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Electric trucks (e-trucks) play an important role in reducing CO₂ emissions especially on short distances in pre and post-carriage in combined road-rail freight transport (CT). Using the example of a CT terminal, this article highlights the logistical and energy challenges involved in using e-trucks to establish suitable charging infrastructures and ensuring a reliable power supply.

Due to its weightlessness, space offers enormous opportunities for production. The unique conditions of microgravity, for example, can simplify the development of organs and tissues from the body's own stem cells, allowing therapies to be developed in a more targeted manner. Even though many independent initiatives are currently emerging to explore this and other potential applications, their success is not a foregone conclusion.

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.

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.

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.

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.

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.

Predictive analytics opens up opportunities to improve decision-making in manifold areas, including in supply chain management (SCM). Yet, the complete realization of its potential requires the identification of the corresponding needs upfront. This paper provides a structured concept that guides through the complex and interdisciplinary endeavor of requirements analysis for predictive analytics in SCM. Due to the generic nature of this approach, it can be applied for any use case and be adapted or enhanced in case of need.

Intelligent digital twins (IDT), as the next evolutionary stage of digital twins, have the potential to accelerate and optimize processes within companies. The intelligent twin presented here independently analyzes 3D CAD data and automatically conducts a physical simulation of the assembly. Utilizing the IDT optimized assembly, reduces production costs and accelerates the production planning process. This specific use case illustrates the broader possibilities and advantages of IDTs, offering valuable insights for their transferability.

The digital twin [DT] is considered a key technology of Industry 4.0. Its basic concept is now being successfully applied in practice, as demonstrated by the commissioning of Mercedes' Factory56 in 2022. New identification technologies, tracking systems and communication solutions faciliate new ways of controlling production and managing material flows, particularly at the shop floor level. With precise technical data permanently available not only for products, but also for material availability and order fulfillment status, production processes can be managed more dynamically and efficiently. This is precisely where the concept of the DT comes into play, enabling the immediate use and evaluation of this data.Its relevance continues to grow, especially in the context of make-to-order production, the rising variety of product configurations, and the globalization of production and supply networks. This article introduces the basic concept of the DT and illustrates how it connects to ...