Training

Learning factories offer the possibility to plan, execute and analyze the knowledge imparted in theory on realistic industrial systems. This article analyzes the potential of developing and operating a learning factory in a virtual environment. For this purpose, institutions with learning factories are surveyed regarding the challenges and desires in the operation of learning factories and the mentioned aspects are discussed with regard to their representability in Virtual Reality. The result shows that Virtual Reality positively influences a large part of the aspects and has a high potential to solve current challenges in the establishment and operation of learning factories.

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.

Employees, machines and products get connected: the 4th Industrial Revolution has begun [1]. This is a central statement on the homepage of the German „Plattform Industrie 4.0“. Meanwhile the basic ideas started to move from the shopfloor level to the level of business processes and business models. In consequence it was detected pretty early, that digitization will affect the employees and their qualification and tasks in the companies; the term Work 4.0 evolved. The following contribution emphasizes the systematization of qualifications. A new model will be displayed, that helps to bring transparency to the distribution of qualification in companies. The useability of the model is verified in two case studies. The model is a practicable instrument for companies to gain a realistic view to the upcoming challenges of the shift of qualification due to digitization.

The authors present a methodological approach for designing assistance systems conducive to learning and derive requirements for their design. They base the design of these systems on a fundamental understanding of the cooperation between humans and machines, which still places decisions and responsibility with humans. Finally, the authors show concrete requirements and measures of a participatory design and implementation process.

The digital transformation, especially the shift of maintenance to smart maintenance, contains a number of challenges. The management of data plays a significant role in this [1]. The use of sensor technology and devices for mobile data acquisition offers a variety of possibilities for quickly capturing large amounts of data, even in real time. In this context, however, there is also a need for tools that enable efficient data exchange and, at the same time, structured storage of large amounts of data. One of these tools is the digital lifecycle record, which has enormous potential for data-driven services thanks to its special features.

The employee remains a key productivity element in industrial assembly. Assembly assistance systems have therefore become an integral part of employee support. This paper presents a novel assistance system that complements process-related assistance with human-centered functionalities. In addition, an approach for the systematic evaluation of assembly assistance systems is presented in this paper. The research is based on an evaluation of the current state of the art through systematic market analysis of available assembly assistance systems.

German industrial companies are suffering from an increasing shortage of skilled workers. In order to secure Germany’s existing competitive advantages, suitable solutions have to be carried out to counter this shortage. Technologies in the context of “Industry 4.0” offer promising solutions. Using this technologies, significant productivity improvements as well as higher resource utilization rates can be achieved. However, the main challenge is to identify the right technical solutions for the specific business challenges. In the following, a systematic approach is presented to face these challenges.

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.