Fundamentals of Autonomous Operation Systems

This course offers an in-depth exploration of Lean, Six Sigma, and advanced AI-driven techniques to optimise autonomous production systems. Through practical learning, students will master process control, continuous improvement, and zero-defect manufacturing. By integrating data analysis and machine learning, participants will acquire the skills needed to drive innovation and operational excellence in modern manufacturing.

Upon completing this course, students will have acquired advanced knowledge and practical skills to optimise autonomous production systems. They will:

• Master Lean tools such as Value Stream Mapping and Zero Defect Manufacturing to streamline processes and ensure high-quality outcomes.
• Apply Six Sigma methodologies and advanced statistical techniques to reduce variation, enhance process control, and solve complex production challenges.
• Leverage AI and machine learning to automate processes, predict and prevent defects, and make data-driven decisions in real-time.
• Drive continuous improvement using data-driven approaches, applying insights to optimise production systems and achieve operational excellence.
• Develop expertise in integrating automation technologies to support real-time decision-making and adaptive production systems, enabling fully autonomous operations.

This course will empower students to be forward-thinking leaders in modern manufacturing, seamlessly integrating Lean principles, AI technologies, and automation to push the boundaries of innovation and decision-making in autonomous production.

The course is structured to ensure both theoretical understanding and practical application through a combination of lectures, group work, and interactive learning:

• Weekly lectures (2 hours per week) will be held on campus, with simultaneous streaming for remote participants, ensuring accessibility for all students.
• A series of mandatory assignments will be given as part of group work. These assignments will systematically build up the key components of the semester project, allowing students to apply the course principles in a collaborative environment.
• The majority of the assignments will involve practical experiments, which can be carried out weekly in a dedicated room reserved specifically for course participants and their group work. This hands-on approach helps students deepen their practical skills.
• The flipped-classroom method will be used for select topics, enabling students to prepare beforehand and engage in more active, discussion-based learning during class time.
• Quizzes will be used periodically to assess students' current knowledge and to ensure that the course material is being understood effectively. Adjustments will be made based on quiz outcomes to address any knowledge gaps.

This approach ensures an interactive and engaging learning experience, combining theoretical knowledge with hands-on, practical application to prepare students for real-world challenges in autonomous production systems.