Smart Manufacturing and Logistics in Industry X.O.
About the course
Can be taken as a single course.
Who has to apply for admission?
• PhD students at UiT that wishes to take a PhD level course which is not going to be a part of the PhD degree they study for at UiT.
• PhD students from other institutions than UiT.
• Applicants that has achieved a master’s degree (five year master or three year bachelor degree + two year master degree), but isn’t a PhD student.
This course will equip students with the essential knowledge, technologies, and insights into Industry X.0, which is reshaping the future of manufacturing and logistics systems.
Admission requirements
- This course is aimed at PhD candidates from engineering disciplines, but is open to postdoc fellows if seats are available.
- PhD candidates from both UiT and NORDTEK universities may be admitted to the course. PhD candidates not registered at UiT must upload a document from their university stating that they are registered PhD candidates
If there are 5 applicants or fewer, the course responsible will evaluate whether the course will take place.university stating that they are registered PhD candidates.
Objectives of the course
Upon successful completion of the course, the following learning outcomes are expected to be achieved:
Knowledge:
- Explain and contextualize the theoretical evolution of industrial revolutions, emphasizing the human-centric and sustainable paradigms of Industry X.0.
- Analyze the principles of cyber-physical production systems and digital supply networks in manufacturing and logistics.
- Evaluate key enabling technologies, e.g., AI, IoT, digital twins, simulation, and XR, in terms of functionality and impact on industrial transformation.
- Synthesize perspectives on sustainability, resilience, and human-technology collaboration to interpret the strategic role of Industry X.0.
Skills:
- Develop and apply extended reality (XR) tools for production visualization, operator support, and training.
- Build and analyze digital twin models for manufacturing systems, integrating real-time data for performance optimization.
- Formulate and solve logistics and supply chain problems using optimization and simulation methods.
- Apply data analytics and AI tools to design and assess sustainable and resilient digital supply chain solutions.
- Integrate digital tools in industrial contexts to deliver a validated prototype (e.g., XR model, digital twin, or optimization algorithm).
General Competence:
- Propose and implement technology solutions aligned with industrial requirements
- Demonstrate adaptive problem-solving in technological transformation contexts.
- Document and present the project outcomes.
Teaching methods
The course is equivalent to a 3-week full-time study. The course will be organized into three parts:
- Part 1 is a one-week (equivalent) intensive lecture and assignments.
- Part II is a one-week (equivalent) with selected lectures, workshops, labs, and reading assignments/project.
- Part III is a one-week (equivalent) guided project.
Language of instruction and examination
EnglishSchedule
The schedules are normally finalized and published well in advance of the start of the semester, often a few weeks beforehand. This gives students the opportunity to organize their studies and prepare for upcoming activities.
It is recommended to check the schedule regularly, as changes may occur.
Examination
| Oral exam | Duration: 30 Minutes |
Grade: Passed / Not Passed |
To take an examination, the student must have passed the following coursework requirements
| Case study | Grade: Approved – not approved |
| Presentations | Grade: Approved – not approved |
| Individual project | Grade: Approved – not approved |
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