Industrie 4.0

In the course of digitalization, collaboration between humans and machines is inevitable. This should be considered as early as possible in further training. There’s a major obstacle to this in mechanical engineering: the lack of access to the knowledge needed for success. This can have a negative impact on the acceptance of digitalized processes. A teaching and learning platform teaching digitalization on real machines does important work here.

The success of technologies depends not only on their innovative strength and acceptance, but also on their management. Decision-makers evaluate factors like technical framework conditions and organizational requirements, with the demand for flexibility adding to the complexity. Industry 4.0 addresses this with networking, transparency and decentralized decisions. Digital twins, which can be implemented with open source software, play a key role.

In the context of Industry 4.0, learning factories are important places for company-based learning. Studies show that they have continued to develop since their emergence and are no longer limited to vocational and academic education. This leads to the question of how much the concept of the learning factory represents an innovative approach to further training in small and medium-sized enterprises (SMEs). This article focuses on three selected learning factories relevant to continuing education that were analyzed using qualitative methods with regard to their concepts and cooperation. The findings are embedded in a theoretical framework that links the scientific discussion on learning locations and educational cooperation. The empirical findings from three learning factories illustrate relevant learning locations for continuing education in SMEs.

The shortage of skilled labor is putting pressure on many manufacturing companies worldwide. While skilled labor is becoming scarcer in traditional industrialized economies, proper training is urgently needed in countries with high unemployment. But how to solve this challenge? Find out what makes learning factories so successful in this issue.

In order to cope with growing customer requirements and the associated increase in complexity, companies are opening up their value chains, reducing their vertical integration and increasingly entering into collaborations. Cross-company data exchange along the supply chain is thus becoming a key component for competitiveness and the realization of customer-specific solutions. For this reason, the European Union has launched the GAIA-X project, which aims to create the next generation of data infrastructure for Europe and its companies. The GAIA-X maturity model offers an approach for classifying companies into different development stages and provides concrete requirements for further development along a predefined development path towards becoming a fully-fledged participant in the federated GAIA-X data infrastructure.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.

SME often encounter risks and obstacles when implementing IIoT solutions, but these challenges can be mitigated with the use of an IIoT platform. To select the appropriate platform, a decision- making approach has been developed. By choosing the right use cases, companies can directly benefit from IIoT implementation and gain a competitive edge in the market. Our research indicates that at least three applications have a favorable balance between benefits and effort. Once successfully implemented, these applications can be expanded and scaled as the company becomes more digitally proficient.

Professor Bernd Scholz-Reiter studied industrial engineering at the Technical University of Berlin. After several positions in Germany and abroad, he accepted the call to the University of Bremen in 2000, where he initially held the professorship of Planning and Control of Production Systems in the Department of Production Engineering. From 2002 to 2012, he also headed the Bremen Institute for Production and Logistics (BIBA). From 2012 to 2022, Bernd Scholz-Reiter was rector of the University of Bremen.