Typeset

Developing Data Standards in Battery Cell Manufacturing

Developing Data Standards in Battery Cell Manufacturing

From requirements analysis to standard development procedure
David Roth, Tom Hülsmann, Felix Tidde
The growing demand for battery cells offers significant potential for the use of digital solutions in their manufacture, which in turn creates opportunities for added value through adaptive and flexible production systems. A key enabler is interoperable data exchange based on formalized data descriptions. Existing ontologies and information models remain too abstract for direct implementation. This paper presents a requirements analysis of data standards in battery cell manufacturing. A procedure for developing domain-specific standards based on OPC UA (Open Platform Communications Unified Architecture) is derived from the results.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 96-103
Bridging Automated and Traditional Approaches in Material Transport

Bridging Automated and Traditional Approaches in Material Transport

Why manual tugger train systems remain prevalent in intralogistics
Christoph S. Zoller, Wladimir Rempel, Justus Langer, Bonita Grzechca
The ongoing automation of production logistics through driverless transport systems (DTS) can significantly enhance the efficiency and quality of transport processes. Despite these advantages, many companies still choose manual tugger train systems for material supply. Semi-structured interviews with industry experts provide insight into the reasons behind these decisions, with particular emphasis factors that extend beyond purely economic assessment. The findings indicate that the lack of flexibility of driverless transport systems and the effort required for implementation effort are key reasons why manual transport solutions are often preferred in intralogistics.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 60-66
Design Thinking

Design Thinking

A practical approach
Benjamin März, Nick Ackerhans
Originally developed in the software industry, a method inspired by the mindset of designers has proven to be a broadly applicable approach to solving complex problems. Design thinking holds the potential to contribute positively to social and economic change. A look at its development offers valuable insights for a deeper understanding.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 68-75
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
Proactive Skill Development in Logistics Management

Proactive Skill Development in Logistics Management

The future of dynamic work contexts
Michael Heins, Lisa Vogt
Smooth logistics management is fundamental for companies to function effectively. This is challenged by actor- and organization-related barriers in production planning and management. The Berufliche Hochschule Hamburg fosters the skills that individuals need to overcome obstacles in digitalized working environments. This concept is presented in the following article.
Industry 4.0 Science | Volume 41 | 2025 | Edition 4 | Pages 22-28
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.
Sustainability Information Across the Supply Chain

Sustainability Information Across the Supply Chain

Structured requirements analysis for using sustainability data in networks
Lina Keefer, David Koch ORCID Icon, Ann-Kathrin Briem, Shaoran Geng
Sustainability has gained increasing importance for all stakeholders in the value creation network in recent years. As a result, companies are working to optimizr their products and processes with respect to the three dimensions of sustainability. To responsibly design production systems that are sustainable in the long term, continuous data exchange between all actors in the value creation network is essential. Both in early product development and in production planning and execution, reliable information and corresponding decision support are crucial. The following article addresses the structured collection of requirements that companies in the automotive industry have for a data model and methodology to enable decision support.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 52-58
Smart Business Models in Intralogistics

Smart Business Models in Intralogistics

A service-oriented approach to customized logistics solutions
Anja Wiebusch, Niklas Wilkowski
Equipment-as-a-Service (EaaS) enables logistics companies to offer their customers tailored solutions, helping them to remain flexible and reduce costs as well as risks even in difficult times. Customers no longer pay for the object itself but only for the service provided, such as the usage time of a forklift truck. This allows them to focus on their core competencies and convert high investment costs into more flexible operating costs [1]. High capital commitment and the risk of underutilization of machines can thus be avoided and transferred to the logistics provider. This article examines the adjustments that logistics providers must make to accommodate this business model as well as some possible use cases.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 30-35
Machine Learning to Promote Sustainability 

Machine Learning to Promote Sustainability 

Company analysis based on expert interviews
Niklas Bode ORCID Icon, Lukas Nagel ORCID Icon, Oskay Ozen ORCID Icon, Matthias Weigold
This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 44-51
Increasing Resilience in Logistics with IT

Increasing Resilience in Logistics with IT

Investigating supply chain risk management information systems
Alexander Baur, Jasmin Hauser, Dieter Uckelmann ORCID Icon
The blockage of the Suez Canal in 2021, caused by the accident involving the container ship Ever Given, clearly illustrates the need to design global supply chains in such a way that they can respond quickly to disruptions. In a volatile, uncertain, complex, and ambiguous (VUCA) environment, conventional logistics processes that focus on efficiency, and supply chain management methods in particular, are increasingly reaching their limits. Resilience, achieved through a combination of robustness and agility, is essential to ensure responsiveness. This article analyzes how risk management information systems (RMIS) can increase resilience. The analysis covers data availability, data transparency, modeling and simulation of risk scenarios, and the development of appropriate emergency action plans. Despite existing challenges in designing IT infrastructure, the measures mentioned have the potential to increase resilience in logistics.
Industry 4.0 Science | Volume 41 | Edition 4 | Pages 36-42
1 6 7 8 44