artificial intelligence

Artificial intelligence is often mentioned often mentioned in the same context as Industry 4.0, but the exact role of AI is unclear. Is AI just another 4IR technology or an essential "enabler" for other 4IR technologies? Six experts assess the impact of AI on 41 4IR technologies. AI could indeed be a decisive factor in unleashing the full potential of Industry 4.0.

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.

In our contribution we underline the relevance of creative intent for making semi-autonomous human-AI systems reliable and performative. We show the challenges resulting from the autonomy of AI-based systems and argue that human creative intent allows to cope with them and to bring systems forward. It provides them with the capacity to generate meaningful contextual interactions through hybrid human-technology reflective practices. We illustrate the necessity for enhancing the reliability of systems on the basis of a high variety of use cases from (semi)autonomous driving, manufacturing, software development, healthcare and higher education - all of outstanding relevance for societal development towards a bright future. Against this background we discuss how to design technology for keeping human creative intent and performative reflective practices in the loop. The outlook gives emphasis to future research methods with respect to living lab components and ethnographic research in ...

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.

The need to sometimes respond very quickly to changes requires companies to have a high degree of flexibility and speed of reaction. Application system architectures, which usually consist of old and self-developed systems, often do not allow companies to meet these requirements. However, investment funds for new software are limited, so priorities must be set when it comes to replacing legacy systems. An adaptability analysis is an efficient analysis method for planning the renewal of the application system landscape. This article describes the procedure and results of an adaptability analysis, using the example of an internationally active automotive supplier.