Animal feed manufacturers have made major strides in automation and quality control, yet the industry continues to operate with structural constraints that limit efficiency, precision, and profitability.
The world feed mills often run well below their capacity, struggle to incorporate real-time data into decision-making, and rely on a batch-based production model that no longer fits the needs of precision livestock systems. These challenges define the “hidden inefficiencies” of today’s feed industry and present clear opportunities for digital transformation.
Ingredient variability and the constraints of batch-based formulation
The most fundamental limitation in feed manufacturing is variability, both in raw ingredients and in finished feed. Most mills still rely on periodic sampling or book-value nutrient matrices, making it difficult to capture in real time the true nutrient value of incoming grain, DDGS, or co-products. This uncertainty forces nutritionists to over-formulate to protect performance, increasing costs and environmental footprint.
These inaccuracies are compounded by the industry’s batch-based production model. Starter, grower, and finisher diets are manufactured in large production runs, even though pigs, poultry, and fish require different nutrients for different situations, influenced by genetics, health status, and environmental conditions. Mills are not structurally designed to produce personalised or adaptive diets, and silo storage limitations restrict the number of rations a mill can make in a single day. As a result, feed is often “good enough” rather than precisely matched to the needs of the animals consuming it.
Hidden inefficiencies—not machines—hold feed mills back.
Why feed mill capacity is underutilised
Although modern mills are engineered to run 24/7, most operate at only 50–70% of their theoretical annual throughput. Several factors drive this underutilisation:
- Inefficient scheduling and changeover times: Batching sequence, pellet die changes, and cleanouts create bottlenecks that reduce output.
- Ingredient supply disruptions: Variability in deliveries or quality issues halt production.
- Unpredictable on-farm consumption: Without accurate on farm silo monitoring, mills must build buffer inventory or run short, complicating scheduling.
- Labour shortages: Insufficient or inexperienced operators slow troubleshooting and limit second- or third-shift capacity.
- Mechanical downtime: Most mills lack predictive maintenance tools to prevent costly stoppages.
This gap between engineered capacity and real-world output represents one of the industry’s greatest untapped efficiencies.
Industry-level barriers: fragmented data, ageing infrastructure, and traceability gaps
Sector-wide, feed manufacturing remains constrained by fragmented data systems, uneven technology adoption, and limited traceability. Farm performance data rarely flows back to the mill in real time; many facilities still rely on partially digitised quality systems; and smaller mills lack capital to upgrade ageing infrastructure. These structural gaps restrict innovation and slow the shift toward precision nutrition.
How AI and digital technologies can solve these issues
Digitalisation is uniquely suited to overcome the feed industry’s current limitations:
- Inline NIR and real-time QC: AI models can adjust formulations automatically based on true nutrient profiles, reducing over-formulation.
- Predictive maintenance and process optimisation: Machine learning can anticipate equipment failures, optimise pelleting conditions, and reduce changeover losses—boosting effective capacity.
- Logistics automation: Silo sensors (e.g., Binsentry, Barn Tools, Distynct) and AI-driven routing can eliminate emergency deliveries and stabilise production schedules.
- Closed-loop precision nutrition: As farms adopt vision systems and precision feeders, growth data can flow to formulation platforms, enabling adaptive diets and smaller, more frequent batches.
- Digital twins: Virtual models of mills allow optimisation of layout, energy use, throughput, and contamination risk before changes are made.
True feed mill progress begins where hidden inefficiencies end.
Unlocking the future feed mill
The limitations facing today’s feed industry are not technological but structural. AI, real-time analytics, and integrated digital ecosystems can transform feed mills from batch-based commodity producers into dynamic, data-driven engines of precision livestock agriculture, seeing underutilised capacity is not a constraint but an opportunity. Digital technologies are capable of radically changing the business of making feed. Are nutritionists, managers and the feed industry ready?
