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Over the past decades, circular economy has established itself as an important strategy for tackling sustainability challenges. Its holistic approach aims to use resources efficiently and minimize waste. This article aims to identify and evaluate the numerous challenges connected to the successful implementation and expansion of the circular economy approach. Economic, technological, social and political aspects are examined to provide a comprehensive insight into the complexity of the strategy and its implementation. The article concludes that a successful circular economy can only be achieved through the coordinated cooperation of different stakeholders and the development of innovative solutions to the identified challenges.

Machine learning offers a wide range of potential, especially in production, and is therefore becoming increasingly important. However, small and medium-sized businesses are lacking guidelines that are specifically tailored to their individual challenges to guide them step-by-step through the process. In conjunction with a potential analysis, the determination of relevant prerequisites and a maturity assessment, this article can serve as a guide for SMEs.

Artificial intelligence is increasingly being integrated into vehicles, but conventional product development processes often do not fully capture the specific requirements of AI projects. In order to meet these requirements, a process reference model (PRM) has been developed specifically for the development of AI functionalities in the automotive industry. This model is intended to support companies in adapting their conventional software development processes more easily to the special features of AI projects.

In modern production companies, a heterogeneous IT landscape often complicates day-to-day work. A promising antidote is the use of intelligent agents, which use generative AI for routine tasks and can therefore increase efficiency. Whether these intelligent systems can be successfully integrated into existing networks determines whether the flow of information can be improved and manual effort reduced.

In industrial assembly, humans are working more closely with machines due to assembly assistance. However, despite their great potential, the implementation of digital systems is time-consuming, which entails high training requirements. Small and medium-sized businesses, in particular, are reaching their limits. A newly developed setup system is designed to facilitate the introduction and use of such assembly assistance systems and increase their acceptance.

The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production.

A new look into the depths of technical drawings: A deep learning framework reads CAD drawings more accurately than ever before, recognizing geometrical dimensioning and tolerancing, dimensions, and every other detail. What used to be tedious manual labor is now carried out by an AI that understands the special features of every line and label. This promising technology not only increases accuracy but also speeds up the processing of drawings considerably. The system thus opens up new avenues for precision in production.

Learning factories promote digital transformation through an interdisciplinary approach between lean management, Industry 4.0, energy efficiency, training center or research farm. SME centers are characterized by the on-site integration of small and medium-sized companies. Such a regional strategy, combined with learning factories, promotes a goal-oriented dialog between science and practice where students can put their theoretical knowledge to the test.

The volatility of economic conditions and rapid technological progress require production sites to be constantly adapted and improved. This needs highly qualified factory planners who can use digital planning tools efficiently. The best qualifications emerge from closely interlinking the teaching of planning methods and the application of these methods in a planning project according to the principle of “learning by doing”.

Nowadays, maintenance logistics is considered an integral part of sustainable maintenance management and is now conducted with IT support in many of the fields where it is applied. It can therefore be seen as one of many examples of digitalization in the working world. Both the selected maintenance strategy, the implementation of which is more or less linked to the use of intelligent technologies, and the current level of IT integration are emblematic of the degree of digitalization in this context. In the agricultural sector, the type of maintenance objects in question plays an important role in the use of digital technologies. This article is dedicated to investigating the status quo of digital maintenance logistics in German agricultural businesses at the interface of ICT, technology and business.