Industrie 4.0

Data-Driven Supplier Selection in Industry 4.0

Data-Driven Supplier Selection in Industry 4.0

Towards an industrial platform for selection and configuration
Purushothaman Ganesh ORCID Icon, Baris E. Albayrak ORCID Icon, Jörg Franke ORCID Icon, Till Sindel ORCID Icon, Jens Fürst, Sebastian Lang ORCID Icon
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 ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 50-61 | DOI 10.30844/I4SE.26.4.6
Replacing Organizational Trust with Technical Guarantees

Replacing Organizational Trust with Technical Guarantees

Privacy-enhancing technologies for dynamic industrial value networks
Hajeong Jeon ORCID Icon, Johannes Lohmöller ORCID Icon, Klaus Wehrle ORCID Icon, Jan Pennekamp ORCID Icon
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.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 72-80 | DOI 10.30844/I4SE.26.4.8
From Private Law to Private Ordering

From Private Law to Private Ordering

Rule-making through industrial digital platforms
Pia C. Romeike, Frederik Bär
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.
Industry 4.0 Science | Volume 42 | 2026 | Edition 4 | Pages 98-105 | DOI 10.30844/I4SD.26.4.10
Industry 4.0—Progress and Digitalization in Limbo

Industry 4.0—Progress and Digitalization in Limbo

Status of sustainable transformation and digitalization in production engineering
Christian Donhauser ORCID Icon, Daniel Riepl
Digitalization projects help users represent complex processes more simply and efficiently. However, there are many obstacles to implementation. Reluctance to implement these projects is palpable. This affects, among others, employers and employees, who may fall behind economically by waiting or avoiding change. These observations can be traced back to an overarching research question: What barriers and systemic challenges hinder sustainable transformation within the context of Industry 4.0, particularly when considering human labor in production engineering? What questions are the affected stakeholders asking? The primary goal of this long-term research project is to define these questions decisively and in detail in order to develop a conceptual foundation that integrates research, teaching, and technological development and thus combines the potential of digital technologies with the experiential and practical knowledge of production workers.
Industry 4.0 Science | Volume 42 | 2026 | Edition 3 | Pages 56-60
Digital Factory Planning for Startups

Digital Factory Planning for Startups

A simulation-based production structure design
Herwig Winkler ORCID Icon, Tobias Isau
With the increasing complexity of production and logistics systems, traditional factory planning approaches are reaching their limits. In this context, digital factory planning offers a promising solution for enabling well-informed decisions, particularly during the early planning phases. For startups, the optimal planning of a production facility is challenging, as they often operate with limited financial and infrastructural resources. This paper presents a methodological approach to digital factory planning that utilizes VR simulation for the layout planning of a factory hall for a young company in the solar industry. The proposed approach demonstrates how simulations can support the design of flexible production structures, particularly in startup environments.
Industry 4.0 Science | Volume 42 | 2026 | Edition 3 | Pages 68-75
Developing Virtual Reality in Learning Contexts

Developing Virtual Reality in Learning Contexts

Navigating efficiency, content relevance and scalability
Stella Kanatouri ORCID Icon, Oliver Sosna ORCID Icon, Alexander Kulik, Sina C. Truckenbrodt ORCID Icon, Friederike Klan ORCID Icon, Christian Erfurth ORCID Icon
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.
Industry 4.0 Science | Volume 42 | Edition 3 | Pages 26-34 | DOI 10.30844/I4SE.26.3.3
I4S 2/2026: Learning Factories

I4S 2/2026: Learning Factories

Drivers of research and learning environments for Industry 4.0
In recent years, learning factories have evolved into key experimental environments in the context of the Fourth Industrial Revolution. In addition to their role as training centers for skilled workers, they also serve as real-world research laboratories. This issue of Industry 4.0 Science examines learning factories as venues for exploring new approaches and technologies—whether digital assistants, cobots, serious games, or digital twins.
Learning Factories for the Future of Manufacturing in Brazil

Learning Factories for the Future of Manufacturing in Brazil

Advancing manufacturing through technology and skills development
Manufacturing firms in developing countries face challenges in closing productivity gaps while adopting Industry 4.0 technologies. Learning factories are one helpful approach to countering these challenges. One such example is the learning factory Fábrica do Futuroin São Paulo, Brazil, which has engaged students, supported competence development, and collaborated with industry in applied research, functioning as a hub for advanced manufacturing initiatives.
From Brownfield to Industry 4.0

From Brownfield to Industry 4.0

Learning factories as training and testing environment for digital transformation
Jakob Weber, Sven Völker ORCID Icon
To succeed in their digital transformation, manufacturing companies need engineers with in-depth knowledge of key technologies and concepts, and a profound understanding of the transition from Industry 3.0 to Industry 4.0. This article describes the concept of a learning factory that is continuously subjected to a digital transformation, thereby creating an environment for the development of transformation competencies. The concept of digital transformation is based on digital worker assistance systems and multi-agent systems for production control. These enable the incremental integration of existing resources into the digitalized factory. The learning factory is not presented to students as a completed solution. Instead, it is continuously developed further as part of student projects. This way, it contributes directly to the qualification of personnel for the implementation of Industry 4.0.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 88-96
Building the Future Workforce Today

Building the Future Workforce Today

Trendiation as a strategic framework for employee qualification and training
Jürgen Fritz, Sebastian Busse, Ingo Dieckmann, Torsten Laub
As Industry 4.0 and artificial intelligence reshape organizational capabilities, traditional training systems struggle to keep pace with evolving skill requirements. This paper introduces Trendiation—a structured methodology for translating emerging trends into actionable strategies—as a systematic approach to this challenge. Through a workshop-based application examining Edutainment, Human-Centered Design, and Workforce Transformation, we demonstrate how organizations can move from abstract trend identification to concrete qualification requirements and prioritized training initiatives. The method produces a traceable artifact chain spanning trend framing, capability-gap assessment, and implementation roadmaps. Participant evaluations indicate high perceived clarity and practical utility. By bridging foresight analysis with participatory design, Trendiation enables organizations to proactively cultivate adaptive capabilities and build learning cultures aligned with future work ...
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 22-29 | DOI 10.30844/I4SE.26.2.22
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