Industry 4.0

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
I4S 1/2026: Applied AI Ethics in the Workplace

I4S 1/2026: Applied AI Ethics in the Workplace

A shared responsibility — from radiology and speech therapy to assembly
AI ethics in the workplace is everyone’s responsibility. It requires accountability from companies as a whole and conscious action from individuals—whether developers or users, managers or employees. Key issues revolve around ethical AI skills and questions of governance and employee representation. How will the world of work change, from radiology and speech therapy to assembly and quality control?
Derivation of MTM Analyses from Motion Capture Data

Derivation of MTM Analyses from Motion Capture Data

Evaluation of the procedure and comparison with a manual MTM analysis
Silas Pöttker ORCID Icon, Maria Neumann ORCID Icon, Martin Benter, Constantin Eckart ORCID Icon, Ulrike Wolf ORCID Icon, Peter Kuhlang, Hermann Lödding ORCID Icon
For around 15 years, German labor productivity per working hour has been increasing at significantly less than 1% per year. At the same time, more detailed productivity analyses reveal high potential in companies. The issue is that the required MTM analyses are complex and not yet employed as broadly and frequently as would be necessary. One solution is the use of digital technologies such as motion capture. These make it possible to carry out productivity analyses with little effort, as they provide data that accelerates the analysis. The MTMmotion® tool from the MTM ASSOCIATION e. V. was developed with the aim of carrying out valid and compliant MTM analyses using data provided by other technologies. This article compares the method developed for a motion capture system and MTMmotion® with a conventional MTM-1® analysis. The main result is that digital technologies can be used to create valid MTM analyses in early planning phases with little effort in order to make early ...
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 112-119 | DOI 10.30844/I4SE.25.5.108
Enabler for the Digital Twin

Enabler for the Digital Twin

Requirements for Technical Documentation 4.0
Christian Koch, Lukas Schulte, René Wöstmann, Jochen Deuse ORCID Icon
The increasing heterogeneity and complexity of industrial plant components from different manufacturers make it difficult to handle technical documentation consistently. In addition, the flexibility required for system changes challenges the long-term usability and legally compliant design of this documentation throughout the entire life cycle of cyber-physical production systems. This article contributes to the discussion on Technical Documentation 4.0 by systematically analyzing existing specifications and approaches and by proposing a concept for a holistic documentation framework.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 76-85
I4S 4/2025: Smart Logistics

I4S 4/2025: Smart Logistics

Sustainable, resilient processes along the entire value chain
Logistics is entering a new era. Climate change and geopolitical uncertainties are shifting the focus to resilience and sustainability. The concept of smart logistics is gaining importance. But what exactly makes logistics smart, and how can it help us organize our societies and the economy? Approaches such as predictive analytics, demand analysis, and machine learning show why smart logistics is more than just a technological trend.
Technologies for Assisting Manual Order Picking

Technologies for Assisting Manual Order Picking

From conventional pick-by systems to AI-driven manual picking assistance
Md Khalid Siddiqui ORCID Icon, Jonathan Kressel ORCID Icon, Jürgen Grinninger
Manual picking remains common due to the high initial cost of support systems. This paper reviews existing technologies, presents an exploratory vision-based prototype, and examines existing literature that explores how combining object detection with language systems could enhance manual workflows. The findings suggest a promising, low-cost direction for worker support in logistics.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 6-19 | DOI 10.30844/I4SE.25.4.6
Digital Supply Chain Twin: The Pathway to Success

Digital Supply Chain Twin: The Pathway to Success

A catalyst for increasing competitiveness
Gökhan Cenk ORCID Icon, Jonas Andersson, Tobias Engel ORCID Icon
Companies face a variety of challenges when optimizing global supply chains. Economic interests must be balanced with legal requirements, such as the German Supply Chain Due Diligence Act (SCDDA) and the European Sustainability Reporting Standards (ESRS). A digital supply chain twin (DSCT) enables the visualization of value creation networks and supports key business functions, such as purchasing, supply chain management, distribution, service, and sales. By leveraging immersive technologies, the DSCT helps generate sustainable competitive advantages across the entire supply network.
Industry 4.0 Science | Volume 41 | 2025 | Edition 3 | Pages 52-60 | DOI 10.30844/I4SE.25.3.52
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