digital transformation

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
AI Colleagues?

AI Colleagues?

Competence requirements and training for AI use in industry
Swetlana Franken ORCID Icon
Artificial intelligence is fundamentally changing tasks, roles, and skills in (industrial) companies. Increasingly, it acts as a colleague, preparing decisions, supporting processes, and interacting with people. This article highlights key competence requirements for AI use in industry, presents an integrated competence model, and outlines practical strategies for the transfer of skills. The aim is to prepare companies and employees for humane, competence-oriented AI implementation that combines technological efficiency with human creativity and judgment.
Industry 4.0 Science | Volume 42 | 2026 | Edition 2 | Pages 78-86
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
Digital Transformation in Educational Institutions: Scrutinizing the Debate and Highlighting Success Factors: A qualitative study on the current use of learning management systems and learning analytics in Germany

Digital Transformation in Educational Institutions: Scrutinizing the Debate and Highlighting Success Factors: A qualitative study on the current use of learning management systems and learning analytics in Germany

Linda Mai, Lynn Schmodde, Marius C. Wehner
In German education, digitalization is evolving, and the COVID-19 pandemic has accelerated this process due to closed schools and universities. While these external conditions were almost the same for all institutions, they have dealt with remote teaching in different ways and developed various new learning concepts. We conducted 27 interviews with headmasters, e-learning managers, and teachers from Germany to gain insights into varying attitudes towards learning management systems, as well as the motivations and concerns about future teaching with digital support. Using an inductive method, we identify different obstacles, including lack of digital competencies, necessity of technical equipment, concerns about replacing traditional lessons, but also enthusiasm for new opportunities. We argue that a meaningful use of learning management systems and learning analytics depends on an open and impartial debate on data protection and equal opportunities. Overall, this paper contributes to ...
Industry 4.0 Science | 2022 | | DOI 10.30844/AKWI_2022_08
Applied Knowledge and Augmented Reality

Applied Knowledge and Augmented Reality

Bridging the gap between learning and application
Jana Gonnermann-Müller ORCID Icon, Philip Wotschack, Martin Krzywdzinski ORCID Icon, Norbert Gronau ORCID Icon
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 ...
Industry 4.0 Science | Volume 41 | 2025 | Edition 5 | Pages 22-29 | DOI 10.30844/I4SE.25.5.22
Field Meets Code

Field Meets Code

Artificial intelligence for better collaboration in software development
Andreas Groche, Dominik Augenstein
Software development is fundamental to digital transformation. A good foundation of data is required for developers to tailor software to the needs of the commissioning department. Unfortunately, the data models required for this are incomplete, often created unilaterally by the development department and not embedded in the business context. This makes it difficult for both developers and AI to find the right algorithms. The present approach increases understanding and exchange between the specialist and development departments and offers digital assistance with data modeling as a basis for software development. Furthermore, AI approaches can help to increase the quality and completeness of the data.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 104-110
Intelligent Load Carrier Management

Intelligent Load Carrier Management

AI-supported monitoring and reduction of losses in logistics
Dominik Augenstein, Lea Basler
Load carriers are essential for transporting manufactured parts in manufacturing companies. Despite their ‘simplicity’, they are usually expensive to purchase as they are manufactured expressly to fit purpose. While tracking methods such as GPS tracking can be used to prevent the loss of load carriers, this is associated with monitoring costs and presents challenges with regard to data protection as soon as the work performance of intralogistics employees is monitored. Assigning load carriers to designated clusters and monitoring these clusters provides an effective solution—without drawing conclusions about employee performance. Furthermore, artificial intelligence can optimize this approach whilst also deterring the theft of load carriers.
Industry 4.0 Science | Volume 41 | 2025 | Edition 2 | Pages 78-84
I4S 2/2025: The Future of Production with AI, Cobots and Virtual Worlds

I4S 2/2025: The Future of Production with AI, Cobots and Virtual Worlds

Technology needs innovative, value-adding business models
Artificial intelligence, collaborative robotics, and virtual worlds, such as the metaverse, are fueling visions for new forms of industrial value creation. However, innovation alone is not enough—given that these technologies only develop their full potential through intelligent business models. How can companies efficiently integrate AI-supported automation, cobots and digital twins into their processes?
Work-Integrated Learning in Industry 4.0

Work-Integrated Learning in Industry 4.0

A qualitative analysis of various assistance systems in assembly
Kathleen Warnhoff ORCID Icon
In the era of Industry 4.0, many industrial companies are facing major transformations. In the process of digitalization, factory management is adopting new technologies such as cognitive assistance systems, which has led to changes in work processes. Regarding assembly in the metal and electrical industries, it is unclear to what extent this development has promoted work-integrated learning. Therefore, the topic of this paper is a qualitative analysis that explores employees' perceptions of the learning opportunities and risks presented by cognitive assistance systems. Results: Not all assembly employees benefit equally from these new developments.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 20-29 | DOI 10.30844/I4SE.25.2.20
I4S 1/2025: 40 Years of Digital Transformation in Manufacturing

I4S 1/2025: 40 Years of Digital Transformation in Manufacturing

Key research questions for tomorrow's production and logistics
Digital transformation has been a central focus of scientific discussions for years. Questions relating to data-driven decisions, artificial intelligence and resilient supply chains are at the heart of current research. The articles in this issue explain key trends and present scientific findings and practical solutions - from automation and the circular economy to cloud computing.
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