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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.

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.

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.

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 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.

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.

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.