Design

As environmental challenges such as climate change and resource scarcity intensify, with Earth Overshoot Day highlighting overconsumption, the circular economy emerges as a crucial solution. Legislation at the national and EU level obliges companies to become more sustainable. Simultaneously, the circular economy strengthens economic resilience, promotes innovation and creates competitive advantages. However, the impact on the labor market is controversial, as fewer primary resources and new products are needed. Sustainable corporate management requires a balanced consideration of the triple bottom line: Planet, People, and Profit, treating each as equally important. In contrast to the linear economy, the circular economy follows the ‘cradle to cradle’ principle and integrates the ten R-Strategies of sustainability. These strategies range from refuse (avoidance) and reduce (reduction) to recycling and repurpose (reuse). Companies should identify which strategies can be swiftly ...

Metaverse-based training programs offer a realistic and risk-free learning environment that is particularly valuable in industrial contexts, e.g. in immersive training and the simulation of workflows. Challenges remain in the areas of data protection, technological acceptance and integration into existing systems. Using a carefully crafted questionnaire, four expert interviews were conducted to investigate whether the metaverse can innovate training programs effectively and lastingly. Its standardized format yields comparable, reliable data while allowing for an accurate evaluation of the results.

Traditional budgeting often resembles a marathon full of spreadsheets, manual reconciliations and time-consuming data collection. However, modern companies need agile, data-driven solutions that allow for transparency, efficiency and strategic foresight. Digital technologies such as digital twins, dashboards and process mining initiate this possibility: they transform the budgeting process from a static set of figures to a dynamic, simulation-capable management tool. Instead of getting lost in detailed work, companies can use them to analyze processes in real time, simulate scenarios and make well-informed decisions.

Artificial intelligence, collaborative robotics, and virtual worlds, such as the metaverse, are fueling visions for new forms of industrial value creation. However, innovation alone is not enough—given that these technologies only develop their full potential through intelligent business models. How can companies efficiently integrate AI-supported automation, cobots and digital twins into their processes?

The AI-driven, self-learning digital twin continuously adapts to real system behavior, ensuring an optimal representation of the production process. A comprehensive semantic model serves as the foundation for advanced artificial intelligence (AI) approaches. Insights derived from AI methods are integrated into this model, enhancing the interpretability and explainability of AI systems. Techniques from the field of eXplainable AI (XAI) facilitate the automated description of AI models and their findings, as well as the development of self-explanatory models.

This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management.

Manufacturing companies incorporate reverse logistics as a building block of the circular economy for greater sustainability. Case studies show that this can result in strategic opportunities. This article summarizes an analysis of expert interviews on the increase in supply chain resilience attributed to reverse logistics. Potential benefits are highlighted, and companies are encouraged to examine the approach and implement innovative solutions. The result is a hypothesis-based value model that serves as an orientation aid for decision-makers.

In line with current digital transformations, the number of software applications in use by companies is continuously increasing. This particularly affects industrial enterprises, which face challenges due to their often complex business processes. A holistic and sustainable integration of these business processes requires a strong link between the different information systems used. In this context, application integration, also known as enterprise application integration (EAI), is becoming more important. Modern approaches such as the use of microservices offer a particularly flexible and efficient solution for seamlessly connecting different applications and thus promoting the agility and scalability of a company’s IT landscape.