Decision Analysis in Excel (IND520)

Content

The analysis in this course will be based on different examples of business- and microeconomic decision problems, with focus on developing spreadsheet models that are logically correct and easy to read. The choice of model will be based on the settings of the decision problem at hand. The material starts with simple linear problems and builds up to more complex decision scenarios under uncertainty. The main topics are optimization models, simulation models, decision trees and data analysis.

The course primarily uses Excel, a powerful and commonly used tool in a variety of industries for performing quantitative analysis. Students can work on their own computers with the Excel software (and add-ins) installed. The main Excel add-ins that will be used are Analytic Solver and @RISK (part of DecisionTools Suite). Note that the Analytic Solver add-in does not work optimally on Mac computers, while @RISK is not available for Mac computers. Students who do not have access to an own PC may use one of the UiS (stationary) computers. Some course material will be covered in pre-recorded lectures as foundation for in-person lectures. Lecture recordings will be made available to students after each class, unless otherwise specified.

The course includes presentations by industry practitioners on how they use Excel in decision making at their institutions. Students will have the opportunity to ask questions and interact with the industry representatives.

Learning outcome

Knowledge

After completing the course, the students will know:

• linear, integer and non-linear mathematical programming relevant to business decision problems

• conditions under which different models can be used and their limitations

• solution algorithms

• analysis of decisions under uncertainty

• simulation analysis

Skills

After completing the course, the student will be able to:

• set up a spreadsheet model of a business decision problem in Excel

• determine when to use LP models, integer models or non-linear models

• perform sensitivity analysis for LP problems

• read and interpret sensitivity reports for LP problems, including shadow prices and slack values
• solve for the problem's optimal solution in Excel

• analyze decision trees for decision problems under uncertainty

• compute the value of different degrees of information based on expected value as decision criteria

• perform basic simulation models in Excel to evaluate the consequences of decisions under uncertainty

• compute and interpret confidence intervals for estimates from simulation models

• provide economic interpretations of the model solutions and what they imply for the costs and constraints facing businesses in decision problems

General competence

After completing the course, the student will be able to communicate with industry representatives on:

• how a business decision problem can be formulated as a mathematical decision problem

• the different types of models available and the conditions under which they apply

• how the models can be practically implemented and solved

• the fundamental economic constraints and trade-offs businesses face in their decision problems

Required prerequisite knowledge

Recommended prerequisites

Basic knowledge of business economics, mathematics, micro-economics, statistics and use of (Excel) spreadsheets.

Exam

Course teacher(s)

Course coordinator:

Course teacher:

Head of Department:

Method of work

Normally one four-hour lecture/problem solving in-person per week. Lectures will be delivered in English. Students will follow/contribute to the lectures using their own computers, or using the university computers. Some course material will be covered in pre-recorded lectures as foundation for in-person lectures. Lecture recordings will be made available to students after each class, unless otherwise specified.

Open for

Master in Industrial Economics and master in Business and Administration at the UiS Business School

Course assessment

Literature