Enhancing supply chain traceability and transparency through machine learning implementation

Sesha Bhargavi V.; Sawai N.M.; Kalyan Chakravarthi M.; Mankar A.R.; Rodriguez M.A.M.; Pavitha N. · Interdisciplinary Approaches to AI, Internet of Everything, and Machine Learning (2024) · DOI: 10.4018/979-8-3373-1032-9.ch028

Resumen

The application of machine learning techniques to increase supply chain traceability and transparency is examined in this study. Recently, traceability systems have been created as useful tools for enhancing "supply chain (SC) transparency and accessibility", particularly in industries with high stakes for consumer health and safety, including food and medicines. "Blockchain-related SC traceability" research has drawn a lot of interest in recent years, and it's safe to say that this technology is now the most promising option for offering services connected to traceability in SC networks. The many technical implementation facets of SC traceability systems powered by blockchain are thoroughly reviewed in this study. In a competitive global market, machine learning is helpful for data analysis, predicting disruptions, including verifying product authenticity. Its transformative potential can be seen in realworld applications including Walmart's rapid food traceability, Nestle's sustainable procurement, and Procter & Gamble's supplier risk management.