Quantitative Analysis in Management

Course unit code

Specialization 080500.68 – Management

Course unit title

Quantitative Analysis in Management

Name(s), surname(s) and title of lecturer(s)

Peter F. Tarassenko,
Cand. Sci., Assoc. Professor

Level of course

Master

Semester

3

ECTS credits

3

Working hours

Contact hours

48

lectures

12

seminars

practical classes

laboratory classes

36

consultations

Self-study

60

Total

108

Work placement

Analysis of the real life management problems

Prerequisites

Introductory courses of Math and/or Linear Geometry, Probability.
Basic knowledge of Operations Management, Finance.

It is also required a good background with Excel Spreadsheet.

Language of instruction

English

Objectives of the course

Learning outcomes

A student’s assessments methods

Develop theoretical knowledge, modeling know-how, and computer skills to be able to select optimal solutions to real life management problems.

Student should be able to:

• Understand and explain why model-based decision support systems are needed and can be utilized in managerial decision processes.

• Explain how and why modeling is used in the support system environment.

• Select appropriate model. Identify and differentiate different model components.

Solved common tasks.

Solved individual tasks.

Completed short tests.

Final written exam.

Teaching methods

Lectures, self-study, computer lab assignments, home assignments.

List of Topics

Topic title

Contact hours

Assignments and independent study hours

Introduction. Effective use of spreadsheets

2

4

Linear programming

14

16

Integer linear programming and sensitivity analysis

8

10

Multi-criteria decisions

8

10

Decision trees

8

10

Network models

8

10

Total

48

60

Assessment requirements

• Knowledge of definitions for the main concepts and terms.
• Ability to explain main problem statements.
• Correct identification of the model components.
• Explanation of the relations between model components.
• Ability to transform the problem statement to the computer-based model.
• Ability to find new solutions after modification of the initial data or the model.

Assessment criteria

Student’s skills will be evaluated by:

• Short testing scores.
• Analysis, discussion, and modification of solved problems.
• Final exam score.

The composition of final accumulative mark

20% Class participation and activity.

40% Solved problems: common and individual, class and home work.

40% Final exam: 3 questions on theory – definitions; 1 problem to solve at paper; 1 problem to solve using Excel.

Author of the course

Peter F. Tarassenko