The goal of this intensive course is to explore how artificial intelligence (AI) can support manufacturing companies to innovate along the journey towards sustainability. 
The course aims to cultivate a cohort of forward-thinking individuals ready to shape the future of the industry. It focuses on providing a holistic and impactful learning experience that prepares participants to make meaningful contributions at the intersection of AI and sustainable innovation. The programme includes project-based learning activities to enhance problem-solving skills and encourage teamwork and collaboration.
The intensive course is launched within the EIT manufacturing framework, and it is organized by TU Wien in collaboration with the Università di Trento and Fondazione Hub Innovazione Trentino (HIT).

Language

The language of all the activities is English.

Contents

Tue, Nov 19

• 17:00-20:00 Welcome reception
• 17:30-17:45 Introduction to EIT Manufacturing (Enrico Blanzeri)
• 17:45-18:00 Introduction to TU Vienna (Sebastian Schlund)
• 18:00-18:30 Keynote on “Positive Impact Production: The Digital and Sustainable Future of European Industry” (Fazel Ansari)
• 18:30-20:00 Get Together
• 20:00 End of Day 1

Wed, Nov 20

9:00-16:00 Circular Economy/End-of-Life-Management (Stefanie Eisl, Sebastian Seisl)

• 9:00-10:00 Circular Economy Simulation game (Duration: approx. 50min, 10 min break):
  • Engage participants into a simulation game that illustrates the concepts of resource use, waste generation, and circularity
  • Facilitate discussions among participants to reflect on their experiences during the game and draw connections to real-world scenarios.
  • Encourage participants to ask questions and clarify any doubts they may have about the concepts introduced.
• 10:00-11:00 Theoretical Approaches to Circular Economy (Duration: approx. 50min, 10 min break):
  • Problem definition of the linear economy and its environmental and social impacts.
  • Detailed exploration of circular economy principles, including resource efficiency, waste reduction, and closed-loop systems.
  • Introduction to theoretical frameworks like the butterfly diagram and the R framework.
  • Discussion of regulations and policies promoting circular economy practices.
• 11:00-12:00 Circular Business Modeling (Duration: approx. 60 min)
  • Overview of various circular business models, such as product-as-a-service, resource recovery, sharing platforms, …
  • Examination of case studies illustrating successful implementation of circular business models across different industries.
  • Discussion of challenges and barriers to adopting circular business practices.
  • Group work: Divide participants into small groups and assign each group a product to analyze its lifecycle and brainstorm potential circular business models à Encourage groups to share their findings and ideas with the rest of the class, facilitating peer feedback and discussion.
    • Reflection: Conclude the session with a reflection on key takeaways and insights gained and encourage participants to consider how they can apply their knowledge in their own work or studies.

• 12:00-13:30 Lunch Break
• 13:30-16:00 Company Challenge
  • Workshop on real Circular Economy case study
  • Application of knowledge and methods
• 16:00 End of Day 2

Learning objectives:

• Understand the concept of circular economy & recognize the difference between linear models: Participants should grasp the fundamental principles and goals of a circular economy, distinguishing it from linear models.
• Familiarize with key terms and principles of circular economy: Participants should become acquainted with essential terminology, frameworks, and principles associated with the circular economy.
• Explore theoretical frameworks and models used in circular economy analysis: Participants should become familiar with various theoretical frameworks and models used to analyze and implement circular economy practices.
• Understand different circular business models: Participants should be able to identify and evaluate various circular business models, understanding their respective advantages, challenges, and applications.
• Apply theoretical knowledge to develop practical circular business strategies: Participants should be able to apply theoretical concepts and frameworks to develop practical strategies for implementing circular business models.

Methods:

• Simulation game (Role-Play exercise)
• Theoretical lecture incl. presentation of case studies
• Interactive group work

Thu, Nov 21

9:00-16:00 Applied AI in Manufacturing (Andreas Steiner)

• 9:00-10:30 Introduction to AI in Manufacturing
  • Real-life applications! Addressing existing manufacturing challenges.
  • What is AI and why is it relevant to manufacturing problem-solving?
  • Overview of AI technologies relevant to manufacturing (Deep Learning, Computer Vision, Natural Language Processing, etc.)
  • Discussion of benefits and challenges in implementing AI within manufacturing contexts.
  • Building Blocks for AI Implementation
  • Presenting an industrial use case to illustrate the practical application of AI in solving manufacturing issues.
  • Introduction of the CRISP-DM framework and its role in providing structure solutions
  • Emphasis on the importance of data, covering data gathering methods, data usability enhancement, and types of data crucial for AI applications.
  • Strategy Game - Data Understanding
  • Hand-on group activity using example raw data patterns to strategize data utilization and problem-solving approaches.
• 10:30-12:00 Machine Learning and its Application in Industry
  • Fundamentals of Machine Learning
  • Working with data - Techniques for data preprocessing
  • Supervised Learning (Classification, Regression)
  • Unsupervised Learning (Clustering, dimensionality reduction)
  • Practical Application Session
  • Live coding session to demonstrate the implementation of machine learning algorithms on manufacturing data.
  • Utilizing tools like Mentimeter for real-time audience engagement in algorithm selection and evaluation metric determination.
  • Future Trends and Opportunities
  • Future-oriented AI trends (e.g., edge computing, digital twins, …) and their potential impact on manufacturing.
  • Interactive Quiz: Conclude with a Kahoot quiz to reinforce learning
• 12:00-13:30 Lunch Break
• 13:30-15:30 Company Challenge
  • Workshop on real AI case study
  • Application of knowledge and methods
• 15:30-16:00 Wrap-Up and Feedback

• 16:00 End of Day 3

Learning objectives

• Students can grasp the necessity of AI in industrial applications, articulating its advantages and understanding the current state of AI technologies.
• Through the utilization of the CRoss-Industry Standard Process for Data Mining (CRISP-DM), students can incorporate AI into industrial settings.
  • They can understand the importance of data by effectively extracting it from the environment, utilizing various techniques such as data collection sensors or data mining methods. Furthermore, they gain knowledge how to preprocess data to ensure its suitability for AI model integration.
  • Depending on the chosen data, students should be able to select an appropriate Machine Learning model tailored to the specific industrial application. Moreover, they know how to evaluate the performance of these models using relevant metrics (i.e., cross-validation) and identify potential limitations.
  • Students should be able to interpret the results, generated by the AI models, and implement them into industrial processes.
• Students can analyze emerging trends and technologies in AI, understanding how they might affect different industrial sectors and predicting their potential impact on future workflows.

Methods

• Theoretical lecture + real-life applications
• Use Case Activity - Hands-on group activity
• Live Coding Session (incl. Mentimeter)
• Interactive quiz

Target Group

Master students and manufacturing professionals (max. 25, min. 10; non-inscribed in EIT Manufacturing master programmes). To ensure an enriching and productive learning experience, a technical background is preferred. 

Tuition fee

The general fee including all the education activities and lunches is 200 €. 
Tuition waivers are foreseen for:

• students coming from the RIS countries: 150 €
• students coming from the EITM Master School partners universities: 150 €
• associates to stakeholders organizations: 150 €
• women are encouraged to join the school with an additional 50 € discount on the tuition fee of the relative category (they will pay 150 €, 100 € respectively)

Enrollment

An online application must be submitted with this form, öffnet eine externe URL in einem neuen Fenster by the 8th of November. Confirmation and payment instructions will follow upon acceptance.

For further information, please contact: sebastian.schlund@tuwien.ac.at

Location details

Gußhausstraße 25-25a, 1040 Wien 

Seminarraum CHEG

You can see the location in Google Maps here, öffnet eine externe URL in einem neuen Fenster.