BIG DATA ANALYTICS AND FORECASTING IN LOGISTICS & SUPPLY CHAIN MANAGEMENT
| Title | ΑΝΑΛΥΤΙΚΗ ΜΕΓΑΛΩΝ ΔΕΔΟΜΕΝΩΝ ΚΑΙ ΠΡΟΒΛΕΨΕΙΣ ΣΤΗΝ ΕΦΟΔΙΑΣΤΙΚΗ & ΔΙΟΙΚΗΣΗ ΑΛΥΣΙΔΑΣ ΕΦΟΔΙΑΣΜΟΥ / BIG DATA ANALYTICS AND FORECASTING IN LOGISTICS & SUPPLY CHAIN MANAGEMENT |
| Code | 10602-LSC-111 |
| Faculty | Social and Economic Sciences |
| School | Economics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Christos Zikopoulos |
| Common | No |
| Status | Active |
| Course ID | 600020688 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024-2025)
Registered students: 0
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024- SĪMERA) MF
Registered students: 2
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
Programme of Study: METAPTYCΗIAKO STĪN EFODIASTIKĪ KAI DIOIKĪSĪ ALYSIDAS EFODIASMOU (2024-sīmera)
Registered students: 16
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Compulsory Course | 1 | 1 | 6 |
| Academic Year | 2025 – 2026 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600287197
|
Course Type 2021
Specialization / Direction
Mode of Delivery
- Face to face
- Distance learning
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600287197
- eLearning: https://elearning.auth.gr/course/view.php?id=15807
Learning Outcomes
The purpose of the course is twofold. The first is to introduce students to the notion of Big Data and in the ways that it can help them make better L & SCM decisions. The second purpose of the course is to familiarize students with the basic time series analysis methods that are useful in forecasting customer demand, one of the most important initial stages in almost any L & SMC decision process, in strategic, tactical or operational level.
By completing the course, the students will also have knowledge of how they can make practical use of the capabilities of modern L & SCM Big Data Analytics Tools and to use the models that are suitable for forecasting time series with different characteristics.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Make decisions
- Work autonomously
- Advance free, creative and causative thinking
Course Content (Syllabus)
The course presents what is L&SCM Big Data, and highlights Applications of Big Data Analytics in L & SCM areas such as supply chain network design, procurement management, inventory management etc. The creation and processing of Big Data are examined, together with their analysis through statistical and visualization tools.
In addition, the course presents and examines mathematical model useful for the analysis of time series that differentiate according to the presence or absence of trend and seasonality, as well as with periods with zero demand. The analysis of time series is conducted in order to forecast future customer demand.
During the module there will be application popular Big Data Analytics Tools such as Tableau and Qlik Sense. Also, the students will have the opportunity to familiarize with time series analysis and forecasting using spreadsheets.
Keywords
Big Data, Forecasting, Analytics
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Laboratory Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Laboratory Work | 55 | ✓ | ✓ | ||
| Reading Assigment | 50 | ✓ | |||
| Written assigments | 33 | ✓ | |||
| Exams | 3 | ||||
| Total | 180 |
Student Assessment
Description
Written Exams
Exams in Computer Lab
Written assignments
Student Assessment methods
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Written Exam with Problem Solving (Summative)
Bibliography
Additional bibliography for study
Big Data Analytics in L&SCM
• Lecture notes.
• Peter W. Robertson, Supply Chain Analytics: Using Data to Optimise Supply Chain Processes, Routledge, 2020.
• Iman Rahimi et al. Big Data Analytics in Supply Chain Management: Theory and Applications, CRC Press 2020.
• Bernard Marr, Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results, Wiley 2016.
• Glenn J. Myatt, Wayne P. Johnson, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining 2nd Edition, Wiley 2014.
• James R. Evans, Business Analytics: Methods, Models, and Decisions, 3rd edition, Pearson, 2019.
• Alexander Loth, Visual Analytics with Tableau, Wiley 2019.
• Joshua N. Milligan, Learning Tableau 2020: Create effective data visualizations, build interactive visual analytics, and transform your organization, 4th Edition, Packt Publishing 2020.
• Pablo Labbe, et al. Hands-On Business Intelligence with Qlik Sense: Implement self-service data analytics with insights and guidance from Qlik Sense experts, Packt Publishing, 2019.
Forecasting in L&SCM:
• Lecture notes.
• Nahmias, S. and Olsen, T.L. Production and Operations Analysis, 7th edition, McGraw-Hill, 2015.
• Silver, E.A., Pyke, D.F. and Thomas, D.J. Inventory and Production Management in Supply Chains, 4th edition, CRC Press, 2017.
Last Update
08-10-2025
