Industry 4.0

Change is necessary for companies to maintain their competitive edge—both digital and ecological change. But while external support is at hand, the drive for change must come from companies themselves. In this issue of Industry 4.0 Science, experts of the Academic Society for Work and Industrial Organization discuss how the real-world application of innovative technologies lead to resource-efficient manufacturing.

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 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.

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.

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.