Basics of Logistics and Supply Chain Management
Code: 44756 ECTS Credits: 6| Degree | Type | Year |
|---|---|---|
| Logistics and Supply Chain Management | OB | 1 |
Contact
- Name:
- Laura Calvet Liņan
- Email:
- laura.calvet.linan@uab.cat
Teachers
- Ane Elixabete Ripoll Zarraga
- (External) Aleksandar Jovanovic
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
Being one of the initial courses in this master, no special prerequisites are needed (i.e., any student that has been accepted in this master is assumed to have the necessary technical and quantitative background to follow the course without many difficulties).
Objectives and Contextualisation
1. Understand the fundamental concepts of supply chain management
2. Apply network design and aggregate planning techniques to optimize the supply chain
3. Analyze and select appropriate transportation and storage systems, including smart technologies and automation
4. Evaluate the impact of economies of scale, inventory management, and sustainability on logistics decision-making
5. Explore the potential of artificial intelligence and machine learning in improving logistics processes
Learning Outcomes
- CA01 (Competence) Compile basic analytical methodologies in order to analyse supply chains.
- KA01 (Knowledge) Identify basic LSCM terminology.
- KA02 (Knowledge) Understand LSCM as a specific field and recognise its basic strategies.
- KA03 (Knowledge) Understand the general LSCM framework.
- SA01 (Skill) Distinguish between specific problems in the field of LSCM.
- SA02 (Skill) Analyse and discuss cases, problems and challenges related to requirements and logistical options.
- SA03 (Skill) Assess the impact of logistics and SCM activities.
- SA04 (Skill) Analyse strengths and weaknesses by comparing them to best practices in LSCM.
Content
1. Introduction to LSCM:
- LSCM concepts
- SC Performance
- SC drivers + distribution network
2. Sustainable LSCM
3. Network design & Aggregate planning in the SC
4. Transportation and ITS
5. Warehousing:
- Material handling
- Robotics & unmanned vehicles
6. Economies of scale and inventories
7. Role of ML/AI in LSCM
Note: This course represents a first introduction to LSCM. In order to give a global picture of most LSCM topics, a lot of concepts are introduced in the course. Some of these concepts will appear again in other courses of the Master, where they will be analyzed in more detail.
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Evaluation Activities | 5 | 0.2 | |
| Problem Sessions | 5 | 0.2 | |
| Theory Lectures | 20 | 0.8 | |
| Type: Supervised | |||
| Practise Sessions | 15 | 0.6 | |
| Type: Autonomous | |||
| Activities | 25 | 1 | |
| Personal Study | 30 | 1.2 | |
| Report and Oral Presentation 1 | 25 | 1 | |
| Report and Oral Presentation 2 | 25 | 1 |
The course is organized through lectures.
The learning process will combine the following activities:
- Theoretical classes
- Problem sessions
- Practical sessions: teamwork and oral presentation
- Autonomous work
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Exercises to do in class | 20% | 0 | 0 | CA01, KA01, KA02, KA03, SA01, SA02, SA03, SA04 |
| Report and Oral Presentation 1 | 20% | 0 | 0 | KA01, KA02, KA03, SA01 |
| Report and Oral Presentation 2 | 20% | 0 | 0 | SA01, SA02, SA03, SA04 |
| Test 1 | 20% | 0 | 0 | CA01, KA01, KA02, KA03 |
| Test 2 | 20% | 0 | 0 | SA01, SA02, SA03, SA04 |
The final grade will be calculated based on the evaluation of different activities:
- 2 Exams
- 2 Reports and an oral presentation in groups of 3 or 4 students
- Exercises to be done in class
All activities must be submitted within the deadlines indicated by the teacher.
To pass the subject it is necessary to meet the 3 requirements described below.
- The overall grade must be equal to or higher than 5
- The mark for each report must be no less than 4
- The mark of each exam must be no less than 2.5
Use of Generative Artificial Intelligence Tools
This subject acknowledges the increasing role of generative artificial intelligence (AI) as a support tool in academic work. Accordingly, the use of such tools is permitted on a limited basis, strictly for enhancing the formal aspects of student submissions. Acceptable uses include improving writing quality, style, clarity of exposition, linguistic accuracy, and translation, as well as obtaining occasional technical assistance.
However, the use of generative AI to create the substantive content of assessed work is strictly prohibited. This includes, but is not limited to: the development of methodological approaches, the design or execution of experiments, the analysis or interpretation of results, the formulation of ideas, and the drafting of conclusions. These tasks must be carried out entirely by the student, as they constitute the essential intellectual and creative contributions required to successfully complete the subject.
Students are required to explicitly declare the use of any generative AI tools in each submitted piece of work. This declaration must include:
- The specific tools used
- The purpose for which they were used
- The extent of their contribution
Excessive, irresponsible, or unnecessary use of such tools may negatively affect the final grade. Any undeclared or inappropriate use of generative AI may result in failure of the subject
Bibliography
During the course, the instructor will provide students with a complete set of slides in PDF format. These slides also contain references to scientific articles that will be accessible either in open access format or via the University library.
Some additional books that students can optionally check to extend their knowledge on the topics of this course are listed below:
- Bowersox, D.; Closs, D.; Cooper, M.; Bowersox, J. (2019): Supply Chain Logistics Management. McGraw Hill. Chopra, S.; Meindl, P. (2018): Supply Chain Management. Prentice Hall.
- Jonsson, P. (2008): Logistics and Supply Chain Management. McGraw Hill.
- Waters, D. (2009): Supply Chain Management: An Introduction to Logistics. Palgrave Macmillan.
- Wiston, W.; Albright, S. (2008): Spreadsheet Modeling and Risk Analysis. Cengage Learning.
Software
This course makes use of the following software: MS Excel, Open Solver for Excel, R, and Python.
Groups and Languages
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (PAULm) Classroom practices (master) | 1 | English | first semester | morning-mixed |
| (PLABm) Practical laboratories (master) | 1 | English | first semester | morning-mixed |
| (TEm) Theory (master) | 1 | English | first semester | morning-mixed |