Resource Efficiency

Technical systems are characterized by increasing interdisciplinarity, complexity and networking. A product and its corresponding production systems require interdisciplinary multi-objective optimization. Sustainability and recyclability demands increase said complexity. The efficiency of previously established engineering methods is reaching its limits, which can only be overcome by systematic integration of extreme data. The aim of "hybrid decision support" is as follows: Data science and artificial intelligence should be used to supplement human capabilities in conjunction with existing heuristics, methods, modeling and simulation to increase the efficiency of product creation.

Reducing CO2 emissions is one of the most urgent tasks of our time. Simultaneously, artificial intelligence is developing rapidly. However, AI often brings about its own significant CO2 impact. Experimental testing of Green AI strategies is therefore crucial for their long-term success. A management tool can support this process so that both users and managers can make optimal use of AI as a tool.

EU regulations demand more intensive and transparent sustainable practices from companies. Industry needs to adapt many processes and products to take charge of this responsibility. Artificial Intelligence (AI) in particular offers innovative potential. Firstly, however, this technology needs to be evaluated focusing on weak AI—market-ready systems that perform specific tasks using algorithms and data-supported models efficiently.

The decarbonization of the energy sector is crucial for a climate-neutral EU, as a large proportion of greenhouse gas emissions come from energy use. Especially the wind energy sector, with its high material costs, faces major challenges. The rapid expansion of wind energy requires innovative solutions to establish sustainable End-of-Life (EoL) management practices. A digital decision-making framework for sustainable EoL strategies is therefore extremely useful.

The transformation in production offers the chance to redesign existing value chains. Cooperation between various ecological, social and governmental stakeholders is seen as particularly key to sustainable development. However, little research has been conducted into how companies can best manage the resulting interdependencies. Agriculture is used as an example to examine how businesses can activate resources along the value chain.

The EU Strategy for sustainable and circular textiles aims to reduce the industry’s environmental impact while at the same time increasing its competitiveness. In this transition towards circularity, firms in the highly fragmented textile value chains need solutions that help overcome barriers and provide support. This paper presents digital solutions that are particularly suited for SMEs and that have been developed with public funding. It aims at encouraging SMEs, not only from the textile industry, to specify their individual transition paths towards circularity and to use digitalization to foster implementation.

Although the concept of double transformation is being intensely discussed in companies, the practical implementation in operational structures often remains unclear. This article sheds light on how digital technologies and environmental sustainability strategies can be developed either synergistically, antagonistically or independently of each other. In addition, it discusses the different experiences of employees in different industries and the varying progress in the introduction of digital and ecological measures. To this end, it will discuss existing research findings and practical examples that pave the way for the successful integration of both transformation processes in companies.

A decisive competitive factor for smaller and medium-sized manufacturers of one-of-a-kind and small batches is their products’ timely completion, delivery and commissioning. Precise logistics planning is just as important as production control. However, the processes are often characterized by uncertainties, e.g. due to local conditions at the customer or cooperation with suppliers. Digital shadows for data evaluation in real time offer a convincing solution.

Bedingt durch globale Entwicklungen hinsichtlich der Preise und Versorgungssicherheit im Energiesektor stehen besonders energieintensive Unternehmen vor großen Herausforderungen. Zusätzlich fordern Kunden mehr Informationen über Energiekennzahlen und CO₂-Emissionen sowie ressourcenschonendere Prozesse. Mit einer energiedatenbasierten Simulationsmethode werden die Ressourceninformationen direkt aus dem Energiedatenmanagementsystem (EDMS) extrahiert und weiterverarbeitet. Hierbei werden sowohl aktuelle als auch stetig aktualisierte historische Daten verwendet, die automatisiert abgeglichen werden. Die digitale Abbildung der vorhandenen Prozesse ist lediglich auf Seiten der Energiedaten notwendig, ohne die technischen Prozesse in ihrer Gänze analysieren zu müssen. Mit dem so erstellten energetischen digitalen Schatten lassen sich Energiebedarfe für bevorstehende Produktionen und Produkte simulieren und können durch automatisierte Vorschläge in der Produktionsplanung positiv ...