The more food safety and quality assurance experts know about potential hazards the better they can manage risks and prevent an incident in the supply chain. Monitoring risks in a complex and global supply chain can be challenging if one considers the large amount of data sources about hazards that need to be continuously monitored and the limited amount of time that the experts have to monitor and process the information for the hazards. In a very dynamic food supply chain, relying on outdated information about hazards may lead to a static risk assessment that can increase the possibility of missing increasing and emerging risks. Furthermore, creating descriptive statistics for the hazards it's not sufficient, we need to move to predictive analytics that will inform procedures and awareness of risks.
This presentation will focus on how risks can be identified and continuously monitored using global food safety and fraud incidents. Insights and trends for ingredients and materials widely used by the dairy industry will be presented and analysed. A methodology for predicting risks by leveraging Big Data mining and Artificial Intelligence technologies will be presented along with predictive analytics for key ingredients and materials used by the dairy industry. The main goal of the presentation is to provide the knowledge about all the risks and to present how such predictive analytics can help food companies to move from reaction to prevention. The presentation will conclude with the steps on how to perform remote risk assessment using a food safety intelligence platform like FOODAKAI (https://www.agroknow.com/foodakai) that monitors and predicts risks across the supply chain.