Design

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”.

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.

Establishing “smart” production processes that are focused on sustainability and based on aggregated data requires a great deal of exchange at various levels. The vertical integration of different production components provides companies with an important basis for achieving their sustainability goals. Digital twins can play a decisive role in driving this process forward.

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.