Market Survey of Consulting Firms Specialized in Factory Planning

Artificial intelligence is increasingly shaping continuing vocational education and training by enabling the personalization of individual learning experiences. At the same time, audio-based learning formats are attracting growing interest among learners because of their flexibility and suitability for workplace learning. However, a conceptual framework for AI-supported audio learning, as well as the potential of combining artificial intelligence with audio-based learning, has received little attention to date. This paper therefore presents a conceptual perspective on the design possibilities and educational potential of AI-supported audio learning formats.

Generative Artificial Intelligence (GenAI) offers new opportunities for organizational knowledge management, particularly when it comes to learning processes at the interface between explicit, firm-specific, and tacit knowledge. Its use is therefore of particular interest for application areas such as industrial maintenance. Based on a mechanical engineering case study, this article demonstrates that augmenting both processes and employees with GenAI requires AI literacy combined with professional skills.

Vertical factories reduce land use and harness the advantages of urban production. However, their multi-story structure significantly increases planning complexity. To counter such effects, this paper presents a typology that combines proven solutions for recurring challenges in the planning and operation of vertical factories. Relevant characteristics were examined using real-world case studies and structured within a morphological framework. This framework was consolidated into factory types that provide guidance for conceptual planning and serve as the foundation for intelligent planning support.

Artificial intelligence (AI) offers a wide range of possibilities in industrial production, but it also presents challenges regarding employee acceptance. AI demonstrators are therefore of central importance, as they enable hands-on experience with AI. However, there has been a lack of systematically identified requirements for demonstrators that specifically promote acceptance and address negative emotions. Using a multi-stage research design, 69 requirements were identified, structured into functional requirements, quality requirements, and boundary conditions.

Monitoring construction progress, as required in the one-off production of large structures, is very time- and labor-intensive due to a high level of complexity and individuality. The goal of this article is to develop a sensor-based approach for capturing and evaluating multiple inspection characteristics. The use of machine learning models to detect objects and derive relevant information forms the basis for linking current condition to construction schedule. This enables a significant increase in efficiency during construction progress monitoring and a well-founded assessment of progress.

Companies are increasingly challenged to address shortages of skilled workers while meeting rising demands for productivity, flexibility, and innovation. Because labor supply can only be expanded to a limited extent, there is a growing focus on designing work systems with productivity in mind. Autonomous systems offer significant potential in this regard. Their implementation requires not only technical adjustments but, above all, changes in organization, skills, and work design. This article analyzes empirically grounded change requirements in existing work systems as well as associated economic potential.