Course

Introduction to Quantitative Decision Making

Faculty
Commerce & Business Administration
Department
Business
Course code
BUSN 2290
Credits
3.00
Semester length
15 Weeks X 4 Hours Per Week = 60 Hours
Max class size
35
Method(s) of instruction
Lecture
Seminar
Typically offered
To be determined

Overview

Course description
The course will provide an introduction to modeling, analyzing and solving business decision problems under certainty and uncertainty. By developing good modeling skills, students will be able to solve and develop managerial insight, in a variety of common and not so common problems in today’s business environment. The course also develops concepts of uncertainty, probability and simulation which are the foundation of many business problems. Microsoft Excel will be used to model and solve many of these problems.
Course content
  1. Introduction to modelling - steps of modelling process
  2. Introduction to decision making under certainty
  3. Optimization, sensitivity analysis
  4. Introduction to linear models and linear programming, product mix model, advertising model, production process model, work schedule model
  5. Multi-period production model, transportation model, assignment model
  6. Introduction to probability, Bayes’ Theorem
  7. Probability distribution and decision analysis, payoff table and decision tree
  8. Planning a decision strategy.
Learning activities

Lectures and seminars.

Means of assessment
Final Exam          30%
Term Examinations (2-3) 40% - 50%
Computer Lab Test  5% - 10%
Assignments 10% - 20%
          100%

To pass the course it is necessary to achieve at least 50% on the final exam and achieve at least 50% on the combined average of all tests and examinations.

Learning outcomes

At the end of the course, the successful student should be able to:

  1. Understand solving problems and decision making using quantitative analysis.
  2. Understand the process of modelling (Cost, Revenue and Profit Models).
  3. Understand decision making under certainty (Optimization, Sensitivity Analysis).
  4. Understand computer-based systems to help with quantitative analysis and design models for business use.
  5. Understand developing Excel-based models for problem solving.
  6. Understand linear models and linear programming (product mix model, advertising model, production process model, work schedule model) using optimization functions such as Excel Solver,