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Due to the advancement of robotics in industrial production, the strict separation of workspaces is gradually dissolving (HRC - human-robot collaboration). HRC combines the strength and efficiency of robots with the skills and cognitive abilities of humans through seamless cooperation [1]. Manufacturing companies, in which HRC scenarios are to be implemented, require a transparent and reflective selection of technology with regard to safeguarding the worker against hazards posed by the robot. To address this challenge, this paper proposes a process model that helps technology developers and systems integrators select appropriate technologies and solutions for HRC scenarios.

The IT-risks which factory infrastructures are exposed to, require a common view of IT-security and operational technology (OT) protection. In this context, the measures from office IT can only be transferred to the production area and production control to a limited extent. The requirements and protection goals for the equipment used and the networking between these components are too different. An integrated approach and continuous management of IT security helps to identify and implement targeted measures in a concerted manner.

Many companies struggle to determine their own status and strategy when it comes to digitalization and self-organizing production. The Industry 4.0 Maturity Index offers a guideline in this process and helps to ensure future competitiveness. Using the multidimensional maturity model, companies can evaluate their status-quo and develop a roadmap to accelerate their digital development. The case study of a manufacturer of electrical connectivity solutions demonstrates how to reduce costs and increase productivity with the help of the Industry 4.0 Maturity Index.

After a delayed start, in the last years OT security has substantially caught up with IT security. Many technical IT security controls are also available for machines and plants. But the maturity distribution in the field of OT is more complex and fragmented, as shown in the present article. Certain fundamental issues and contradictions of IT security become particularly obvious here and might need to be solved within OT security.

Industry 4.0 forces companies to keep their business models up-to-date to keep their competitiveness and to satisfy customer demands. Key words like “individual production” and “lot size one” put pressure on companies to adapt their current production to the new requirements. Especially for companies with a mass production this sounds like a 180° turn. Furthermore, no best-practices for introducing industry 4.0 exist and therefore, one cannot rely on such hold points. Nevertheless, a structured transformation of the own business model towards industry is not impossible.

There are numerous analysis methods available to support engineers working on continuous improvement projects. Digital transformation facilitates to reduce the effort for data acquisition and processing. The Institute of Production Management and Technology and the medical company Dräger have jointly developed a web application for multi-method analysis. This article describes its data structure and technology.

Cyber-physical systems (CPS) have shaped the discussion about Industrie 4.0 for some time. For ensuring the competitiveness of producing enterprises the vision for the future figures out cyber-physical production systems (CPPS) as a core component of a modern factory. Adaptability and coping with complexity are (among others) potentials of this new generation of production management. The succesful transformation of this theoretical construct into the practical implementation can only take place with regard to the conditions characterizing the context of a factory. The subject of this contribution is a concept that takes up the brown field character and describes a solution for CPS-extension of existing (legacy) systems.

Logistic processes at seaports and inland terminals play an important role for finished vehicle logistics. High flexibility and reactivity are required to cope with frequent deviations from original operational planning. This paper describes a concept for developing tools and systems to optimize planning and control processes. This includes a simulation backed planning system and a control system for order allocation using mobile devices and their GPS as well as sensor data.

Software development of industrial robots requires interdisciplinary knowledge and technical experience. Due to the heterogeneity of the manufacturer-dependent programming languages and tools, robot programming remains highly complex, although robots themselves are flexible and can be used for a wide range of applications. To support different roles during the development, including component provider, application developer, system integrator and end user, a model based approach was developed in the research project ReApp. The data exchange format AutomationML was used for the modelling of robot components and systems. Based on domain ontologies, the AutomationML models were processed semantically and converted to a machine-interpretable information model, from which source code was generated.

The economic operation of the offshore wind energy turbines is of fundamental importance for the industry. Due to the prevailing weather conditions at sea these operations require optimal plannung and control. This contribution presents the work process and information requirements of an offshore service company. Suitable industry 4.0 technologies are identified to increase information transparency for the supply chain. In conjunction with a cooperative planning and control instrument, a reliable basis of decison-making for the execution of service assignments can be provided. This constitutes a direct contribution to a reduction of operation costs for offshore service logistics.