Data modeling to facilitate internal traceability at a grain elevator
Data management in food supply chains to facilitate product traceability has gained importance in recent years. This paper presents a relational database model to facilitate internal traceability at a grain elevator, which is one of the first nodes in a food supply chain. This approach for modeling traceability information in bulk food supply chains has not been studied in past. At an elevator, grain lots (inbound deliveries) are blended to meet buyer specifications, and individual lot identity is not maintained. As a result, an outbound shipment to a customer likely contains grain from many different sources. In a food safety related emergency, tracing the source of a problem or tracking other affected shipments would be nearly impossible. An efficient internal data management system could mitigate these problems by recording all grain lot transformations/activities, including movement, aggregation, segregation, and destruction as well as supplier and customer information. In this paper, a relational database management system is proposed that stores all necessary information, including product and quality information, related to the grain lots in order to enable product traceability. The system can be queried to retrieve information related to incoming, internal and outgoing lots and to retrieve information that connects the individual incoming grain lots to an outgoing shipment. Furthermore, this system can be used both to trace back to the source of a given lot and to track information about previously shipped lots forward. In addition to traceability application, the information stored in this database provides a comprehensive dataset for many applications including mass flow optimization, resource optimization and improved operational efficiency of the grain elevator.
data modeling internal traceability elevator bulk grain handling er model