8 Industrial AI and data ecosystems: two sides of the same coin By Dr. Olaf Sauer, Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe Simply supplying or operating highly productive and reliable machines, systems, or components will no longer be sufficient as a distinguishing feature and basis for business success in the future. In addition to traditional hardware-related skills, factory operators and their equipment suppliers must quickly learn and master comprehensive skills to make effective use of new methods and tools such as artificial intelligence (AI) and machine learning (ML), digital twins, and data ecosystems/data spaces. This will not be successful on its own: only in cooperation with like-minded partners across the entire value creation network, where everyone plays to their strengths, can AI models create added value for all involved partners. Data ecosystems and data spaces form the basis for the beneficial use of industrial AI, especially for small and medium-sized manufacturing companies. They standardize data from different sources, offer easy access to the data ecosystem via standardized connectors, and thus enable reusable AI applications. In the following paragraphs, further key aspects and advantages are listed. Data ecosystems facilitate easy data accessibility and quality, even across company borders, e.g., by providing access to machines process, quality, and/or maintenance data from various sources. Standardized formats and metadata defined by the data ecosystems facilitate the targeted use of runtime data. Open standards, which are already beginning to become widespread in the manufacturing industry, then facilitate the combination of data from different systems, e.g., ERP, MES, SCADA, or PLM systems. Companies are often reluctant to exchange data across company borders because they fear to reveal valuable knowledge that has been built up over many years. Data ecosystems offer maximum protection for precisely this purpose through defined governance and data security: defined roles, access and usage controls, trusted data sources, and automatable data contracts (data access agreements) facilitate and support data exchange within the network. This enables ecosystem partners, such as suppliers, customers and service providers, to integrate data from entire value chains and exploit all potential benefits that they would never be able to leverage on their own. This results in significant advantages when using artificial intelligence and machine learning: • Existing, prepared data sets can be used for multiple AI applications, e.g., predictive maintenance, process optimization, quality assurance, etc. • The AI models have higher model quality and are trustworthy because consistent data improve model accuracy, leading to stability and comprehensibility of AI applications. • Sharing data in trusted data ecosystems avoids costly isolated solutions and enables the gradual and therefore risk-reduced deployment of AI, from pilot projects to serial production. Open standards lead to less data preparation per project and thus to faster prototypes and their iterative optimization. • Partnership-based value creation arises because companies in a value chain use data cooperatively, e.g., jointly trained models with suppliers, thereby strengthening their competitiveness together. Ultimately, every participant in the data ecosystem benefits. Read more https://t1p.de/p52o4 Data spaces and data ecosystems https://t1p.de/3gw44 AI engineering, process optimization https://t1p.de/ct7lh Image: © Fraunhofer PODCAST German Julia Dusold and Anja Ringel from Industry Insight in conversation with Olaf Sauer. https://t1p.de/l6pnt Industry Insights is a podcast by “Produktion”.
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