Künstliche Intelligenz

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.

A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.