Manufacturing Systems

For a successful implementation as well as sustainable use and maintenance of digital twins for production and logistics systems, it is necessary to identify relevant use cases and master the associated challenges. This paper analyzes scientific literature on common applications and challenges in the implementation of digital twins for the planning and operation of production and logistics systems. To confirm the practical relevance of the results, the results of an empirical survey have also been included. The findings are used to derive key focus areas for the successful implementation and long-term use of digital twins in production and logistics.

In today’s manufacturing industry, digital twins are a key enabler for optimizing production processes and efficient resource use. However, creating digital twins is often associated with high or difficult-to-estimate costs and typically requires unknown characteristic values, such as material parameters, making practical implementation challenging. With RAPIDZ, we present a tool for creating and using digital twins that overcomes these barriers through its modular structure. The virtual modeling of physical systems enables comprehensive analysis and real-time forecasting of material flows, energy consumption and machine performance. The use of RAPIDZ increases production line efficiency, enhances flexibility and response time, and enables proactive maintenance to minimize downtime.

Collecting data from different sources on the shop floor and making it accessible to different IT systems is one of the core tasks during the process of factory digitization. Due to the different protocols and interfaces, the data collection task comes with unique challenges. With the Sensor Technology Adapter Gateway (STAG), we present a solution that closes the gap between the shop floor and the IT system’s backend. STAG is an industry-grade middleware that automates translations between data models and protocols.

In the era of Industry 4.0, many industrial companies are facing major transformations. In the process of digitalization, factory management is adopting new technologies such as cognitive assistance systems, which has led to changes in work processes. Regarding assembly in the metal and electrical industries, it is unclear to what extent this development has promoted work-integrated learning. Therefore, the topic of this paper is a qualitative analysis that explores employees' perceptions of the learning opportunities and risks presented by cognitive assistance systems. Results: Not all assembly employees benefit equally from these new developments.

The rapid development of generative artificial intelligence is opening up new avenues for the manufacturing industry amid a shortage of skilled workers. Large language models can potentially make production processes in medium- sized businesses more efficient. But how exactly is this potential measured? Key areas of application such as communication, training and knowledge management show why a lot depends on employee acceptance.