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.

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.

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.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.

In order to exploit the full potential of the circular economy, companies need to develop holistic solutions and rethink their business models. With this in mind, a summer school entitled Circular Economy was organized by Merseburg University's start-up service in September 2023 with the aim of developing innovative ideas for new business models with a focus on circular models. Current trends in materials science were used as conceivable scenarios for circular forms of economic activity in the idea generation process. Entrepreneurship is an interface function here. Entrepreneurs not only want to run companies, they also want to bring about change with new products, services and business processes.