ISSUE FOCUS 44 FEED & ADDITIVE MAGAZINE January 2026 As feed manufacturers face rising variability, tighter regulations, and cost pressure, predictive quality and process optimization are becoming essential operational capabilities. By applying AI and machine learning to real-time process data, feed mills can move from reactive quality control to earlier, more informed intervention, improving stability, efficiency, and decision-making across production. Feed manufacturers are operating under increasing pressure to deliver consistent product quality while managing rising raw material variability, tighter regulatory requirements, and ongoing cost constraints. In this context, traditional quality control approaches based primarily on end-product inspection and reactive intervention are often insufficient to prevent losses linked to variability, rework, or unplanned downtime. Conventional quality management in feed production relies heavily on sampling, laboratory analysis, and statistical process control to identify deviations once they have already occurred. These methods remain essential for compliance and verification, but their retrospective nature limits their ability to prevent quality issues before they affect production. In many feed mills, quality deviations are only fully understood after production has moved on, when laboratory results confirm an issue that operators had already suspected but could not quantify in time. Advances in artificial intelligence (AI) and machine learning (ML) are enabling a shift toward predictive quality control and more proactive process optimization. Rather than replacing existing quality systems, these technologies complement established practices by analyzing process data continuously and identifying early signs of process drift before they result in quality failures or performance losses. Modern feed mills already generate significant volumes of operational data, including process parameters such as temperature, pressure, throughput, and energy consumption; equipment-related signals such as vibration and motor load; raw material characteristics including moisture content and bulk density; and quality indicators such as pellet durability index (PDI), fines percentage, and homogeneity. Historically, much of this data has been reviewed in isolation or used primarily for troubleshooting after an issue has occurred. Machine learning models are designed to evaluate these variables simultaneously rather than independently. This capability is particularly relevant in feed manufacturing, where quality outcomes are rarely driven by a single parameter. Instead, they result from the interaction between formulation, conPREDICTIVE QUALITY AND PROCESS OPTIMIZATION IN FEED MANUFACTURING André Magrini Chief Revenue Officer OGI Systems
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