In today's models of global collaboration throughout the automotive value chain, the systems’ complexity and the importance of software in what is happening is steadily growing due to increasing digitisation. This requires a joint quality contribution from all partners. So far, data has been exchanged between OEMs and suppliers primarily in the event of warranty claims.
There is no operational network in the industry to comprehensively cover the elements of the value chains and provide the necessary funds for cooperative quality management with all the partners involved.
The vision of the quality management use case is to move from parts-based, bilateral quality management between suppliers and customers to a data-based approach across the OEM-n-tier value chains.
The Catena-X approach is to design a scalable data network within which both production and the field are connected in order to enable maximum product quality. By these means, a new level of transparency and traceability is achieved for all partners involved in the value chain.
This speeds up the resolution of quality issues, in particular through faster data availability and delivery. This leads to far higher customer satisfaction and a reduction in the costs of errors in turn.
As blueprints for interaction, defined methods, data models and processes form the basis of an efficient, data-based quality assurance along the value chain. The joint analysis of field data by OEMs and suppliers also enables the supplier to identify quality issues, even across multiple OEMs, and to define counter-measures as early as possible.
The sovereign exchange of data makes it possible to provide the data necessary for early warning and root cause analysis in a protected way, without revealing any information that should not be shared. This allows a focus on content in the quality process without disclosing any confidential, information from the value chain that is of relevance to the competition.
The use case provides access to a wide range of quality assurance tools, including analysis functions at different levels, which can be selected flexibly, depending on the needs of the individual quality case.