Industrial Engineering
Course Details
KTO KARATAY UNIVERSITY
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
Mühendislik ve Doğa Bilimleri Fakültesi
Programme of Industrial Engineering
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 15271752 | Digital Transformation | 2025 | Autumn | 7 | 3+0+0 | 3 | 5 |
| Course Type | Elective |
| Course Cycle | Bachelor's (First Cycle) (TQF-HE: Level 6 / QF-EHEA: Level 1 / EQF-LLL: Level 6) |
| Course Language | Turkish |
| Methods and Techniques | Lecture, case study analysis, software-based practice and laboratory work, guest lectures by industry experts, group-based term project and project presentation. |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Prof. Murat DARÇIN |
| Instructor(s) | Asst. Prof. Şule ERYÜRÜK |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Asst. Prof. Şule ERYÜRÜK | A-Z34 | [email protected] | 7537 |
Course Content
The conceptual framework of digital transformation and the distinction between digitization, digitalization and digital transformation; historical development from Industry 1.0 to 5.0 and the design principles of Industry 4.0; digital maturity models and transformation strategy; industrial data acquisition, sensor technologies and the Industrial Internet of Things; cloud and edge computing, cybersecurity in manufacturing; big data analytics and visualization of production data; artificial intelligence applications in demand forecasting, quality prediction and predictive maintenance; digital twins and production system simulation; automation, industrial robots and collaborative robots; additive manufacturing and augmented/virtual reality applications; digital supply chains, traceability and blockchain; integration of lean manufacturing with digital technologies (Lean 4.0), digital value stream mapping and real-time OEE monitoring; energy monitoring, carbon footprint tracking and green transformation; preparation of a digital transformation roadmap, investment feasibility and change management.
Objectives of the Course
The aim of this course is to enable students to understand digital transformation not as a technology investment but as a holistic change process that redesigns an enterprise's processes, business model and decision-making mechanisms. The course covers the Industry 4.0 and 5.0 frameworks, industrial data acquisition and the Industrial Internet of Things, data analytics and artificial intelligence applications, digital twins, automation, digital supply chains, and the lean manufacturing and sustainability dimensions of transformation. Students are expected to be able to assess the digital maturity level of a manufacturing enterprise, identify improvement opportunities, and develop a feasible digital transformation roadmap that jointly considers technical, economic and human factors.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P1 | Knowledge of mathematics, natural sciences, fundamental engineering, computational sciences, and industrial engineering-specific subjects; the ability to apply this knowledge to solve complex industrial engineering problems. | 4 |
| P2 | The ability to define, formulate, and analyze complex industrial engineering problems using fundamental science, mathematics, and engineering knowledge, while keeping in mind the relevant UN Sustainable Development Goals. | 4 |
| P10 | Knowledge of business practices such as project management and economic feasibility analysis; awareness of entrepreneurship and innovation. | 4 |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction and theoratical framework |
| 2 | Digital Maturity and strategy |
| 3 | Industrial Data and IIOT |
| 4 | Clodu, edge computing and cyber security |
| 5 | Big Data Analytics |
| 6 | Artificial Intellginece applications |
| 7 | Digital Twin and Simulation |
| 8 | Midterm |
| 9 | Automotion and robotics |
| 10 | Additive manufacturing and AR/VR |
| 11 | Digital Supply Chain |
| 12 | Lean and digital entegration (Lean 4.0) |
| 13 | Sustainability and green transformation |
| 14 | Transformation roadmap and change management |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 20 (%) |
| Seminar | - | - |
| Quiz | - | - |
| Listening | - | - |
| Midterms | 1 | 30 (%) |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 3 | 42 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 14 | 3 | 42 |
| Midterms | 1 | 15 | 15 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 1 | 15 | 15 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 15 | 15 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 129 | ||
| Total Work Load / 30 | 4,30 | ||
| Course ECTS Credits: | 4 | ||