This research investigates machine learning methodologies for milk production optimization and customer engagement in Saudi dairy companies. Through empirical analysis of 47 dairy operations and structured interviews with 16 industry executives, we identify specific algorithmic applications addressing milk sector challenges. Core findings reveal: demand prediction algorithms reduce production waste by 44-45% through accurate forecasting of milk consumption patterns; cold chain monitoring systems prevent product deterioration; quality detection systems achieve 96% accuracy in identifying contaminated milk; customer analytics platforms generate 37-42% improvements in conversion metrics. Implementation costs of 1.9-3.4 million SAR produce first-year returns exceeding 4.2-9.6 million SAR. The research documents the implementation pathway requirements, organizational barriers, and prerequisites for success. Dairy enterprises demonstrate receptiveness to technology adoption when supported by executive commitment, data infrastructure investment, and workforce capability development. This investigation extends prior research by focusing specifically on dairy-sector operational requirements and market dynamics unique to the Arabian Peninsula. Findings suggest that machine learning technologies offer substantial commercial opportunities for dairy producers seeking operational efficiency and market responsiveness in Saudi Arabia's competitive dairy landscape.
Zouheir Sallman (Wed,) studied this question.