The rapid development and widespread use of digital technologies such as AI and robotics in production lines has led to increased competition for innovative manufacturing companies. In order to meet the demands of the market, AI-based decision making is essential for optimising and flexibilising individual production systems.
The overall objective is to provide learning material for the application of AI for decision making in SMEs and MidCaps. We therefore propose an integrated approach. First, we need to remove the barriers to AI adoption. This should start with design thinking and physical experimentation to create the appropriate context for the introduction of AI technology. Bridging the gap between theoretical knowledge of AI technology and the study of specific manufacturing processes
Then we go into the details of what was used to make the AI decision. What was the state of the data (e.g. data quality, missing values in the data, etc.)? Finally, we show how AI techniques are applied, for example in generative AI and quality analysis. The knowledge and technical limitations of different AI techniques are highlighted.
The introduction of AI often requires a change in work processes and mindsets. Targeted knowledge building can support the introduction of AI and increase the acceptance and, above all, the benefits for stakeholders and employees.