Dairy Manufacturing Webinar, Modelling Tools to Improve Dairy Product Quality, Novemebr 11, 2026

Modelling Tools to Improve Dairy Product Quality

Presenter

Luke Quian

Webinar Description

The rapidly evolving landscape of artificial intelligence, particularly in computer vision, natural language processing, and agent-based systems, has lowered the barrier for developing and deploying digital tools across the dairy food supply chain. These increasingly accessible AI capabilities create new opportunities to improve dairy product quality, reduce microbial spoilage, and prevent food loss and waste. This presentation will discuss how modelling tools can support dairy quality management through four connected areas: (i) diagnosing critical factors and management practices that inform control strategies, (ii) enabling real-time prediction and monitoring of microbial contamination in dairy products and production environments, (iii) improving existing risk assessment and shelf-life prediction tools through expanded data streams, and (iv) using AI agents as decision-support interfaces that connect predictive models, operational records, and user inputs to generate actionable recommendations.

While many current tools are developed for specific quality-control tasks, the next major opportunity lies in connecting these models into an integrated digital decision-support system. Such a system could allow AI agents to query predictive models, interpret real-time and historical data, evaluate intervention scenarios, and assist stakeholders in making timely, context-specific decisions across farms, processing facilities, distribution, retail, and consumer stages. However, achieving this vision will require broader digitalization of the dairy food system, careful design of model connectivity across supply-chain stages, and solutions for data privacy, trust, and stakeholder-specific decision needs. The presentation will conclude by outlining an ideal integrated modelling framework for dairy product quality, highlighting currently available tools, emerging AI-agent applications, and future research needs.