An Intelligent Early Alert Mechanism for Optimizing Milk Supply Chain using Mathematical Model

Safety risks in the milk industry's supply chain present critical anomalies. These encompass hazards spanning from contamination during production to compromised storage conditions, potentially leading to health threats for consumers. Implementing advanced monitoring systems, ensuring adherence to regulatory standards, and fostering transparency across the supply chain is imperative. The purpose of this research is to develop an intelligent early alert mechanism for optimizing the milk industry from a supply chain perspective. We obtained a dataset that includes information on dairy supply chain infrastructure. It contains significant anomaly indicators such as milk quality and raw milk price. We employed a mathematical model with Feature fine-tuned Random Forest (F-RF) to predict anomalies like safety risks in milk industries. It effectively identifies anomalies, particularly safety a risk, facilitating proactive interventions to mitigate potential food safety hazards and optimize the efficiency of the milk industry's supply chain. A cross-correlation study was carried out to examine whether abnormalities in any of the indicators could be utilized as a precursory alert for the existence of a food safety hazard. This research implies that significant alterations in areas near the food supply chain might set off an increase in food safety issues that show indications months later. When food producers and regulators are aware of these interactions, they can take prompt action to avoid issues with food safety.