Künstliche Intelligenz

Global competitive pressure is forcing companies to use resources efficiently, especially in high-wage countries. This is further intensified by market and legislative pressure for sustainable products and processes. In the face of digital and ecological change, holistic approaches to optimizing manual work processes are essential. An AI-supported assistance system for work plan creation is intended to remedy this and thus enable more efficient process design.

EU regulations demand more intensive and transparent sustainable practices from companies. Industry needs to adapt many processes and products to take charge of this responsibility. Artificial Intelligence (AI) in particular offers innovative potential. Firstly, however, this technology needs to be evaluated focusing on weak AI—market-ready systems that perform specific tasks using algorithms and data-supported models efficiently.

The rapid development of artificial intelligence (AI) is constantly opening new opportunities, particularly in training for the factory of the future. For employees, this not only means a significant advantage in the actual manufacturing process, but also in the field of continuing education. This paper provides an overview of AI tutoring systems continuing education in the context of Industry 4.0 by presenting a categorization that discusses different approaches of AI tutoring systems by learning methods, application areas and their respective technologies. In addition, an outlook on the disruptive effect of generative AI on AI tutoring systems in Industry 4.0 is given.

The integration of AI in Industry 4.0 is steadily increasing. Applications include both single-purpose and generative AI systems in operation practices as well as training approaches. In addition to the technical challenges posed by these systems, organizations need to assess, plan and support the organizational changes associated with technology integration.

For the transition to a sustainable economy to succeed in its entirety, logistics must be considered in addition to raw materials and manufactu­ring. Artificial intelligence will play a central role in improving the exchan­ge of data between the individual links in the supply chain and in regula­ting processes and costs at the various stages of production. The meat industry, with its hygienic and increasing ethical requirements for animal welfare, is set to greatly benefit from the digital revolution.

Artificial Intelligence methods can be structured according to different aspects. Applications within Industrie 4.0 can also be classified into levels and process groups using the RAMI framework or the ISA95 standard. However, a taxonomy is lacking that relates the classification of the application areas to the processes improved by machine learning methods while at the same time locating and evaluating them. Such a framework helps to classify new processes and solutions and supports finding suitable machine learning methods for concrete problems in the Industry 4.0 context.