learning

Learning and skill-enhancing work designs are essential for new work and organizational concepts such as “learning organizations” and “Industry 4.0.” Developing and applying criteria for promoting learning and skills development in digitally transformed work environments enables more effective and efficient work processes, makes work more people-centered, and the AI-based future of work more manageable. Digitalization is also introducing work-integrated learning formats such as online communities, learning platforms, and digital cognitive assistance systems that already meet many of these criteria. In the future, designing work environments that promote learning and skills development will become a central task of corporate training and personnel and organizational development.

An important element for companies to deal with the demands of the world of work is the continuous and needs-specific further training of employees. The possibility of learning close to the workplace has a major role to play here.

In the past the dynamics of the markets in interaction with an increasing globalization led to the fact that industries concentrated more and more on their core competences. By the reduction of the companies vertical integration value-added processes were outsourced in the same measure as supporting activities. In order to be able to meet the so developing complexity, production and logistics networks were formed to enhance the co-operation between the enterprises in a long-term and stable form. In these networks beside products and semi-finished materials also knowledge and information are produced and transferred. The protection and division of knowledge, experience and behaviours are of a special relevance if e.g. a participant leaves the network. In such a case the question arises whether and how the knowledge of the separating participant can be particularly retained for the network and a possible advancing producer, in order to not disturb the efficiency of the network. For the ...

The realization of cognitive technical systems deals with the implementation of a knowledge representational level inside technical systems. Hence, cognitive functions and processes (planning, learning etc.) can be used. Especially, the realization of learning (in contrast to adaptation) is the key for novel applications. In application, fields dealing with the guidance of complex systems which can not partially or completely be performed by human operators depending on the system itself, promise a new quality of automation.