Nachhaltigkeit

Machine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application.

In remanufacturing, used goods are brought up to the quality level of a new product. This distinguishes it from recycling, which recovers materials and converts them into new units or products. Various uncertainties, such as the condition, quantity and time of return, can be minimized with Industry 4.0 approaches and artificial intelligence. A special learning concept ensures that employees have the required competence profiles in this context.

The typical problems of a medium-sized company, coupled with the new requirements for sustainability, harbor the potential for economic tension. Learning factories can counteract this: they simulate production processes and offer an environment where participants can develop knowledge and skills in a realistic production setting. Establishing an innovation factory not only increases productivity, but also significantly reduces land consumption.

Platforms and business models based on them have been around for many years. However, the rapid development of information technology in recent years has greatly reduced the need for physical assets and infrastructure to enable value creation. Digitalization enables highly efficient interactions among platform participants. This lowers the barriers to accessing tangible and intangible goods, which in turn opens up opportunities to develop instruments that improve the sustainability of platform results.

The decentralized data ecosystem Gaia-X, which is currently under development, supports the future viability of the digital data economy in Europe. But how can relevant use cases be realized in Gaia-X from a service-oriented perspective? To answer this question, this article presents a methodology that describes a structured and interdisciplinary approach to service development in the ongoing Gaia-X 4 ROMS consortium research project [1]. In this project, federated services are realized in five processing steps on the basis of use cases. IT experts, software developers and industry users can leverage the model to efficiently coordinate the joint realization of use cases with Gaia-X and the goal of sovereign data exchange.

In additive manufacturing – which is also known as 3D printing – plastic waste is produced, for example in the form of required support structures or faulty prints. One option for resource recirculation in additive manufacturing is direct use in a pellet 3D printer that incorporates fused granulate fabrication (FGF). The elimination of the filament production process step reduces the manufacturing time and the energy required for recirculation.

This article describes how companies can accelerate the implementation of the United Nations Sustainable Development Goals (SDGs). As a guideline, we propose the concept of corporate purpose. By focusing on the added value generated for stakeholders, a corporate purpose has a meaningful effect and provides orientation. As part of our research, a self-assessment was developed, which makes the status quo of corporate purpose in companies measurable.