Autor: Christopher Prinz

The paper reflects the potential and remaining shortcomings of AI-based work systems for exploiting and enhancing individual and organizational learning processes. It especially refers to the use adaptive systems in production and gives examples of good practice for the design of AI-based work systems which promote the interplay between individual and artificial intelligence. The conceptual framework refers to different methods in machine learning which are complemented by insights from individual and organizational learning theory.

The increase of complexity in the field of production due to “Industrie 4.0” causes also a rapid increase of the complexity of tasks on the shopfloor level. Thus, efficient methods for the systematic identification of the competence deficits of the employees as well as new forms of knowledge management for an adequate administration of knowledge have to be established.

As a result of continuously increasing economic constraints for the producing sector in Germany due to competitors from low-wage countries, production paradigms are changing substantially. This paradigm shift is characterized by rapidly advancing automation processes. Hence, highly complex Cyber-Physical Production Systems (CPPS) are developed and put into practice. In combination with decreasing numbers of staff and the resulting loss of knowledge, this leads to a deficit of competence required to handle the increasing complexity of CPPS. As a result, a need for innovative assistance systems arises for the support of the remaining employees. The given article describes the challenges and problems and drafts a potential technical solution as well as challenges concerning organization and staff.

Learning factories in Germany concentrate on the two classic aspects of production: Optimization of production processes (lean management) as well as the efficient use of resources. The learning factory of the chair of production systems at Ruhr-University Bochum considers also the topic management and organization of labour to convey competences in the field of co-determination. In cooperation between the faculty of mechanical engineering and social sciences as well as the office of cooperation RUB/IMU different learning factory exercises have been developed, which impart this learning factory a special significance.