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The planning and implementation of automated warehouses is characterized by high investments and risks. The FMEA (Failure Mode and Effects Analysis) currently used to reduce risks requires a great deal of effort to conduct, as it has deficits in terms of design and implementation support. These deficits include a predominant focus on the process view without linking this to the design FMEA for automation objects, an insufficient structure for the use of similar repetitive processes and technologies, a lack of automated, parameterized generation of activities, failures and causes, and a lack of integrated test scenario derivation. These deficits lead to unrecognized failures and increase the effort required to carry out the FMEA and develop test scenarios. In this article, we present a generic FMEA model which, among other things, is able to access extensive practical data in the form of knowledge bases and thus resolve the aforementioned deficits.

Interoperable automation can benefit cybersecurity certification processes that result from the EU Cybersecurity Act (e.g. EUCS) so that they represent less overhead for the stakeholders involved. The development of key standardization efforts involving relevant stakeholders (e.g. regulators) is needed to fully realize these benefits. EU projects like H2020 MEDINA, HEU COBALT and communities such as EUROSCAL are well on the way to achieving this goal. However, more practical experience is needed to make continuous certification with automation a reality.

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

Motion-Mining® is a technology that uses motion sensors and pattern recognition to enable automated process mapping and analysis of manual work. This article evaluates the advantages and limitations of its use in manufacturing and logistics processes. To this end, Motion-Mining® is compared with traditional lean management tools used to analyze manual activities. Experiences derived from four use cases provide decision support for selecting the appropriate method for a specific use case.

Spare parts for older products are often difficult to obtain or cannot be produced in an economically viable way using conventional manufacturing techniques. This article examines whether damping elements for gearbox bearings (in/for the automotive sector) can be manufactured from thermoplastic polyurethanes (TPU) with the same or compatible properties as the original part alternatively using additive manufacturing.

Industrial manufacturing offers a wide range of opportunities for a more ecologically compatible and socially just organization. Sustainability-oriented HR management can competently support industrial manufacturing companies in the design and implementation of sustainable manufacturing. It is important to integrate ecological, social and economic requirements into the respective areas of responsibility. In addition, employees must be motivated and qualified to adopt environmentally friendly working practices in order to be able to implement sustainable manufacturing in practice. Sustainable incentive systems and employee-oriented personnel management must also be taken into account.

Correctly recycling obsolete refrigeration devices plays an important role in environmental and climate protection efforts. Recycling plants are subject to regular audits to ensure their compliance with strict environmental regulations. However, the collection of audit-related data is a challenging and time-consuming task, as it is usually done manually and is prone to errors. One solution for more sustainable and efficient monitoring is to automate digital data collection using sensors and artificial intelligence. This enables a direct estimate of the expected level of pollutants. This paves the way for continuous performance monitoring and efficient management of refrigeration appliance recycling plants.

The circular economy (CE) promises a more sustainable use of resources by managing products in a cycle and striving for a transformation from a linear to a circular supply chain. In particular, digital technologies as enablers for the circular economy have been increasingly researched and applied in practice in recent years. This article describes which digital technologies offer potential for increasing circularity in the production of circular photovoltaic (PV) systems.

The flow of materials along regional and global value chains has far-reaching environmental, economic and social effects. The sustainability profile of materials should therefore play a central role in management. Adapting material flows to adhere to sustainable principles requires an integrated approach that includes all areas of the company. The decisive factor is that sustainable materials management ultimately requires the entire life cycle of a product to be taken into account – from design and produc- tion through to sales and aftercare measures.