Autor: Dominik Augenstein

Production in space, of semiconductors for example, offers many advantages for companies. At the same time, high transport costs mean that careful consideration must be given to the production materials being transported. The use of Kalman filters enables (real-time) control from Earth, making space production a cost-efficient option. Machine learning could make it a viable approach even for highly complex production systems.

Software development is fundamental to digital transformation. A good foundation of data is required for developers to tailor software to the needs of the commissioning department. Unfortunately, the data models required for this are incomplete, often created unilaterally by the development department and not embedded in the business context. This makes it difficult for both developers and AI to find the right algorithms. The present approach increases understanding and exchange between the specialist and development departments and offers digital assistance with data modeling as a basis for software development. Furthermore, AI approaches can help to increase the quality and completeness of the data.

Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their ‘simplicity’, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution—without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers.

The constant advance of digitalization confronts companies with new challenges and opportunities. Immediate data processing is now ubiquitous and the advantages are obvious. However, broadband coverage in Germany is insufficient, which makes it difficult to improve processes. Mathematical approaches and machine learning enable timely optimization and smooth production.

Through disruptive changes and an increasing globalization, companies have to rethink their traditional working models. Thereby, the digital transformation seems to provide a solid answer to this challenge and enables a rapid adaption to the new circumstances. Humans are of central importance to respond to these changing demands. A challenge hereby is, that humans have to be embedded in such a flexible working environment considering, that the competence profile is changing rapidly. In order to solve these challenges, a competence profile is provided. Furthermore, it is shown that a working model for the digital transformation enables a company to respond quickly and flexibly to new environmental conditions.

Industry 4.0 forces companies to keep their business models up-to-date to keep their competitiveness and to satisfy customer demands. Key words like “individual production” and “lot size one” put pressure on companies to adapt their current production to the new requirements. Especially for companies with a mass production this sounds like a 180° turn. Furthermore, no best-practices for introducing industry 4.0 exist and therefore, one cannot rely on such hold points. Nevertheless, a structured transformation of the own business model towards industry is not impossible.