Artificial intelligence is a groundbreaking technology of our time. The effects of artificial intelligence will have far-reaching consequences comparable to electricity or the invention of the printing press. Currently the resources for innovations are however dominated by giant technology enterprises in North America and China.
SMEs face obstacles to the introduction of additive manufacturing (AM) methods: There is a lack of qualified employees and access to know-how, equipment, infra-structure, and markets. The main objective of the AMable project is to overcome these barriers to implementation and enable the introduction of AM technologies by SMEs.
The project deals with the conceptual integration of GAIA-X components into the COSMOPlat platform of the project partner Haier. With this customer platform, the global market leader for household appliances provides support in optimizing the manufacturing processes of its products, among other things.
The Fraunhofer Cluster of Excellence Cognitive Internet Technologies CCIT re-searches cognitive technologies for the industrial Internet. Researchers in various disciplines are developing key technologies along the value chain, from sensors to intelligent learning methods in data processing to the cloud. CCIT helps companies with the forward-looking design of their business by opening up market access with cognitive solution services and products.
Only three percent of container terminals are automated today. However, the chal-lenges and requirements in this sector can only be overcome and met through fur-ther digitization and process automation. Port operations need to be optimized, the supply chain of operators and carriers must be improved, and emissions and waste have to be reduced.
Innovative logistics solutions and process innovations make a considerable contri-bution to the competitiveness of companies. The DB Schenker Enterprise Lab for Logistics and Digitization established in 2015 accelerates the digital transformation of processes in the concrete business environment of a logistics service provider.
Industrial data storage and use is becoming more and more important in increasingly digitizing processes. For example, a company-specific approach to centralized and decentralized data management must be found and operational responsibilities must be clearly defined. A functioning data management system is of strategic importance for business processes such as customer service.
The digital transformation affects nearly all areas of the country, whether societal, political, social, or even legal. In the project to establish the European Blockchain Institute in North Rhine-Westphalia, formative forces from research and business are being concentrated on the future-oriented blockchain technology in order to drive this transformation forward.
In the Fraunhofer-Gesellschaft’s decentralized organization, research data frequently exist in the individual information silos of the institutes. There is often no exchange of data between institutes because a requesting institute has no transparency about the existence of related data and a potential provider institute wants to protect its data.
Supplier or sub-supplier bottlenecks can have massive technical and economic consequences for automobile manufacturers. However, suppliers hesitate to share information about foreseeable capacity bottlenecks with manufacturers, for exam-ple because they fear disadvantages in price negotiations.
Companies are continuously recording increasing volumes of master, movement, and process data, and using them in numerous business processes. As a result, the intelligent management of these data is increasingly becoming a key success factor in global competition. Obtaining gainful information and effectively improv-ing business processes can only succeed when data of high quality are collected and made available.
Companies are continuously recording increasing volumes of master, movement, and process data, and using them in numerous business processes. As a result, the intelligent management of these data is increasingly becoming a key success factor in global competition. Obtaining gainful information and effectively improv-ing business processes can only succeed when data of high quality are collected and made available.
Scientific institutions based in Dortmund that – on their own – are already shaping and driving logistics research today are working together under the umbrella of the High Performance Center Logistics and IT. In the High Performance Center, they are now establishing a national infrastructure and innovation ecosystem in cooperation with industry, not only together but also synergetically – from fundamental research to applied research.
Next to digitization and individualization, automation is the third key pillar of indus-try 4.0. Increased networking in production and logistics is simplifying many pro-cesses. Ultimately they can even be carried out autonomously by special software components (agents). The Legal Testbed provides a simulation environment to support the development of agents by SMEs with a focus on reviewing legal cer-tainty and IT security.
Digital platforms and their AI are crucial to fully exploit the optimization potential for processes, digital services and new business areas in logistics. In order to achieve this optimally, a decentralized (platform) eco-system is being created in the SELE project, the results of which will be made available as open source.
Germany’s industry is undergoing a fundamental shift from products to services in value creation. However, the required service knowledge for industrial facilities ex-ceeds the knowledge of individual service technicians and in part even of compa-nies. Along with the shortage of skilled workers, German SMEs face a tremendous challenge in the coming years to maintain a leading position in the provision of services.
The market for data flourished in past years. But the market has been shaken by a considerable loss of trust in platforms that were considered secure and data pro-tection friendly. This loss of trust has hit the data economy hard since its open data resources have been restricted. Unless technical standards are amended, this de-cline is expected to continue.
Notwithstanding the monitoring of individual production steps, modern production systems encompass complex dependencies between materials, operating equip-ment, and processing steps that do not meet the required level of quality, even with supposedly correct processing. This results in costly rework or even rejects.
Fraunhofer Institute for Software and Systems Engineering
