Energy Systems Engineering (English)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

General course introduction information

Course Code:

BBA381

Course Name:

Business Analytics and Decision Making

Course Semester:

Fall

Course Credits:

Theoretical	Practical	Credit	ECTS
3	0	3	6

Language of instruction:

Course Requisites:

Does the Course Require Work Experience?:

Type of course:

Compulsory

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi İREM YALKI

Course Lecturer(s):

Dr.Öğr.Üyesi İREM YALKI

Course Assistants:

Course Objective and Content

Course Objectives:	The purpose of the course is to explain the fundamental aims of quantitative analysis. In this context, decision analysis, linear programming, the transportation problem, the assignment problem and the transshipment problem will be examined.
Course Content:	The purpose of the course is to explain the fundamental aims of quantitative analysis. In this context, decision analysis, linear programming, the transportation problem, the assignment problem and the transshipment problem will be examined.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) • Define and identify the variables in a statistical event.
2 - Skills
Cognitive - Practical
1) • Identify how to gather, arrange and present the data belonging to a statistical event.
2) • Choose the relevant methodology for data analysis.
3) • Provide the skills necessary for optimal decision-making processes.
4) • Analyze the decision making progress of the companies and their relation to ethics.
3 - Competences
Communication and Social Competence
Learning Competence
Field Specific Competence
Competence to Work Independently and Take Responsibility
1) • Apply the research methods in various departments in the companies.

Lesson Plan

Week	Subject	Related Preparation
1)	• Describe the course. • Compare the difference between quantitative and qualitative analysis. • Define the problem. • Analyze how to develop a model. • • Explain what quantitative analysis is. • Explain what qualitative analysis is. • Explain the quantitative analysis approach. • List the three categories of business analytics.
2)	• Explain the advantages of mathematical modeling. • Explain the mathematical models categorized by risk. • Define the problem. • Develop a model. • Acquire input data. • Develop a solution. • Test the solution. • Analyze the results. • Identify the research problem. • Analyze how to develop a quantitative analysis model. • List the important issues in determining research question. • List the possible problems in the quantitative analysis approach. • Explain the quantitative analysis approach in the business life their relation to ethics.
3)	• Identify the decision theory. • Identify the optimistic decision making under uncertainty. • Identify the pessimistic decision making under uncertainty. • Identify the criterion of realism (Hurwicz Criterion) decision making under uncertainty. • Identify the equally likely (Laplace) decision making under uncertainty. • Identify the minimax regret decision making under uncertainty. • List the types of decision making under risk. • List the six steps in decision making. • Explain the types of decision making environments. • Identify the decision making under uncertainty. • List the types of decision making under uncertainty. • Identify the decision making under risk.
4)	• Identify and explain the sensitivity analysis. • Demonstrate how probability values are estimated by Bayesian analysis. • Calculate the revised probabilities. • Discuss the potential problems in using survey results. • Illustrate how to measure utility and construct a utility curve. • Identify the decision trees. • Identify the utility theory. • Explain the utility as a decision making criterion.
5)	• Illustrate how to use graphical representation of constraints. • Analyze the isoprofit line solution method. • Analyze the corner point solution method. • Explain what slack and surplus is. • Analyze linear programming models. • List and explain the requirements of a linear programming. • Formulate linear program problems. • Illustrate how to use graphical solution to a linear program problem.
6)	• Analyze ‘no feasible solution’ case in linear program. • Analyze ‘unboundedness’ case in linear program. • Analyze ‘redundancy’ case in linear program. • Analyze ‘alternate optimal solution’ case in linear program. • Solve minimization problems. • Analyze four special cases in linear program. • Identify and explain the sensitivity analysis.
7)	• Identify and explain the marketing applications. • Identify and explain the manufacturing applications. • Identify and explain the employee scheduling applications. • Identify and explain the financial applications. • Identify and explain the ingredient blending applications. • Identify and explain the transportation applications. • Classify the linear programming applications. • Formulate linear program problems.
8)	• Evaluate students via midterm exam • Explain the advantages of mathematical modeling. • Explain the mathematical models categorized by risk. • Identify the criterion of realism (Hurwicz Criterion) decision making under uncertainty. • Identify the equally likely (Laplace) decision making under uncertainty. • Demonstrate how probability values are estimated by Bayesian analysis. • Illustrate how to use graphical representation of constraints. • Analyze the corner point solution method. • Identify and explain the marketing applications. • Midterm exam • List the important issues in determining research question. • List the possible problems in the quantitative analysis approach. • List the types of decision making under uncertainty. • Identify the decision trees. • Formulate linear program problems. • Illustrate how to use graphical solution to a linear program problem. • Formulate linear program problems.
9)	• Analyze the transportation algorithm. • Analyze the unbalanced transportation problems. • Analyze the degeneracy in transportation problems. • Analyze the more than one optimal solution. • Analyze the maximization transportation problems. • Analyze the unacceptable or prohibited routes. • Analyze the unbalanced assignment problems. • Analyze the maximization assignment problems. • Analyze the transportation problem. • Analyze the assignment problem. • Analyze the transshipment problem. • List and analyze the special situations with the transportation algorithm. • Identify the facility location analysis. • List and analyze the special situations with the assignment algorithm.
10)	• Identify and explain the maximal-flow technique. • Identify and explain the shortest- route technique. • Formulate and solve the linear program for maximal flow problem. • Formulate and solve the linear program for shortest-route problem. • Analyze network models. • Analyze the maximal-flow problem. • Analyze the shortest-route problem. • Analyze the minimal-spanning tree problem.
11)	• Solve the model with binary variables. • Identify and explain the nonlinear objective function and linear constraints. • Identify and explain the nonlinear objective function and nonlinear constraints. • Identify and explain the linear objective function with nonlinear contraints. • Analyze integer programming. • Analyze goal programming. • Analyze nonlinear programming. • Discuss the difference between goal programming and linear programming.
12)	• Explain what CPM is. • Explain what PERT is. • Illustrate how to draw the CPM/PERT network. • Illustrate how to find the critical path. • Calculate the probability of project completion. • Identify and explain project management • Identify and explain the sensitivity analysis. • Compare the difference between CPM and PERT.
13)	• Analyze the planning and scheduling project costs. • Identify and explain the monitoring and controlling project costs. • List and explain the four steps of project crashing. • Explain what subprojects is. • Explain what milestones is. • Analyze the PERT/Cost. • Analyze the project crashing. • Analyze the project crashing with linear programming
14)	• Identify and explain language of games. • Identify and explain the minimax criterion. • Identify and explain pure strategy games. • Identify and explain mixed strategy games. • Identify and explain dominance strategy games. • Analyze game theory. • List five types of games in game theory. • Solve game theory problems with linear programming.
15)	• Evaluate students via final exam • Analyze the transportation algorithm. • Identify and explain the shortest- route technique. • Solve the model with binary variables. • Illustrate how to draw the CPM/PERT network. • Illustrate how to find the critical path. • Identify and explain dominance strategy games. • Final Exam • Analyze the transportation problem. • Analyze the shortest-route problem. • Analyze integer programming. • Identify and explain project management • Analyze game theory.•

Sources

Course Notes / Textbooks:

(The institution recognizes the use of the textbook in the classroom as part of the educational methodology and strategy applied in diverse materials. The textbook is part of the curriculum and is used to reach the student in an effective manner in the classroom. Every student is expected to acquire and use the textbook.)

Quantitative Analysis for Management , Pearson Education, Inc, 13th edition, 2021, Barry Render, Ralph M. Stair, Michael E. Hanna and Trevor S. Hale

References:

Quantitative Analysis for Management , Pearson Education, Inc, 13th edition, 2021, Barry Render, Ralph M. Stair, Michael E. Hanna and Trevor S. Hale

Course-Program Learning Outcome Relationship

Learning Outcomes	1	2	3	4	5	6
Program Outcomes
1) Closed Department

Course - Learning Outcome Relationship

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Closed Department

Learning Activity and Teaching Methods

Field Study
Expression
Brainstorming/ Six tihnking hats
Individual study and homework
Lesson
Group study and homework
Homework
Problem Solving
Report Writing

Assessment & Grading Methods and Criteria

Written Exam (Open-ended questions, multiple choice, true-false, matching, fill in the blanks, sequencing)
Oral Examination
Homework
Individual Project
Group project
Presentation
Reporting

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 20
Homework Assignments	1	% 10
Midterms	1	% 30
Final	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

Workload and ECTS Credit Grading

Activities	Number of Activities	Workload
Course Hours	15	45
Study Hours Out of Class	13	61
Homework Assignments	1	4
Quizzes	2	4
Midterms	1	10
Final	1	14
Total Workload		138