Practical example and current challenges
- The food industry plays a strategically important role in the German economy, both economically and socially (€185 billion turnover, approx. 618,000 employees) . Large amounts of data are collected for processing raw materials into standardised products by means of physical, biological, or chemical processes, as well as the final quality control. An economic utilisation of the data across locations or manufacturers is only carried out in parts at a high aggregation level, so that a large part of the data value remains unused.
- The continuous collection and analysis of data would help the food producer to optimise its production (local ecosystem). If the food producer also sees itself as a data producer, it can offer data products that are networked on a supra-regional level to form higher-value data products (global ecosystem). They can be integrated together with data products from other industries to enable geographical, ecological, and economic analyses. This enables different stakeholders such as farmers, trade, and the processing industry to find answers to questions about conditions, grievances, and forecasts in local regions as well as nationally. This in turn enables an early prediction of price developments for raw materials.
- FAST is a decentralised data marketplace for trading data and algorithms in the context of food production with the basic idea of a European, legally compliant, and sovereign data infrastructure. The overall objective of FAST is to realise so-called "Data Products as a Service", which abstract valuable knowledge from raw data. These provide insights for specific applications along the entire value chain of food production. This makes it possible, for example, to implement targeted measures to avoid storage bottlenecks, optimisation potential about sustainability as well as improved demand planning or the assessment of customer needs on the market to reduce investment risks.
- The implementation of the FAST use case would create a data ecosystem that can sustainably build and support a platform economy based on the market conditions and needs of the players in the food industry. This data economy is supported by new, dynamic business and pricing models as well as a technical and contractual framework. Only through innovative, context-based pricing models can standards be established and an incentive created for their use. Thus, potential roles and business relationships are identified and context-based pricing and licensing models are derived
What added value does the "GAIA-X project" offer?
- GAIA-X enables the secure storage and enrichment of data and gives the possibility to use com-puting power not available on site as well as cloud modules and AI applications to compensate for existing deficits.
- GAIA-X sets rules and standards for collaborative approaches and enables the use of a permission concept for secure and regulated access, sharing, embedding and provision of data that was previously inaccessible.
- GAIA-X provides adequate rules and standards for cooperative business model approaches as well as legally secure concepts of data monetisation to reduce complexity and costs of data commercialisation.
Use Case Team
- Prof. Dr.-Ing. Wolfgang Maaß - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
- Dr.-Ing. Sabine Janzen - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
- Hannah Stein - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
- Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (Smart Service Engineering)
- Agrarmarkt Informations-Gesellschaft mbH (AMI)
- Chocoladefabriken Lindt & Sprüngli GmbH
- Forschungsinstitut für Rationalisierung e.V. (FIR), RWTH Aachen
- Software AG
- Universität des Saarlandes