Open Access Articles

Due to the sharp rise in energy prices, compressed air, or compressor performance and its power consumption, are once again in the foreground as part of energy management. Due to the fact, that compressed air often escapes via leaks and these are difficult to localize or measure, a niche topic is becoming very noteworthy again. Automated leak detection can determine the need for repairs and help increase energy efficiency. It is important that not only leaks are detected, but that the extent of the leaks can also be quantified. A cost-effective method of detecting leaks is energy monitoring of the air compressor. Continuous monitoring and evaluation of the energy data means that you can react quickly.

The political and social request for environmentally compatible products is putting companies under increasing pressure. Small and medium-sized companies (SME) in particular have to quickly find or develop solutions to these demands. This paper presents a methodological approach that combines the proven strategies of agile development with the specific activities of so-called ecodesign. The methodology is first discussed theoretically and then experimentally evaluated and discussed by means of a case study in a real company context.

Climate change is one of the biggest challenges facing companies. To be able to define strategies for decarbonizing their business activities, companies need to start accounting for their emissions. Calculating Scope 3 emissions is a complex, resource- intensive, and often imprecise process for companies. By using digital technologies, Scope 3 carbon accounting can become more transparent, efficient, and secure. This article highlights the potential of digital technologies in Scope 3 carbon accounting.

In selective laser melting, metal powder is melted layer by layer and fused with the already manufactured part. Within this process, defective layers are created, which can be avoided. Such defects can only be detected by various compression and tensile strength experiments after printing is complete. This procedure is costly and inefficient. Therefore, the authors would like to present a demonstrator which, with the help of machine learning methods which draw from sensor-based data acquisition, is able to detect faulty layers during the manufacturing process itself and to support the machine supervisor with decision recommendations.

The use of artificial intelligence (AI) is becoming more important for a variety of industries, which is why enterprise resource planning (ERP) systems also offer many possible uses of AI. Due to their newly acquired, AI-based adaptability and learning abilities, modern AI-integrated ERP systems are able to develop competencies, plan processes, make forecasts and interact intelligently with humans. It is not uncommon for such systems to initiate major structural changes for companies and to open up new markets and design areas [1]. In order to measure the progress of an ERP system in terms of AI, the Center for Enterprise Research (CER) has developed an AI maturity model. Building on this model, a tool for evaluating AI integration in an ERP system should be able to showcase potential for development and enable market comparison.

A study by the German Academy of Science and Engineering (acatech) indicates that artificial intelligence (AI) is of growing importance for the success of manufacturing companies [1]. The emerging, data-driven solutions in the manufacturing field are highly diverse, both in terms of the processes and the locations (different factories, factory sub-areas, etc.) where these solutions are implemented. Often the solutions are also hardly scaled beyond the limits defined in the pilot project. When such an AI project ends, the goals of a use case are fulfilled, but this often results in another isolated solution being added to the company’s established IT system landscape. The data this solution delivers is not further used, and complex maintenance requirements negate any gains in efficiency.

Any networked information system that is used in the context of manufacturing and logistics in a factory can be referred to as factory software. This article describes six trends that will significantly influence the way software is used in factories in the near future. The trends are described in ascending order in terms of significance of impact.

Robotic Process Automation (RPA) stands for the software-supported operation of software solutions via their user interface. The primary goal that RPA seeks to achieve is the automated execution of routine tasks that previously required human intervention. However, the potential of RPA to improve processes in the long term is very limited. Automating processes and bridging front-end media disruptions leads to a variety of dependencies and conditions, which are summarized in this article. The path to a sustainable enterprise architecture (and the processes and systems comprised therein) requires open, adaptive systems with modern architecture that are characterized by a high degree of interoperability at various levels.

The COVID-19 crisis had an unmistakable impact on the procurement situation in global supply chains, to which companies had to adapt quickly. The effects make it clear that to reduce risks, companies must address the structure and transparency of supply chains. The following article examines what knowledge the actors have and how digitalization can lead to further improvement. The results show that companies currently have little supply chain knowledge beyond their direct suppliers, but are increasingly able to obtain the supply chain data they require. At the same time, the results indicate that there is still potential to increase transparency and the use of data.

Within the concepts of Industry 4.0, the term “Smart Factory” stands for the creation of effective production environments through digitalization and cyber-physical systems. Most manufacturers plan to make their manufacturing systems more automated, flexible and adaptive. In the course of these efforts, intelligent materials are increasingly brought into focus. Combined actuator and sensory properties enable the construction of lightweight and compact multifunctional actuator-sensor systems that are operated in an energy-efficient, noise-free and emission-free manner. This makes them appropriate for building networked systems. Shape memory alloys (SMAs) and dielectric elastomers (DEs) are particularly suitable for building intelligent actuators, and are presented in this article alongside several use cases.