Integrating Veterinary Public Health Data into EPCIS-Based Digital Traceability for Dairy Supply Chains

Abstract

Dairy foods—particularly cheeses produced from raw or minimally processed milk—remain vulnerable to hazards such as Listeria monocytogenes, where delayed laboratory confirmation can expand recalls, increase food waste, and delay outbreak containment. This study proposes a veterinary-aware digital traceability framework that embeds herd health data, milk-quality testing, and inspection outcomes directly into batch-level EPCIS event records. By representing veterinary public health controls as structured, machine-actionable traceability elements, the framework enables automatic logging of mandatory control points, systematic compliance verification, and rule-based risk state transitions within standard EPCIS infrastructures. Using regulation-consistent dairy simulations modeling delayed Listeria detection during maturation, we evaluate the operational impact of event-level causal traceability within the proposed architecture. Compared with conventional time-window recall strategies, provenance-based trace-forward queries reduced recall scope under the evaluated synthetic scenarios. Integrating structured veterinary controls into EPCIS-based traceability systems supports automated regulatory evidence generation and more targeted recall decisions, contributing to improved auditability and reduced food waste in dairy supply chains.