Supply Chain Visibility and End-to-End Traceability: Enhancing Food Safety and Consumer Trust in the Consumer Products Industry

Abstract

The consumer products industry has prioritized food safety incidents and opaque supply chains, as they are no longer an afterthought for them; it's become a strategic priority. This article discusses the practical benefits for foodborne illness risk reduction, quicker recalls, and consumer-trust restoration that can be achieved through end-to-end supply chain visibility, built on the Internet of Things, distributed ledger technology, and the power of artificial intelligence and machine learning. It suggests a 5-layer reference architecture covering data capture, connectivity, data integration and trust, AI/ML analytics and end-user visibility applications, and outlines AI/ML use cases such as anomaly detection, predictive quality scoring, computer-vision quality control, and graph-based trace-back, plus a “workflow” spanning incident to recall enabling continuous model retraining. The article also examines obstacles to implementation, such as data standardization, integration with legacy systems, and cost, and ends with a vision of digital twins, generative AI, and predictive recall. The tables and diagrams illustrate a comprehensive analysis to provide a structured and evidence-based reference for practitioners and researchers on designing and evaluating modern traceability programs.