ARTIFICIAL INTELLIGENCE ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
ISM5212	Quality Management	Spring	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	Turkish
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assoc. Prof. AHMET BEŞKESE
Course Lecturer(s):	Assoc. Prof. AHMET BEŞKESE
Recommended Optional Program Components:	N.A.
Course Objectives:	The aim of the course is to provide the fundamentals of quality management including statistical quality control. The course covers causes of variation, statistical process control, control charts, quality control tools and techniques. The managerial and organizational aspects of quality, total quality management (TQM), quality awards, quality assurance systems, the IS0 certification process, six-sigma and the DMAIC process are also covered. Applications with statistical software packages are also utilized.

Learning Outcomes

The students who have succeeded in this course;
I. Discuss quality, quality improvement and different dimensions of quality.
II. Describe the quality management philosophies of Deming, Juran, Feigenbaum and Crosby.
III. Discuss TQM, six-sigma, ISO standards and quality awards.
IV. Explain the steps of DMAIC.
V. Recognize the chance and assignable causes of variability in a process.
VI. Use the basic process improvement tools of statistical process control.
VII. Evaluate confidence intervals for one sample and for comparing two samples.
VIII. Construct different types of control charts for variables.
IX. Analyze process capability using control charts.
X. Construct different types of control charts for attributes.

Course Content

The course covers acceptance sampling, types of sampling plans, causes of variation, statistical process control, control charts, quality control tools and techniques. The managerial and organizational aspects of quality, total quality management (TQM), quality awards, quality assurance systems, the IS0 certification process, six-sigma and the DMAIC process are also covered.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Quality: basic definitions and historical development of quality and quality improvement
2)	Relation between quality and productivity, quality costs, quality management philosophies
3)	Management Aspects of Quality: TQM, ISO, Six-sigma
4)	Management Aspects of Quality: DFSS, Lean, DMAIC process
5)	Tools and Techniques for Quality Control and Improvement
6)	Statistical Inference about Product and Process Quality
7)	Statistical Inference about Product and Process Quality
8)	Midterm
9)	Control Charts for Variables: Xbar-R, Xbar-S, I-MR control charts
10)	Control Charts for Variables: CUSUM, EWMA control charts
11)	Process Capability Analysis using Control Charts
12)	Control Charts for Attributes: p, np control charts
13)	Control Charts for Attributes: c, u control charts
14)	Project presentations

Sources

Course Notes / Textbooks:	Douglas C. Montgomery, Cheryl L. Jennings, Michele E. Pfund, 2011. Managing, Controlling, and Improving Quality, John Wiley & Sons, 1st Edition
References:	Douglas C. Montgomery, 2009. Statistical Quality Control: A Modern Introduction, John Wiley & Sons, 6th Edition

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Homework Assignments	4	% 10
Project	1	% 20
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 40
PERCENTAGE OF FINAL WORK		% 60
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	2	28
Presentations / Seminar	1	10	10
Project	1	40	40
Homework Assignments	4	10	40
Midterms	1	15	15
Final	1	20	20
Total Workload			195

Contribution of Learning Outcomes to Programme Outcomes

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

	Program Outcomes	Level of Contribution
1)	Have sufficient background in mathematics, science and artificial intelligence engineering.
2)	Use theoretical and applied knowledge in the fields of mathematics, science and artificial intelligence engineering together for engineering solutions.
3)	Identify, define, formulate and solve engineering problems, select and apply appropriate analytical methods and modeling techniques for this purpose.
4)	Analyse a system, system component or process and design it under realistic constraints to meet desired requirements; apply modern design methods in this direction.
5)	Select and use modern techniques and tools necessary for engineering applications.
6)	Design and conduct experiments, collect data, and analyse and interpret results.
7)	Work effectively both as an individual and as a multi-disciplinary team member.
8)	Access information via conducting literature research, using databases and other resources
9)	Follow the developments in science and technology and constantly update themself with an awareness of the necessity of lifelong learning.
10)	Use information and communication technologies together with computer software with at least the European Computer License Advanced Level required by their field.
11)	Communicate effectively, both verbal and written; know a foreign language at least at the European Language Portfolio B1 General Level.
12)	Have an awareness of the universal and social impacts of engineering solutions and applications; know about entrepreneurship and innovation; and have an awareness of the problems of the age.
13)	Have a sense of professional and ethical responsibility.
14)	Have an awareness of project management, workplace practices, employee health, environment and work safety; know the legal consequences of engineering practices.