In an era where technology and sustainability are paramount, the field of industrial production is undergoing a significant transformation. It’s a domain where environmental considerations and technological advancements are not just parallel paths but are increasingly converging, creating a new paradigm in industrial practices.
Green Transformation, prioritizing sustainability, is reshaping the way industries operate. It’s about more than just compliance with environmental standards; it’s a fundamental shift towards responsible production methods that consider the long-term impact on our planet. Simultaneously, the Digital Transformation, with AI at its core, is redefining efficiency and innovation in industrial settings. The blend of these transformations signifies a crucial development in modern industry: the ability to achieve economic growth while being mindful of ecological footprints.
For students embarking on a Master’s program in AI in Industrial Production, understanding this symbiosis between green and digital processes is critical. AI is not just a tool for automation and data analysis; it’s a means to achieve sustainable production. This integration offers a holistic approach to industrial challenges, combining economic viability with environmental responsibility.
This course unit covers 100 hours, from which 14 hours lectures, 28 hours lab work, and 58 hours individual study and work.
Specific Objectives / Learning Outcomes
The professional competences that students are expected to develop are:
Strategic Planning for Green and Digital Transformation (GDT): Students will gain the ability to establish strategic approaches for industrial companies to successfully navigate and implement Green and Digital Transformations. This includes formulating plans that integrate sustainable practices with advanced digital technologies, ensuring that the transformation aligns with the company’s overall vision and goals.
Management of Sustainable Production Systems: The course will equip students with competencies to implement and manage sustainable production systems. This encompasses a thorough understanding of digital equipment, Industry 5.0 related technologies, and practices in green manufacturing. Students will also learn about circular economy principles and low carbon approaches, enabling them to create production systems that are not only efficient but also environmentally responsible.
Ethical Implementation of I5.0/AI Solutions: Students will develop the ability to align the delivery of successful Industry 5.0 and AI solutions in production companies with the highest ethical and social standards. This includes understanding the ethical implications of AI and technology deployment in industrial settings, ensuring responsible use, and considering the societal impacts of these technologies.
These competencies are critical in preparing professionals who can effectively lead and manage the integration of AI and green practices in industrial production, ensuring that such transformations are not only technologically advanced and efficient but also socially responsible and environmentally sustainable.
Alignment to Social and Economic Expectations
The course on Green and Digital Transformation in Industrial Production, particularly within the Master’s program in AI in Industrial Production, is meticulously designed to align with current social and economic expectations. In today’s world, there is an increasing societal demand for industrial practices that are environmentally sustainable and ethically sound. The course addresses these expectations by equipping students with the skills and knowledge to implement green manufacturing techniques and AI-driven solutions that minimize environmental impact while maximizing efficiency and productivity.
Economically, the course prepares students to contribute to a rapidly evolving industrial sector where technological innovation is key to competitive advantage. By focusing on the integration of Industry 5.0 and AI, the course ensures that graduates are well-equipped to lead and innovate in a landscape where digital transformation is not just a trend, but a necessity for economic growth and resilience. The emphasis on sustainable and low carbon approaches also prepares students for the future of manufacturing, where environmental considerations are expected to play an increasingly significant role in business strategy and regulatory frameworks.
Socially, the course aligns with the growing awareness and concern over ethical issues related to AI and technology deployment in industrial settings. By incorporating ethical and social standards into the curriculum, the course ensures that students are not only technically proficient but also conscientious about the societal implications of their work. This approach prepares graduates to be responsible leaders in their field, capable of making decisions that balance technological advancement with social responsibility.
Unit 1: Fundamentals of Green Transformation in Companies (2 hours)
- Comprehensive introduction to the concept of green transformation within industrial companies, covering its historical development and current relevance.
- Detailed examination of key strategies and practices for implementing green transformation, including energy efficiency, waste reduction, and sustainable resource management.
- Interactive discussion on the impact of green transformation on corporate responsibility, brand image, and long-term sustainability.
Unit 2: Basics of Digital Transformation in Industrial Settings (2 hours)
- In-depth overview of digital transformation in the industrial sector, focusing on its evolution and the role of emerging technologies like AI and IoT.
- Analysis of the core elements of digital transformation in companies, such as automation, digitalization of processes, and data-driven decision-making.
- Case studies illustrating successful digital transformation initiatives in various industries, highlighting challenges and solutions.
Unit 3: EU’s Twin Transition – Green and Digital (2 hours)
- Exploration of the European Union’s concept of the twin transition in green and digital sectors, outlining its objectives and significance.
- Discussion on how the twin transition approach can be integrated into business strategies and operational models.
- Review of EU policies and initiatives that support the twin transition, and their implications for companies.
Unit 4: Circular Economy, Bioeconomy, and Low-Carbon Economy (3 hours)
- Detailed analysis of the circular economy model, focusing on principles such as reduce, reuse, recycle, and recover.
- Examination of the bioeconomy concept, exploring its role in sustainable industrial production through the utilization of biological resources.
- Discussion on strategies for achieving a low-carbon economy, including carbon footprint reduction and carbon-neutral technologies.
Unit 5: Ethical and Social Standards in AI Solutions (2 hours)
- Overview of ethical considerations in AI implementation, including bias, privacy, and transparency.
- Discussion on the social standards and responsibilities associated with deploying AI solutions in industrial contexts.
- Interactive sessions on best practices for ensuring ethical and socially responsible AI applications in production systems.
Unit 6: Production Resilience – Strategy and Tactics (2 hours)
- Comprehensive exploration of production resilience, focusing on strategic planning and tactical approaches to maintain operational stability.
- Analysis of risk management strategies and contingency planning in production systems.
- Case studies on how companies have successfully implemented resilience strategies in the face of disruptions.
Unit 7: Production Resilience – Processes and Equipment (2 hours)
- Detailed examination of resilient processes and equipment in production systems, including their design and implementation.
- Discussion on the role of AI and digital technologies in enhancing production resilience.
- Review of real-world examples where process and equipment resilience has been critical to maintaining production continuity.
Lab Unit 1: Developing Green Products with Digital Tools – Part 1 (2 hours)
- Focus: Introduction to using digital tools in the design phase of green products.
- Activities: Hands-on practice with software for sustainable product design.
Lab Unit 2: Developing Green Products with Digital Tools – Part 2 (2 hours)
- Focus: Advanced techniques in digital tool utilization for green product development.
- Activities: A project involving the design of a green product using digital tools.
Lab Unit 3: Green Process Improvement with Digital Tools – Part 1 (2 hours)
- Focus: Basics of using digital tools for enhancing the sustainability of production processes.
- Activities: Simulation exercises on process optimization for energy and resource efficiency.
Lab Unit 4: Green Process Improvement with Digital Tools – Part 2 (2 hours)
- Focus: Advanced strategies for implementing digital tools in green process improvement.
- Activities: Group project to redesign an existing process for enhanced sustainability.
Lab Unit 5: Implementing a Circular Approach in Manufacturing – Part 1 (2 hours)
- Focus: Fundamentals of the circular economy concept in manufacturing.
- Activities: Case study analysis of successful circular economy implementations.
Lab Unit 6: Implementing a Circular Approach in Manufacturing – Part 2 (2 hours)
- Focus: Practical application of circular economy principles in manufacturing settings.
- Activities: Simulation exercise to apply circular principles in a given manufacturing scenario.
Lab Unit 7: Ethical and Social Constraints and Opportunities – Part 1 (2 hours)
- Focus: Understanding the ethical and social considerations in AI and GDT.
- Activities: Discussion and analysis of case studies focusing on ethical dilemmas.
Lab Unit 8: Ethical and Social Constraints and Opportunities – Part 2 (2 hours)
- Focus: Exploring opportunities arising from ethical and social adherence in AI and GDT.
- Activities: Developing guidelines for ethical and socially responsible AI applications in industry.
Lab Unit 9: Company Simulation: Establishing a GDT Strategy in Production – Part 1 (2 hours)
- Focus: Crafting a foundational strategy for GDT in a simulated company environment.
- Activities: Role-playing to develop a GDT strategy considering organizational constraints.
Lab Unit 10: Company Simulation: Establishing a GDT Strategy in Production – Part 2 (2 hours)
- Focus: Implementation and refinement of GDT strategies in a simulated setting.
- Activities: Simulation exercise to enact and adjust GDT strategies based on dynamic scenarios.
Lab Unit 11: Company Simulation: Leveraging Industry 5.0 and AI for GDT – Part 1 (2 hours)
- Focus: Introduction to integrating Industry 5.0 and AI in GDT efforts.
- Activities: Interactive sessions on using AI tools for sustainable production optimization.
Lab Unit 12: Company Simulation: Leveraging Industry 5.0 and AI for GDT – Part 2 (2 hours)
- Focus: Advanced application of Industry 5.0 and AI in enhancing GDT.
- Activities: Group project on developing an AI-driven GDT plan for a simulated company.
Lab Unit 13: Company Simulation: Monitoring and Augmenting Performances – Part 1 (2 hours)
- Focus: Techniques for monitoring performance in GDT initiatives.
- Activities: Using analytics tools to track and analyze GDT performance metrics.
Lab Unit 14: Company Simulation: Monitoring and Augmenting Performances – Part 2 (2 hours)
- Focus: Advanced methods for augmenting performance in GDT initiatives.
- Activities: Capstone project that involves refining GDT strategies based on performance data.
Interactive Sustainable Design Software: User-friendly interfaces for life cycle assessment (LCA) and sustainable design – SOLIDWORKS Sustainability.
Process Simulation and Improvement Tools: Drag-and-drop process modeling tools – Microsoft Visio or Lucidchart for digital process mapping, and Minitab for process improvement analysis.
Circular Economy Educational Platforms: Ellen MacArthur Foundation’s Circular Economy Toolkit, which provides interactive learning resources and case studies for understanding circular economy principles.
No-Code AI and Machine Learning Platforms: Microsoft’s Azure Machine Learning Studio, which allow students to create, train, and deploy machine learning models without writing code.
Ethical and Social Impact Assessment Software: SHERPA’s AI ethics toolkit, which offers a user-friendly interface for assessing the ethical and social implications of AI solutions.
Strategic Planning Software: Smartsheet or MindMeister for developing and visualizing resilience strategies in production.
Industrial IoT Platforms with GUI: Bosch IoT Suite that offer graphical user interfaces for creating IoT solutions without coding.
VR/AR for Industry 5.0 Visualization: Easy-to-use VR and AR applications, such as Sketchfab or Vuforia Studio, for interactive visualization of Industry 5.0 concepts.
Performance Monitoring and Analytics Tools: Zoho Analytics, allowing students to monitor and interpret performance data without needing to program.
Green Manufacturing Demonstrative Models: Physical or virtual models demonstrating sustainable manufacturing practices.
Data Visualization Software: Drag-and-drop data visualization tools such as Tableau Public for creating visual representations of data and analysis outcomes.