Green and Digital Transformation

Syllabus

Introduction

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.

Total Hours

This course unit covers 100 hours, from which 14 hours lectures, 28 hours lab work, and 58 hours individual study and work.

General Objective

The primary objective of this course on Green and Digital Transformation in Industrial Production, tailored for a Master’s program in AI in Industrial Production, is to equip students with the necessary skills and knowledge to implement Industry 5.0 and AI solutions in production systems. This course focuses on achieving green, sustainable, and resilient results. It aims to provide an in-depth understanding of the convergence of advanced AI technologies with the principles of sustainable industrial practices. The course is designed to enable students to explore and apply AI-driven strategies that not only enhance efficiency and productivity but also ensure environmental sustainability and resilience in the face of evolving industrial challenges. This holistic approach is crucial for shaping future professionals capable of leading the transformation towards more sustainable and technologically advanced industrial production systems.

Specific Objectives / Learning Outcomes

For the course on Green and Digital Transformation in Industrial Production, specifically within the context of a Master’s program in AI in Industrial Production, the specific objectives are:

Understanding the Fundamentals of Industry 5.0 and AI: To provide students with a thorough grounding in the concepts and technologies underpinning Industry 5.0 and AI. This includes studying the latest advancements in machine learning, automation, and data analytics, and how they can be integrated into industrial production.
Application of AI for Sustainable Production: To enable students to apply AI techniques to enhance sustainability in production processes. This involves learning how to use AI for optimizing resource usage, reducing waste, and minimizing environmental impacts, aligning with green transformation goals.
Developing Resilient Production Systems: To teach students how to design and implement production systems that are not only efficient and sustainable but also resilient to disruptions. This includes understanding how AI can aid in predictive maintenance, supply chain optimization, and risk management.
Practical Implementation Skills: To provide practical experience through projects or case studies where students apply their AI knowledge to real-world industrial production scenarios. This hands-on approach is aimed at bridging the gap between theoretical knowledge and practical application.
Critical Analysis and Problem-Solving: To cultivate the ability to critically analyze current production systems and propose AI-driven improvements. This objective focuses on developing problem-solving skills that are essential for addressing the complex challenges faced in modern industrial environments.
Ethical and Environmental Considerations: To ensure students understand the ethical implications of AI implementation in industrial settings and its environmental impacts. This includes discussing responsible AI use and sustainable practices within the industry.

These specific objectives are designed to provide a comprehensive and practical understanding of how AI can be effectively integrated into industrial production to achieve green, sustainable, and resilient outcomes.

Professional Competencies

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.

Cross Competencies

For the course on Green and Digital Transformation in Industrial Production, especially within the context of a Master’s program in AI in Industrial Production, the cross-competencies that students are expected to develop include:

Interdisciplinary Collaboration: The ability to work effectively across various disciplines, integrating knowledge from AI, environmental science, engineering, and business management. This competency is essential for addressing the multifaceted challenges of green and digital transformation in industrial production.
Critical Thinking and Problem-Solving: Developing the skill to critically analyze complex industrial problems and devise innovative, sustainable solutions. This involves applying AI and Industry 5.0 principles in creative ways to tackle environmental and technological challenges.
Communication and Leadership Skills: Gaining proficiency in communicating complex ideas effectively to a diverse range of stakeholders, including technical teams, management, and non-technical audiences. Leadership skills are also crucial for driving change and guiding teams through the transformation process.
Ethical Judgment and Social Responsibility: Cultivating the ability to make decisions that are ethically sound and socially responsible, especially in the application of AI and digital technologies in industrial settings. This involves understanding the broader societal impacts of these technologies and striving for solutions that benefit society as a whole.
Adaptability and Continuous Learning: The capacity to adapt to rapidly changing technological landscapes and the willingness to engage in continuous learning. This is crucial in a field like AI, where new developments and techniques are constantly emerging.
Project Management and Implementation Skills: Acquiring the skills to manage projects effectively, from planning and resource allocation to execution and evaluation. This includes understanding how to implement green and digital initiatives in an industrial context, dealing with challenges such as budget constraints, timelines, and stakeholder expectations.

These cross-competencies are integral to the holistic development of professionals who can lead and innovate in the field of industrial production, ensuring that they are not only technically proficient but also capable of navigating the complex, interdisciplinary landscape of green and digital transformation.

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.

Evaluation

Assessment Methods:

Theoretical Lectures Component:

Quizzes: Regular in-class and online quizzes will assess students’ understanding of key concepts in green and digital transformation, including Industry 5.0, AI applications, and sustainable production methodologies.
Written Assignments: Students will be required to submit assignments that involve exploring and critically analyzing the implementation of green and digital transformations in industrial settings, focusing on problem-solving and practical application of theoretical knowledge.
Midterm and Final Exams: Comprehensive exams will evaluate students’ overall grasp of the course material. These exams will include multiple-choice, short answer, and essay questions, particularly focused on the integration of green practices and digital technologies in industrial production.

Practical Laboratory Component:

Lab Reports: Students must submit comprehensive lab reports documenting their case studies and simulation projects in green and digital transformation, focusing on methodology, results, and analytical insights.
Oral Presentations: Students will present their lab projects, assessed based on presentation skills, content clarity, and their ability to engage the audience, especially focusing on the solutions developed in green and digital transformation.

Assessment Criteria:

Lectures Component:

Knowledge and Understanding: Evaluating students’ comprehension and application of core concepts in green and digital transformation.
Analytical and Problem-Solving Skills: Assessing the ability to analyze complex industrial challenges and effectively apply green and digital transformation solutions.
Communication Skills: Gauging proficiency in clearly and engagingly conveying concepts and solutions related to green and digital transformation.
Teamwork and Collaboration: Evaluating the ability to work effectively in teams, particularly in group projects and discussions.
Application of Technology: Assessing proficiency in using digital tools and understanding their application in green industrial transformation.

Laboratory Work Component:

Technical Skills: Evaluating competence in applying technical skills to develop practical solutions in green and digital transformation.
Quality of Work: Assessing the ability to produce high-quality, innovative applications for green and digital transformation in industrial settings.
Creativity and Innovation: Gauging capacity for creative thinking and innovation in developing green and digital transformation solutions.
Attention to Detail: Evaluating thoroughness in documenting and executing projects.
Time Management: Assessing effectiveness in managing time to complete lab tasks and projects.

Quantitative Performance Indicators:

For Lectures:

Attendance and Participation: Minimum 80% attendance and active participation in class discussions required.
Homework and Quizzes: Completion of all assignments and quizzes with a minimum average score of 60%.
Midterm Exam: Minimum score of 50% required.

For Lab Works:

Lab Attendance and Participation: Full attendance and active participation in lab sessions required.
Lab Reports: Timely submission of all lab reports, each scoring a minimum of 60%.
Lab Assignments: Completion of all lab assignments with a minimum average score of 60%.
Lab Exams: Minimum score of 50% required on lab exams.

For Final Exam:

Comprehensive Understanding of Course Material: Minimum 70% of total questions correctly answered.
Demonstrating Understanding of Key Concepts: Minimum 50% score on multiple-choice and short-answer questions.
Application of Concepts to Practical Problems: Minimum 50% score on problem-solving questions.
Critical Evaluation of GDT Impacts: Minimum 50% score on essay questions.
Evidence of Practical Application: Demonstrated through correctly answered application-based questions.
Display of Critical Thinking Skills: Evidenced by correct answers to analytical and synthesis questions.
Overall Exam Performance: Evaluated as a percentage of the total exam score, with a minimum passing mark of 50% or above.

Lectures

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 Work

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.

Supporting Infrastructure

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.

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