Design

Collaborative Drone Inspection

Collaborative Drone Inspection

A new approach to inspection work with AI support
Till Becker
Drone technology and the use of artificial intelligence (AI) offer promising advantages in various sectors, including in inspection. The use of innovative inspection technologies can make inspections more efficient overall. This research project examines various legal and economic aspects of AI-based autonomous drone inspections. It also develops a target process that represents the use of an AI-based drone inspection and controls the use of such inspection technology. In particular, this article focuses on a collaborative approach to this new inspection methodology.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 94-100
Functional Safety and Cyber Security in the Process Industry

Functional Safety and Cyber Security in the Process Industry

A tension between stability and agility
Thimmo Kugele, Claudia Nowak, Arno Götz, Alexander Lawall ORCID Icon
Functional safety (safety) and cyber security (security) are key aspects of modern industry and technology. Safety aims to minimize risks posed by system malfunctions. This includes measures to protect people and the environment from failures and errors within systems. Security focuses on protecting systems and networks from digital attacks. The primary security objectives include ensuring confidentiality, integrity, and availability. A joint consideration of safety and security is essential for the future of the process industry, as both the physical safety and digital integrity of modern systems must be ensured. In industrial practice, this creates a field of tension: measures to enhance security can negatively impact safety and vice versa. This article analyzes relevant standards and regulations, presents key approaches for the integrated consideration of safety and security, and highlights areas of further research.
Industry 4.0 Science | Volume 41 | 2025 | Edition 2 | Pages 87-93
Why Moving Toward a Circular Economy Is Crucial

Why Moving Toward a Circular Economy Is Crucial

The ten R-Strategies of sustainable management
As environmental challenges such as climate change and resource scarcity intensify, with Earth Overshoot Day highlighting overconsumption, the circular economy emerges as a crucial solution. Legislation at the national and EU level obliges companies to become more sustainable. Simultaneously, the circular economy strengthens economic resilience, promotes innovation and creates competitive advantages. However, the impact on the labor market is controversial, as fewer primary resources and new products are needed. Sustainable corporate management requires a balanced consideration of the triple bottom line: Planet, People, and Profit, treating each as equally important. In contrast to the linear economy, the circular economy follows the ‘cradle to cradle’ principle and integrates the ten R-Strategies of sustainability. These strategies range from refuse (avoidance) and reduce (reduction) to recycling and repurpose (reuse). Companies should identify which strategies can be swiftly ...
Industry 4.0 Science | Volume 41 | 2025 | Edition 2 | Pages 68-76
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?
Digital Twins Using Semantic Modeling and AI

Digital Twins Using Semantic Modeling and AI

Self-learning development and simulation of industrial production facilities
Wolfram Höpken ORCID Icon, Ralf Stetter ORCID Icon, Markus Pfeil ORCID Icon, Thomas Bayer ORCID Icon, Bernd Michelberger, Markus Till, Timo Schuchter, Alexander Lohr
The AI-driven, self-learning digital twin continuously adapts to real system behavior, ensuring an optimal representation of the production process. A comprehensive semantic model serves as the foundation for advanced artificial intelligence (AI) approaches. Insights derived from AI methods are integrated into this model, enhancing the interpretability and explainability of AI systems. Techniques from the field of eXplainable AI (XAI) facilitate the automated description of AI models and their findings, as well as the development of self-explanatory models.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 30-36
Error Management in Production

Error Management in Production

Current situation and challenges in the industry
Johannes Prior ORCID Icon, Milan Brisse ORCID Icon, Nikita Govorov, Robert Egel ORCID Icon, Bernd Kuhlenkötter ORCID Icon
This study explores experience-based error management on the basis of 23 participating companies. This study aims to identify essential criteria for effective error management in production. For this purpose, a comprehensive questionnaire was created, featuring 77 questions across eight key topics, including error culture, documentation, root cause analysis and software-supported knowledge management. The following analysis highlights both positive and negative measures, providing specific recommendations to optimize experience-based error management.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 38-45
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
Circular Economy Enabled by Digitization

Circular Economy Enabled by Digitization

Digital networking in the procurement of manufacturing companies
Pius Finkel ORCID Icon, Peter Wurster ORCID Icon, David Pfister
Current developments in digitalization and data economy, especially multilateral data sharing platforms, offer the potential to accelerate the implementation of circular economy practices in the manufacturing industry. This article systematically examines the extent to which digitalization could serve as a catalyst for circular economy in the procurement of such companies. As a basis for the following research, eight experts from five leading global manufacturers and suppliers in the automotive and aviation industries were interviewed. This article demonstrates practical hypotheses for the sustainable design of supply chains and proposes two specific use cases for circular economy practices that can proactively counteract the use of resources.
Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 26-33 | DOI 10.30844/I4SE.25.1.26
Increasing Supply Chain Resilience with Reverse Logistics

Increasing Supply Chain Resilience with Reverse Logistics

Hypotheses for a value model
Jürgen Hamann ORCID Icon, Christoph Wenig ORCID Icon
Manufacturing companies incorporate reverse logistics as a building block of the circular economy for greater sustainability. Case studies show that this can result in strategic opportunities. This article summarizes an analysis of expert interviews on the increase in supply chain resilience attributed to reverse logistics. Potential benefits are highlighted, and companies are encouraged to examine the approach and implement innovative solutions. The result is a hypothesis-based value model that serves as an orientation aid for decision-makers.
Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 34-40
Distributed Application Integration in Industry

Distributed Application Integration in Industry

Employing microservices for enterprise application integration (EAI)
Jan-Peer Rudolph ORCID Icon
In line with current digital transformations, the number of software applications in use by companies is continuously increasing. This particularly affects industrial enterprises, which face challenges due to their often complex business processes. A holistic and sustainable integration of these business processes requires a strong link between the different information systems used. In this context, application integration, also known as enterprise application integration (EAI), is becoming more important. Modern approaches such as the use of microservices offer a particularly flexible and efficient solution for seamlessly connecting different applications and thus promoting the agility and scalability of a company’s IT landscape.
Industry 4.0 Science | Volume 41 | 2025 | Edition 1 | Pages 74-80
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