The course is organized by means of traditional lectures combined with practical work and seminars. The practical part of the course explores special tools in the form of management simulation games that are used in dynamic laboratories to provide virtual business environments. In-class interactive seminars are introduced to explain technology issues and analyse intermediate results.

The learning process will combine the following activities:

• Theory lectures: Aim to understanding the state of the art in logistics management; explain theoretical principles of decision synthesis and procedure, methods and tools for business modelling, decision-making and information support in logistics and supply chain management.
• Interactive laboratory sessions: Aim to understand challenges, elements and solutions for logistics and supply chain management; get experiences in using and designing digital logistics management tools; split in four laboratory blocks:

Block 1. Internal logistics management: focuses on production and purchasing functions of an assembly plant. Includes teamwork (2-3 students in a group) to plan production and purchase orders, develop capacity plans and job schedules; perform simulation runs in a virtual production environment in order to estimate results of the planning decisions per a production week; study different control strategies and planning scenarios, and participate in classroom discussions. As the results of this work, students present a teamwork project aiming at implementing a dynamic production planning tool to allow data transfer and processing.

Block 2. Collaborative planning and inventory replenishment: focuses on general mechanisms andmethods of in multi-echelon supply chain management with a particular focus on supply chain dynamics and inventory management;

Block 3. International logistics management: focuses on different functional aspects of international logistics company management (production and purchasing, distribution and transportation, marketing and investments, facility location and demand forecasting as well as finances);

Block 4. Logistics data analysis: focuses on dashboard development methods and tools to support decision making in logistics and supply chain.

• Workshop on innovative platforms, technologies and software: Independent learning and technology testing, search and study of literature and selected software, use case development and in-class presentation with further discussions.
• Autonomous work: reading, self-testing, reflecting. Retrieve and analyse information from different sources; reflect learning and problem solving processes in order to derive lessons learned.

Use of Generative Artificial Intelligence Tools – Policy Statement

This module 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 ofgenerative 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.

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.

The final mark of this course will be calculated from the assessment of following evaluation activities:

• Lab Blocks from 1 to 4. For Block 1, a teamwork project has to be developed and presented. For each other block, a report is required that follows given structural guidelines. All reports are marked according to formal and contents aspects. All reports are electronically submitted on the course page at the university e-platform.
• Workshop (scientific report and presentation). The topic can either be chosen from a given list or proposed by the student. Each student has to submit electronically his/her topic together with a 200 words abstract. Teachers decide about acceptance/rejection of the topic. In the report, students analyse functionality of the selected platform/IT tool, technologies applied in the software, its innovation aspects and perspectives, and gives a use case example. Student reports and presentations will be marked by the teachers (peer review).
• Final exam. Theoretical questions on topics addressed throughout the semester in order to present an understanding of management and decision synthesis in logistics.

The student passes course if all lab reports, a teamwork project, a scientific report and the final exam are evaluated as ‘sufficient’ (by grade 4.0 corresponding to a minimum of 50% of the maximum performance per evaluation activity) at least. The student fails if performance in at least one of the evaluation activities does not reach the 50% threshold or if a teamwork project and other reports are not submitted within the due date specified by the professor.

The weights of each evaluation activity are given in the table below.