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

This paper focuses on the investigation of current technical challenges in the context of industrial Virtual Reality (VR)-based training applications. This paper analyzes the current state of the art of industrial VR applications and provides a structured overview of the existing technical challenges. The identified challenges are discussed based on an industrial training scenario for the safe handling of electrical components.

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.