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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.

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.

Resource-efficient businesses have become increasingly important for companies in recent years. Although this brings new potentials, the practical implementation in the form of suitable business models is accompanied by challenges. In this paper, we will examine which concepts and methods already exist for the development of circular and resource- efficient business models and show approaches and solutions from science and practice using the example of interdisciplinary research projects.

How can organisations successfully participate in interoperable and decentralised data ecosystems? To answer this question, this paper presents a process model using the transport logistics industry as an example, which methodically describes the collaborative and interdisciplinary development of services in the decentralised federated data ecosystem Gaia-X [6]. The model supports evaluation and decision-making processes within the development of decentralised data ecosystems in practice and helps IT decision-makers and participating stakeholders to identify the relevant communication flows in a use case. It can be used independently for specific use cases, data spaces and connector technologies and ensures that the communication and alignment of individual development statuses in a decentrally organised framework is comprehensible and understandable for the overall context.