Catena-X

Actual situation

German automotive companies, suppliers and service providers are particularly involved in global supply chains and are therefore particularly affected by the challenges abbreviated as "VUCA": Volatility, Uncertainty, Complexity and Ambiguity. In recent years, prominent geopolitical disputes (the trade dispute between the USA and China), major natural events such as the tsunami disaster in Fukushima and the COVID-19 pandemic have shown how sensitive local and global supply chains are. Currently, for example, bottlenecks in the supply of semiconductor products in the German automotive industry are threatening production processes to an extent that cannot yet be finally quantified. These examples show that the resilience of production must be improved due to its complexity and global orientation.

So far, however, there has been a lack of consistency in the data (supply) chains across the product life cycle, the production life cycle and the supply chain. The reasons for this are

• The degree of digitalization is decreasing drastically, especially among medium-sized suppliers. Due to a lack of willingness to invest or a lack of necessity, there is a lack of IT infrastructure and IT specialists, even among so-called hidden champions.
• There is a lack of trust in sharing data with supply chain partners. Although collaborative data use increases resilience and efficiency, companies are reluctant to do so. This is due to a lack of trust that data will be used for purposes other than those originally intended.
• There is a lack of data standardization. Proprietary systems, individual data formats and protocols hinder supply chain-wide networking. Existing standards are complex, error-prone and do not define a standard for interfaces, authentication and authorization mechanisms.
• Algorithms are not interoperable. The lack of standardization of data and interfaces, often monolithically designed and antiquated software systems severely restrict the use of data. Existing algorithms, e.g. for transportation planning or store floor control, can only be replaced with newer, more efficient models at great expense.

Catena-X project

Networked data (data chains) ensure the resilience of German / European companies against global disruptions and volatilities. Resilience is to be created through end-to-end digital traceability in the supply chain and the near-real-time or proactive identification of damaging events, disruptions or relevant changes. The immediate and supply chain-wide assessment of the extent of damage and the digitally supported rapid implementation of countermeasures are the key to resilience. Further potential, also with regard to the sustainability of automotive production, lies in the consistency of data over the entire life cycle of a product. Networking product and production data across company boundaries and the product's usage phase simplifies the circular economy, as data on product composition can be provided during dismantling. The feedback of usage data also enables faster product and production optimization.

The Catena-X Automotive Network creates the first major use case of GAIA-X via system-relevant representatives of the automotive value chain in a holistic approach. This will improve the transparency, interoperability and effectiveness of the entire value creation network. Shared digital models of vehicles and their system components are to be created along the entire automotive supply chain, with the possibility of using this data in new digital business models, drastically increasing transparency in product development and production and improving quality for the customer. Valid product-, process- and machine-related data is to be exchanged with suppliers and service providers via standardized interfaces or digital services across all stages of the value chain in a collaborative manner and while preserving the (data) sovereignty of each individual partner. Data spaces, platforms and ecosystems are to be established that enable federated data storage and use, from which the partners benefit equally and stimulate new data-based business models at all stages of the value chain. In line with Germany's and Europe's data strategies, the project is creating a data space for the automotive industry based on GAIA-X and International Data Spaces.

The vision of the "Catena-X Automotive Network" project is a joint data-driven value creation network for all players in the German and European automotive industry. Catena-X creates a distributed GAIA-X-based data ecosystem based on European standards, which guarantees the digital sovereignty of all players in the automotive industry. The efficiency of the existing data and services of each individual player is increased by networking with each other. The partners involved in the network can leverage the great potential of shared data as a core component of digital innovations in the automotive value chain. The players access Catena-X as a neutral, secure, trustworthy platform tailored to the automotive industry with open source-based services and business applications.

The basic understanding of Catena-X is that a data ecosystem can be established with suppliers, customers, competitors and, in future, with companies from other sectors (e.g. mechanical engineering/chemicals) in order to maintain the international competitiveness of the German and European automotive industry with the help of continuous and resilient value chains.

Project structure

The project focuses on automotive use cases. These use cases address core topics of the innovations required by vehicle manufacturers and the supplier industry in Germany and Europe. The focus is on cross-company value chains.

• The traceability application for components creates a linked data chain along the value chain, which is based on the dependencies to the suppliers and sub-suppliers involved.
• With the sustainability application, the CO2 value of vehicles or components can be recorded for the first time across numerous tier levels and generated dynamically, e.g. depending on the production location (including real data). Certificates for environmental protection and social standards can also be recorded.
• In the Quality Improvement application, data access facilitates shorter discovery and resolution cycles as potential problems are proactively identified and complex error patterns are solved collaboratively.
• The Demand and Capacity Management application is a cross-tier solution that optimizes demand and capacity to significantly improve supply chain resilience and proactively manage risk.
• The business partner management use case enables the efficient integration of companies through centralized, low-effort registration with a unique business partner identity (One-PID) in the automotive industry network.
• The modular production application increases the flexibility, availability and robustness of production while at the same time increasing product diversity.
• The Manufacturing-as-a-service use case aims to achieve improvements in value creation by flexibly balancing capacities between integrated smart factories on the platform.
• In the real-time control and simulation use case, AI-based and simulation-supported forecasting algorithms with result visualization and a mobile user interface are being developed. The use of distributed machine learning (federated learning) allows for a higher dimensionality and complexity of the data to determine the relevant production-logistical key figures.
• The central scope of consideration for the use case of data and model-centered development and operational support is the "digital twin" concept for the integrated mapping of products with associated functions, properties and core business variables via a common data model.

The circular economy application enables partners to circularize raw materials (use of secondary materials), i.e. raw materials from an old vehicle can flow back into production, as recyclers and producers can exchange information via the network for the first time.

 

Funded by the Federal Ministry for Economic Affairs and Climate Protection: funding code 13IK004Q.