Industry 4.0

Industrial supply chains must repeatedly reconfigure sourcing strategies in response to disruptions, yet supplier capability information remains heterogeneous and difficult to operationalize. Existing research addresses supplier selection, simulation, and interoperability standards separately, treating discovery, evaluation, and optimization as disconnected steps requiring manual data transformation. This paper presents a framework for a data-driven industrial platform integrating three components: Asset Administration Shell-based supplier profiles structured by an Actor-Service ontology for semantic discovery, automatic discrete-event simulation model generation from layout data for performance evaluation, and KPI-driven configuration using optimization algorithms. The main contributions are an end-to-end interoperable workflow, a two-tier supplier profile concept separating semantic descriptions from simulation parameters, and a standardized KPI interface maintaining consistency ...

Long-term relationships are now insufficient to ensure trust in digitalized and dynamic industrial networks where sensitive data sharing is prevalent. While organizational trust mechanisms, such as contracts, remain significant, they are often slow and challenging to scale within evolving digital platforms. This article thus proposes privacy-enhancing technologies as a foundation for embedding technical and verifiable guarantees alongside organizational trust mechanisms. As a result, secure industrial collaboration can be achieved without the persistence of data silos.

Industrial platforms are fundamentally transforming the organization and structure of value creation. Beyond serving as intermediaries that connect machine manufacturers, suppliers, and service providers, they enable data-driven decision-making in production while fostering innovation in product development. In doing so, they create new opportunities for collaboration and value creation through enhanced connectivity and interaction across the platform ecosystem.

Industrial digital platforms are considered the driving force behind digital value creation, yet legally they largely operate in a gray area. This article examines what defines industrial digital platforms and what legal framework applies to them. Existing platform regulations apply only to a limited extent to industrial digital platforms, which is why these platforms often establish their own legal framework through their terms and conditions. The article also explores the implications of European regulation, power asymmetries, and the opportunities and limitations of private regulation.

While virtual reality can facilitate hands-on learning, its development faces barriers, including high costs and time demands and scalability challenges. This article presents two case studies that illustrate strategies for overcoming such barriers when training the next generation of skilled workers in environmental technologies. By examining approaches for streamlining development and increasing content relevance and scalability, we highlight lessons learned for future practice. We conclude by envisioning a future in which educational institutions can flexibly and cost-effectively prototype virtual reality in learning contexts, ensuring alignment with curricular goals and learners’ needs.

The rapid advancement of immersive technologies has created new opportunities to transform human-machine collaboration in industry. This paper presents an immersive platform with a digital twin that combines both physical and cognitive characteristics of human dynamics. By integrating multimodal sensing, human biomechanics, and cognitive state into digital twin technology, the proposed system enhances operational safety and ensures better ergonomics. The main argument is that human digital twins are not only desirable but essential for next-generation industrial systems. We discuss the limitations of existing human modeling approaches, outline the conceptual foundations of human digital twins, and demonstrate their industrial relevance across safety, productivity, ergonomics and sustainability.

In recent years, numerous maturity models have been developed with the aim of providing a clear indication of the progress each company has made in terms of Industry 4.0 development. However, not all models include all aspects of Industry 4.0. The models are also not equally practical. This article offers an in-depth comparison and assessment of the comprehensiveness of the ten most important Industry 4.0 maturity models.