CU1: Management system specification in production and logistics (5 ECTS)

The course is organized by means of traditional lectures combined with seminars and practical work. The learning process will combine the following activities:

• Classroom sessions: include theory lectures. Aim to understand specific requirements of logistics and production management and their complexity; understand specification needs and market situation of typical categories of management systems in production and logistics; specify and formalize requirements for a logistics/production management and control system; explain procedure, methods, and tools for specifying, selecting, implementing, testing and analysing management systems in production and logistics.
• Workshop: discussion on how Artificial Intelligence tools can support market search and analysis; practical training on the application of creativity techniques in teams
• Lab sessions: include demonstrations, experiments in physical environment, classroom discussions. Aim to understand challenges, elements and solutions for managing and controlling production and logistics.
• Case study: group work, student presentation. Aims to (i) apply procedures, methods, and tools for specifying requirements for management systems in production and logistics; (ii) identify and apply criteria for selecting management systems; (iii) elaborate solid arguments to convince and motivate decision makers; (iv) run and manage a management system specification project in order to prepare for respective purchasing activities; (v) create a vision on management systems of the future.
• 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.

CU2: XR technologies in logistics and supply chain management (2.5 ETCS)

The course is organized by means of traditional lectures combined with practical work. The learning process will combine the following activities:

• Classroom sessions: include theory lectures. Aim to understand kinds of XR technologies, their basic functionality, applicability as entities in cyber-physical systems and connected to logistics management and control systems
• Lab sessions: include demonstrations, experiments in physical environment, classroom discussions. Aim to understand chances and challenges related to applying XR technologies in logistics management and control
• 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.

CU3. AI application in logistics and supply chain management (2.5 ECTS)

The course is organized by means of traditional lectures combined with practical work. The learning process will combine the following activities:

• Classroom sessions: include theory lectures and demonstrations. Aim to differentiate between supervised and unsupervised machine learning; classify logistics management problems as regression, classification and clustering tasks; analyze applications that can (or cannot) supported by AI; explain the principles, applications areas, and limitations of machine learning algorithms in logistics management systems
• Lab sessions: include guided programming exercises, experiments with logistics/ supply chain management datasets, demonstrations using Jupyter Notebook or Google Colab, project work, and discussion of results. Aim to prepare and analyse logistics data; assess the effects of prediction errors; develop, document, and present a small AI-based prototype for a selected logistics management problem
• 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

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.

CU1: Management system specification in production and logistics (5 ECTS)

• Case study – part A. Student teams run a market analysis on Manufacturing Execution Systems (MES), Shop floor Management Systems (SFMS), Management Information Systems (MIS), or Customer Information Systems (CIS) using AI-based tools like ChatGPT in order to analyse market situation and derive functional specification of the given class of management systems. Results are summarized in a meaningful, illustrative PowerPoint presentation and presented towards potential decision makers, i.e. other students (peer review).
• Case study – part B. Student teams analyse chances and challenges from digitalization, Industry 4.0, Artificial Intelligence etc. on the class of management systems given in order to characterize the impact latest technological developments might have on user/functional requirements for management systems in production and logistics and their identification/specification. Here, looking into future and developing visionary scenarios concerning the given class of management systems is of particular importance. Results are summarized in a meaningful, illustrative PowerPoint presentation for potential decision makers, i.e. other students (peer review), followed by a wide discussion of the ideas.

CU3: AI application in logistics and supply chain management (2.5 ECTS)

• Case study. Student teams are assigned a logistics or supply chain management problem for which AI can support decision-making. The teams define the problem, develop a small AI-based prototype using Python, and evaluate its usefulness for the underlying decision. The prototype must apply at least one basic machine-learning algorithm covered in the course. The results are documented in an executable and clearly structures Jupyter Notebook and summarised in a presentation.

CU1, CU2 and CU3

The module concludes with a final (written) exam comprising questions and small cases concerning theoretical and practical knowledge on specification and evaluation of logistics management and control systems as being addressed in all three course units from different perspectives. Students are expected to show their understanding on both course-specific aspects and combined views on the overall topic of the entire module. Share of questions expressed by points to be achieved corresponds to the share of each course on module ECTS and weight of the final exam per course.

The student passes the module if case studies and final exam are evaluated "sufficient" (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 case study results are not submitted within the due date specified by the lecturer.

In case of fail the student needs to retake just that part of module exam s/he failed. The decision about this is in hands of the examiners. If case study is failed, the student (team) will either be provided with a new case study or asked to re-submit case study results according to the corrections/indications provided by the examiner.

Students who fail an exam may be permitted the opportunity to retake this examination twice at a maximum. After that his/her right for examination terminates. Retaking an exam is allowed only in case the student previously failed, but not to improve grades achieved so far.

Examination dates are announced in due time, but at least two weeks prior to the respective exam. Submission deadlines for practical assignments case studies results and any presentation activities related to them are announced when giving assignments/case studies to students. The final exam and a first opportunity for eventually retaking it are scheduled within specified examination periods. Specific examination dates are published on the university's website.

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