Max-Ferdinand Stroh
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Originating in 2011, Industry 4.0 describes the digital revolution of industry and has since become a collective term for smart, mutable and data driven factories. During the last decade systemic and methodical solutions were designed and implemented that enable corresponding data driven use cases for producers. Today's system providers offer complex data ecosystems in which data-driven use cases are built-in and implementers offer focused digitalisation projects to rapidly address quick wins. While an assessment of expectations around Industry 4.0 results in requirements within the domains of modifiability, connectivity, data and organisation for an IT-architecture, many such solutions are found to be violating essential requirements as systemic flexibility and data-availability. Not only is this a relevant matter for architectural purists, but it highlights real problems that industry is still facing while applying digitalisation measures in pursuit of Industry 4.0. While event-driven architectures go back to the design of modern operating systems, the emergence of powerful, resilient and cheap broker-technologies has risen the polarity of event-driven IT-architectures for businesses in the last decade. Although its occurrence is predominantly represented in ecommerce, finance and insurance, many prominent manufactures have since begun their transformation into an event-driven IT-architecture. Reasons for this architectural adaptation include exceptional data availability, resilience, scalability and especially data sovereignty. An assessment of event-driven IT-architecture's properties and implications reveals an excellent fit for the architectural requirements of Industry 4.0. In this work the subject of Industry 4.0 is analysed along literature to derive a collective understanding of expectations from a factory implementing Industry 4.0. Subsequently, IT-architectural requirements are derived that describe an architecture capable of satisfying these expectations. Then event-driven IT-architectures are analysed regarding their structural composition and capabilities. Finally, the fit of event-driven IT-architecture is evaluated against the architectural requirements of Industry 4.0, discussing congruence and divergence.
Der Rückbau kerntechnischer Anlagen stellt eine langfristige und komplexe Aufgabe dar, an der zahlreiche Stakeholder beteiligt sind, darunter Behörden, Betreiber von Kernkraftwerken, Sachverständige sowie weitere Akteure aus der Industrie. In Deutschland sind laut dem Bundesamt für Strahlenschutz derzeit etwa 424.000 Personen beruflich strahlenschutzüberwacht, wobei hiervon ca. 68.000 Personen
in Bereichen Forschung und Lehre (17.000), Industrie (34.000) und Kerntechnik (17.000) beschäftigt sind. Ein erheblicher Teil der Belegschaft wird in den kommenden Jahren in den Ruhestand gehen.
Climate change is leading to massive changes, especially in the areas of energy and mobility. As a connecting element of the energy and mobility transition, electricity grids will play a key role. Bidirectional energy flows and massive fluctuation in generation and consumption patterns lead to high stresses on components and systems, especially in the distribution grid. This confronts Distribution System Operators (DSOs) with new challenges to continue to ensure security of supply in an economical and resource-efficient manner. New maintenance strategies can enable operators to address these challenges. Novel sensors and artificial intelligence enable the technical use of methods such as Predictive Maintenance to detect and predict the probability of failure of critical components based on current condition data. Whereas Predictive Maintenance is already being used in many areas of the manufacturing industry today, the procedures are still new in operating medium-voltage switchgear in the distribution grid, which are critical for ensuring the security of supply. Today's maintenance processes are not automated and are based on Preventive Maintenance strategies and differ very much from those of production environments. For example, the used IT-systems differ as well as the level of involvement of service contractors and regulative requirements and limitations. The use of Predictive Maintenance in the operation of critical infrastructures therefore places special demands on existing maintenance strategies at DSOs to economically ensure security of supply. This paper proposes an operational concept consisting of a process model, IT-system landscape and information logistics model compatible with the current process and system architecture to deploy new maintenance strategies at DSOs.
The following paper presents functionalities of harvesting machines for industrial intercropping use cases and a typology of their applications. The aim is to support the design and development of novel intercropping harvesting machines that are required for the implementation of industrial intercropping systems. Building upon established intercropping use cases, distinctive features and their variations are outlined, and a morphology created. The morphology is then used to develop typologies of industrial intercropping use cases, which capture various manifestations of future industrial intercropping harvesting. Based on the types, functionalities for harvesting machines are derived. Given the transformative potential of this research, it is evident that substantial modifications to existing systems are essential. The envisioned machinery requires for most use cases sophisticated software integration, encompassing data analysis, artificial intelligence, and robotics. This paper identifies the unmet needs within the realm of industrial intercropping. It also creates an understanding of the industrial use cases and presents a concept of the required harvesting technologies, thereby contributing to a more sustainable future in agriculture.
Enabling Industry 4.0: A Roadmap For Efficient Implementation Of 5G Campus Networks In Manufacturing
(2025)
Private 5G networks, which are localized and designed for specific organizations or facilities, enable industrial production environments to transform by providing unparalleled opportunities for enhanced operational efficiency, flexibility, and digital connectivity. Based on four expert interviews and a detailed literature review for further validation, this paper presents a holistic roadmap for implementing private 5G networks in production settings. This roadmap adopts a phased approach, beginning with awareness and needs assessment, followed by the development of specific technical concepts, pilot implementation, network scalability, and ongoing optimization. Each phase is designed to address unique challenges and critical decisions involved in the successful deployment of 5G, including use case identification (e.g., predictive maintenance and autonomous systems), network architecture, partnership models, and integration with legacy IT infrastructure. This guided approach provides production facilities a pragmatic framework to navigate the technical, organizational, and economic complexities of 5G implementation, ensuring alignment with industry requirements and future technological developments. The proposed roadmap synthesizes insights from subject matter experts and recent literature, providing a sustainable, adaptable, and resilient strategy for 5G network deployment in production environments.
The advancing climate change and the resulting need to restructure the energy supply industry mark the beginning of an era of significant upheavals, profoundly impacting energy distribution. The ongoing integration of renewable energy sources introduces new strains on power grids, as they are now confronted with bidirectional energy flows as well as significant fluctuations in energy generation and demand. These developments pose unprecedented challenges to the physical integrity and operational efficiency of distribution networks. Therefore, electrical distribution grid operators (DGOs) must balance these new challenges while ensuring supply security and sustainability, as well as considering economic efficiency. To meet these new challenges, the development of innovative maintenance strategies is essential. In this context, the digitalisation of power grids emerges as a key technology, offering solutions through the introduction of holistic assistance and prognosis systems (APS). This paper focuses on the collection and analysis of requirements leading to the development of an assistance system for maintenance workers in distribution networks based on Large Language Models (LLM). The LLM aims to support workforce management by providing maintenance workers with targeted information and real-time recommendations. The methodology involves conducting expert interviews to gather essential requirements, followed by a thorough analysis of the collected data. Based on this, the foundation for developing the LLM-based assistance system is established. The results demonstrate how LLMs can enhance the efficiency and effectiveness of maintenance management by optimising workflows and supporting personnel in decision-making processes.
5G continues to develop as a key technology for digital transformation in production. This wireless, fast and secure transmission technology with long range, low latency and the ability to connect a large number of devices will enable companies to gradually overcome the hurdles of the fully connected enterprise of the future. At the same time, as a new and innovative technology, 5G still presents companies with a number of challenges. In particular, the issue of non-transparent cost calculation is still causing uncertainty for companies, especially in the decision-making process. The uncertainty of companies regarding the cost factors of private 5G-campus networks results from limited experience. The lack of knowledge about aspects such as license fees, hardware costs and maintenance expenses make budgeting difficult. A systematic analysis is necessary to make informed decisions and maximize the benefits of 5G technologies. This article presents an overview of the cost factors of private 5G campus networks, including the implementation phases and the stakeholders involved, through case study research. Furthermore, in addition to identifying the specific characteristics of the 5G-cost factors and implementation phases and stakeholders, the dependencies between these are also evaluated. The aim is to verify the identified 5G-cost factors so that a precise and transparent calculation is possible and well-founded decisions can be made regarding the implementation and use of 5G-technologies.
Das wirtschaftliche Potenzial von künstlicher Intelligenz (KI) im produzierenden Gewerbe ist mittlerweile unumstritten. Die Technologie ist in der produzierenden Industrie zu einem wichtigen Werkzeug geworden, um eine Vielzahl von Unternehmenszielen zu erreichen. Bei der Umsetzung von KI-Projekten wird jedoch häufig nur die Entwicklung der KI-Modelle betrachtet, weshalb viele Projektergebnisse als Prototypen verstauben und nicht erfolgreich in Prozessen oder Produkten angewendet werden. Ein Grund dafür ist, dass eine ganzheitliche Betrachtung der KI-Anwendung über ihren gesamten Lebenszyklus hinweg fehlt. Insbesondere Aufgaben, die im Betrieb sowie bei der Integration einer KI-Anwendung in die Prozesse und Produkte eines Unternehmens anfallen, sind oft unbekannt oder werden unterschätzt. Ebenso stehen KMU vor der Herausforderung, die für die anfallenden Aufgaben benötigten KIKompetenzen – nicht nur für die Anwendung selbst, sondern auch für vor- und nachgelagerten Prozesse – zu identifizieren. Das Forschungsvorhaben „VoBAKI“ hat zum Ziel, Unternehmen in die Lage zu versetzen, die Aufgaben und erforderlichen Kompetenzen im gesamten Lebenszyklus einer KI-Anwendung bereitzustellen sowie passende Sourcing-Strategien abzuleiten. Im Rahmen des Projekts wurde dazu unter Beteiligung des projektbegleitenden Ausschusses sowie weiterer Expert*innen aus der Wirtschaft unter Anwendung verschiedener, sich ergänzender Methoden (u. a. Literaturrecherchen, Interviews, Workshops und qualitative Inhaltsanalysen) ein umfassendes Vorgehensmodell entwickelt. Zu den Ergebnissen zählen die Sammlung von betrieblichen Zielen für den Einsatz von KI-Anwendungen sowie die Beschreibung von KI-Anwendungsfällen. Ein weiteres zentrales Ergebnis stellt die detaillierte Erarbeitung von Rollen, Aufgaben und benötigten Kompetenzen im KI-Lebenszyklus dar. Ferner werden als Ergebnis der durchgeführten Forschungsaktivitäten Voraussetzungen für KI-Projekte benannt sowie zentrale Kriterien erläutert, die bei der Auswahl von Strategien für die Beschaffung der relevanten KI-Kompetenzen berücksichtigt werden sollten. Weitere Ergebnisse sind Wirkungsmatrizen, die Zusammenhänge zwischen Unternehmenszielen und Sourcing-Strategien aufzeigen, sowie Empfehlungen für die organisatorische Ausgestaltung. Das entwickelte Vorgehensmodell unterstützt KMU sowie weitere Unternehmen mit unterschiedlichen KI-Erfahrungsgraden bei der erfolgreichen Bewertung und Auswahl von Sourcing-Strategien für KI-Kompetenzen und der Umsetzung von KI-Projekten.
In an era increasingly defined by the relentless advance of climate change, the imperative for sustainable transformation has emerged as a central concern for global organizations. For this transformation the intertwined concepts of decarbonization and digitalisation can offer a blueprint for a sustainable future.
Decarbonization, aimed at reducing carbon-based emissions, is critical in lessening the ecological footprint of businesses. Yet, achieving decarbonization is not a solitary journey but one that necessitates the integration of digitalisation as a pivotal facilitator, enhancing the efficiency and efficacy of this transition.
However, the challenge for organizations lies in devising and executing appropriate strategies for this transformation. Within the framework of the Roadmap.SW research project, a methodology is being developed to aid utilities in accelerating their decarbonization and digitalisation efforts. This involves initially assessing the organization’s current capabilities in digitalisation and decarbonization to then establish a desired future state and to finally outline steps for implementation. This research work extends the acatech Industry 4.0 Maturity Index to encompass additional design domains, incorporating capabilities and maturity levels specific to decarbonization. At the same time, the focus of the target group is changing.
While the original model focussed on Industry 4.0, i.e. the transformation of manufacturing companies, the extension focuses on municipal utilities. This approach not only charts a course for sustainable organizational transformation but also underscores the critical interplay between reducing carbon emissions and embracing
digital advancements.
