA 1180 Mathematics for Data Science, EBA 2904 Statistics or equivalent courses, Excel course.

Assessments
Assessments
Exam category: 
Submission
Form of assessment: 
Structured Test
Exam/hand-in semester: 
First Semester
Weight: 
30
Grouping: 
Individual
Duration: 
24 Hour(s)
Comment: 
Midterm multiple choice test with questions that require building models and solving those to be able to answer on the questions. The exam has a fixed duration 24 hours, this allows the students to start a timeboxed multiple choice exam (4 hours) whenever they wish within this period. All exams must be passed to obtain a final grade in the course.
Exam code: 
EBA 36102
Grading scale: 
ECTS
Resit: 
Examination every semester
Exam category: 
School Exam
Form of assessment: 
Written School Exam - digital
Exam/hand-in semester: 
First Semester
Weight: 
70
Grouping: 
Individual
Support materials: 
  • No support materials
Duration: 
4 Hour(s)
Comment: 
Exam includes several tasks to be solved based on a spreadsheet.
Exam code: 
EBA 36103
Grading scale: 
ECTS
Resit: 
Examination every semester
Type of Assessment: 
Ordinary examination
All exams must be passed to get a grade in this course.
Total weight: 
100
Student workload
ActivityDurationComment
Teaching
45 Hour(s)
Group work / Assignments
96 Hour(s)
Student's own work with learning resources
55 Hour(s)
Examination
4 Hour(s)
Sum workload: 
200

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.