SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code Course Name Year Semester Theoretical Practical Credit ECTS
LOJ5104 E-Commerce Logistics 2 Fall 3 0 3 6
Course Type : University Elective
Cycle: Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction: Turkish
Prerequisities and Co-requisities: N/A
Mode of Delivery: Face to face
Name of Coordinator: Dr. Öğr. Üyesi BAŞAK KURU
Dersin Öğretim Eleman(lar)ı: Instructor BÜŞRA ÖZDEMİR
Dr. Öğr. Üyesi BAŞAK KURU
Dersin Kategorisi: Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives: This course aims to equip students with an understanding of e-commerce and e-logistics, as well as fundamental retail logistics processes such as order management, warehouse management, distribution processes, and reverse logistics. It also aims to raise awareness of logistics technologies, sustainability, and international trade.
Course Content: The course begins with an introduction to e-commerce and examines the role of logistics processes in e-commerce. It covers warehousing, distribution, reverse logistics, and technological applications, as well as international logistics and sustainability. Students will also gain practical skills by analyzing the logistics strategies of major e-commerce companies.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:
Knowledge (Described as Theoritical and/or Factual Knowledge.)
  1) Can define the basic concepts, structures and operations of e-commerce and e-logistics.
  2) Explain the basic principles of order management, inventory control and warehouse processes.
  3) Evaluate the conceptual framework of reverse logistics, returns management and sustainable logistics practices.
  4) Explain the basic criteria of international transportation, customs processes and logistics costs and performance indicators.
Skills (Describe as Cognitive and/or Practical Skills.)
  1) Can use logistics software and ERP systems at a basic level and manage simple processes with these tools.
  2) Can make basic logistics decisions by analyzing order, inventory and distribution data.
Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)
  1) In collaboration with stakeholders, it can plan and manage e-commerce logistics processes on the basis of sustainability and compliance with international legislation.

Weekly Course Schedule

Week Subject
Materials Sharing *
Related Preparation Further Study
1) They will focus on the critical role and importance of logistics in e-commerce processes, with discussions on its links to supply chain management. Tura, D. (2020). E-Ticaret ve Dijital Pazarlama. İstanbul: Beta Yayınları.
2) They will focus on the critical role and importance of logistics in e-commerce processes, with discussions on its links to supply chain management Course Notes
3) Students will explore order management processes and inventory control methods, discussing how effective stock management contributes to success in e-commerce. Karaca, E. (2019). Lojistik Yönetimi. Ankara: Detay Yayıncılık.
4) Storage systems, warehouse layout and operational processes in e-commerce will be discussed. Accordingly, a technical visit will be organized. Yıldız, M. (2018). Depo Yönetimi ve Lojistik Süreçler. Ankara: Seçkin Yayıncılık
5) They will learn about cargo, distribution and delivery processes and their importance in terms of customer satisfaction. Koç, E. (2025). Tüketici davranışı ve pazarlama stratejileri: Global ve yerel yaklaşım (10. baskı). Seçkin Yayıncılık. ISBN: 9789750298493
6) Return and reverse logistics processes in e-commerce will be discussed in terms of cost and customer experience. Temur, G. T., & Ayvaz, B. (2015). Tersine lojistik yönetimi: Dünyada ve Türkiye’de durum (1. baskı). Nobel Akademik Yayıncılık. ISBN: 9786053200864
7) Students will learn about the technologies used in logistics, automation systems, and digital solutions Adıgüzel, S. (2022). Lojistik 4.0 (2. baskı). Nobel Bilimsel Eserler. ISBN: 9786050331776
8) Midterm
9) International e-commerce processes, customs procedures and legal regulations will be discussed. Course Notes
10) Sustainable logistics practices and green logistics concepts will be discussed. Kaya, B. (2020). Yeşil Lojistik ve Sürdürülebilirlik. Ankara: Seçkin Yayıncılık.
11) The logistics systems of global e-commerce giants such as Amazon and Alibaba will be examined. Kazankaya, K. (2019). E-ticaret ve dijital pazarlama (2. basım). Sokak Kitapları Yayınları. ISBN: 9786052875414
12) Management of logistics costs, performance measurement methods and efficiency analyses will be emphasized. Course Notes
13) They will learn about logistics software and the integration of ERP systems and discuss the advantages it provides to businesses. Course Notes
14) This week, students will reinforce their theoretical knowledge through simulations and practical exercises, discussing real-life scenarios. Course Notes
*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References: Tura, D. (2020). E-Ticaret ve Dijital Pazarlama. İstanbul: Beta Yayınları.
Karaca, E. (2019). Lojistik Yönetimi. Ankara: Detay Yayıncılık.
Christopher, M. (2016). Logistics & Supply Chain Management. Pearson.
Fernie, J., Sparks, L. (2019). Logistics and Retail Management: Emerging Issues and New Challenges in the Retail Supply Chain. Kogan Page.

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)

1

2

3

4

5

6

7

Program Öğrenme Çıktıları (PÖÇ)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
           
Programme Learning Outcomes Contribution Level (from 1 to 5)
1) It explains fundamental concepts in mathematics, statistics, and probability; and applies this knowledge to data analysis, modeling, and interpretation of results.
2) It explains the principles of algorithm design and develops software for solving problems using at least one programming language.
3) It compares machine learning and data mining algorithms, selects the appropriate method, and applies it to real data.
4) Big data platforms utilize distributed systems and cloud computing architectures to perform data processing operations.
5) They apply natural language processing techniques to text data and develop basic NLP-based applications.
6) It analyzes different data sources, transforms them into meaningful outputs, and presents them using appropriate visualization tools.
7) It creates data-driven decision models using decision support systems.
8) It develops optimization models and produces solutions for industrial and sectoral problems.
9) In professional practice, we operate within the framework of ethical principles, data security, and social responsibility.
10) They keep up with current technological developments in their field, actively participate in teamwork, and develop a lifelong learning awareness.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Teaching and Learning Methods defined at the Programme Level
Teaching and Learning Methods Defined for the Course
Lectures
Discussion
Case Study
Reading
Individual and Group Work

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)
Aassessment and evaluation Methods defined at the Programme Level
Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Quiz

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods # of practice per semester Level of Contribution
Quizzes 1 % 15.00
Midterms 1 % 35.00
Semester Final Exam 1 % 50.00
Total % 100
PERCENTAGE OF SEMESTER WORK % 50
PERCENTAGE OF FINAL WORK % 50
Total % 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities # of Activities per semester Duration (hour) Total Workload
Course 14 3 42
Laboratory 0 0 0
Application 0 0 0
Special Course Internship (Work Placement) 0 0 0
Field Work 0 0 0
Study Hours Out of Class 0 0 0
Presentations / Seminar 14 1 14
Project 0 0 0
Homework Assignments 15 1 15
Total Workload of Teaching & Learning Activities - - 71
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities # of Activities per semester Duration (hour) Total Workload
Quizzes 0 0 0
Midterms 1 36 36
Semester Final Exam 1 46 46
Total Workload of Assesment & Evaluation Activities - - 82
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) 153
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) 6