Traceability systems are crucial for smart manufacturing, providing transparency and structured documentation of a product's value generation flow. As regulatory and organizational demands rise, these systems have evolved from mere risk mitigation tools to essential components of the data revolution in Industry 4.0. In the automotive sector, recall costs are soaring, especially for electrified and autonomous vehicles. A robust traceability system can help mitigate these costs through targeted recall containment. This thesis introduces a modeling methodology for systematically developing traceability in manufacturing industries. It presents a traceability model for complex manufacturing systems, grounded in standardized terminology that includes trace objects, trace links, trace actors, and tracking and tracing functions, encompassing both manufacturing and supply chain data. The model is applied to an automotive case study, utilizing a graph database and a blockchain. The graph database facilitates the connection and storage of semantically rich manufacturing data, while the Ethereum-based permissioned blockchain allows for the tracing of macro data as products navigate the supply chain. This solution ultimately delivers full transparency and secure documentation for complex production networks.
