Typeset

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.

Musculoskeletal disorders are the major cause of incapacity to work in the production industry in Germany. Accordingly, ergonomic work processes are particularly important in order to protect the health of employees and to reduce the high follow-up costs for companies and society. This article presents an approach that assesses and analyzes the individual worker capabilities and individual strain at the workplace with the help of modern motion capture systems. A learning assistance system uses the analysis results for ergonomic behavioral prevention in production.

In the digital transformation, managers are facing changed requirements. They are important actors in the design process of the digital transformation, for which they must change their leadership behavior and acquire the corresponding competencies. In corporate practice, it is necessary to address these issues systematically in leadership training. However, such a qualification is a major challenge for companies. The article shows demands on leadership in the digital transformation and presents an innovative procedure for the competence development of managers at a provider of professional training.

Assistance systems are being used increasingly in the context of digital transformation. They can support employees in industrial production processes both in the learning phase and in the active work phase. In this way, competencies can be built up in a way that is close to the workplace and the process as well as demand-oriented. This paper discusses the current state of research on the possible applications of these assistance systems and illustrates them with examples. Among other things, the current challenges are also highlighted. At the end of the paper, focal points for the future development of AI in industrial learning processes and research on this are identified.

The ongoing digital transformation is changing work and production environments of companies. In addition, a demand-oriented development and efficient use of employee skills are becoming ever more important for success. Thus, there is a need for new decentralized and situationally adaptable solutions that enable self-organized learning paths and informal competence development. This paper presents challenges of collaborative competence development and solutions for IT-supported self-organized learning paths at different organizational levels and shows project examples from the textile industry.

Digitalization and the possibility to use big data give companies the opportunity to establish hybrid business models. This enables them to offer customers smart services in addition to their physical products, create more value and strengthen their competitiveness. The hybridization goes along with new competence requirements, which need to be shaped. In the AnGeWaNt project, hybrid business models were developed and implemented in three companies. This article describes the approach to analyze new competence requirements and how to build up new skills for a successful hybridization

Automatization and Digitalization are keywords regarding innovative assembly of Smart Factories. Despite all, no Smart Factory is able to produce completely without blue color workers. To stay competitive in the global world companies they need to find the balance to hire only as less employees as possible to guarantee all assembly processes. Therefore, Labor Efficiency is the key figure to control attendance and output. This article explains the role of labor efficiency with regards to the Smart Factory. For this purpose, the input factors attendance and output are explained and illustrated by practical examples. Moreover, it explains how labor efficiency can influence competitiveness positively.

Initiated by the digitalization, the organization of production and employees are subject to change. Innovative and digitized formats should be integrated into existing training programs. Therefore, suitable and target group-specific teaching/learning formats are needed that support participative methods and digital collaboration. For this purpose, a digital teaching and learning format for the application area of automotive engineering in SMEs was designed. This prototypical teaching/learning format was created and evaluated in an iterative process through the participation of the potential. The results show that the test subjects mainly refer to the usability criteria of DIN ISO 9241-110. Recommendations for the creation of future digital teaching and learning formats for SMEs are derived from these findings.