Branche: Manufacturing

Sustainability-enhancing transformation processes are necessary in all sectors if we are to remain within planetary boundaries. This also applies to the industrial sector as a significant emitter of greenhouse gases. Employees need new competencies to master this complex task of industrial transformation. These range from CO2 equivalents accounting to the development and evaluation of transformation scenarios, including technical measures. The learning module developed here addresses these competency requirements and uses the example of the ETA factory to show how a competency-oriented learning module for industrial transformation can be structured. It essentially comprises four phases: data collection and CO2 equivalents accounting, cause analysis, development of measures and evaluation of measures.

Artificial intelligence (AI) can enhance smart assembly by predicting human motion and adapting workplace design. Using probabilistic models such as Gaussian Mixture Models (GMMs), AI systems anticipate operator actions to improve coordination with robots. However, these predictive systems raise ethical concerns related to safety, fairness, and privacy under the EU AI Act, which classifies them as high-risk. This paper presents a conceptual method integrating probabilistic motion modeling with ethical evaluation via Z-Inspection®. An industrial case study using the Smart Work Assistant (SWA) demonstrates how multimodal sensing (motion, gaze) and interpretable models enable anticipatory assistance. The approach moves from ethics evaluation to ethics-informed work design, yielding transferable principles and a configurable assessment matrix that supports compliance-by-design in collaborative assembly.

The introduction of artificial intelligence (AI) in companies poses new challenges for regulation and co-determination. Binding requirements have been in force since the 2025 EU AI Act, which must be linked nationally with the Works Constitution Act (BetrVG). The regional competence center humAine has developed a model works agreement on AI (MBV KI) in accordance with Section 77 BetrVG, which strengthens co-determination rights in companies and implements European regulations in a practical way. Flanked by co-determination dialogues, the MBV KI enables company-specific adaptation for responsible and human-centered AI use. Using selected parts of the MBV KI as examples, this article shows how a framework works agreement on AI can be designed and discusses its transferability to companies without a works council. The MBV KI presented here contributes to the sustainable, socially secure design of the digital transformation.

The increasing complexity of industrial environments demands new competencies from workers, particularly the ability to interact with advanced digital systems. Traditional training methods often fall short in supporting the effective transfer of applied knowledge to such contexts, and the effectiveness of this transfer, as measured by performance-based outcomes, remains to be investigated. To address this gap, the present study employed a between-subjects experimental design comparing augmented reality- and paper-based instructions within a realistic production training scenario. The results show that participants who learned with augmented reality completed the production process significantly faster and with fewer errors than those using paper instructions. In addition, learners using augmented reality reported higher usability and experienced lower cognitive load during training. These findings suggest that augmented reality can enhance the transfer of practical skills in industrial ...

Traditional quality control often falls short in high-mix, low-volume production environments due to variability and complexity. This project introduces an advanced workstation to boost industrial productivity and quality, developed with Axiomatic Design to ensure a clear link between customer needs, functional requirements, and design solutions. Combining polarization cameras, high-resolution imaging, adaptive lighting, and deep learning-based computer vision, the system performs high-accuracy inspection on quantity, quality, and compliance. A digital assistance system offers real-time feedback via an intuitive interface. Validation in a controlled environment confirmed both the system’s practical benefits and its scalability.

Compared to traditional AI systems, generative artificial intelligence (GenAI) introduces user-dependent characteristics that create unique challenges for AI governance in organizations. These challenges are particularly tied to human factors, such as employee attitude, awareness, and skills, which are often neglected by existing governance frameworks. This qualitative case study examines how a manufacturing organization implemented GenAI governance mechanisms to foster the responsible use of this technology. The findings reveal that organizations should adopt a holistic approach, combining structural, procedural, and relational mechanisms to address employee-related aspects of GenAI governance. As a result, this study contributes to the growing field of GenAI governance and provides practical insights for its responsible use in organizations.