Design

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.

Agile methods are extremely useful in solving complex problems. This is particularly beneficial in market environments where the routines of traditional corporate management are constantly being questioned. Agility is closely linked to the core ideas of Lean Management, as evidenced by the focus on processes and people. Lean factories facilitate a hands-on engagement with Lean principles, thereby promoting agile process management in various production contexts.

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.

Digitalization opens up new forms of operational training. A growing focus is on smart services, which allow for proactive engagement with customer demands and empower businesses in times of digitalization. While traditional learning environments are rarely tailored toward individual needs, smart services offer new opportunities. Decentralization, previously only a utopic vision, is becoming the reality now.

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.

While generative artificial intelligence has gained more visibility and achieved initial successes, it is largely unused in the industry context. In contrast, its development and versatility point to a promising application for industrial manufacturing – especially in cases where complex challenges such as decisionmaking or process optimization are present. Showcasing the various development horizons and several example case studies provides a particularly illuminating illustration of its potential for the field of technology management.

The planning and implementation of automated warehouses is characterized by high investments and risks. The FMEA (Failure Mode and Effects Analysis) currently used to reduce risks requires a great deal of effort to conduct, as it has deficits in terms of design and implementation support. These deficits include a predominant focus on the process view without linking this to the design FMEA for automation objects, an insufficient structure for the use of similar repetitive processes and technologies, a lack of automated, parameterized generation of activities, failures and causes, and a lack of integrated test scenario derivation. These deficits lead to unrecognized failures and increase the effort required to carry out the FMEA and develop test scenarios. In this article, we present a generic FMEA model which, among other things, is able to access extensive practical data in the form of knowledge bases and thus resolve the aforementioned deficits.