by Bea Van Deynse, Marketing Communication Manager, Bestmix Software, Belgium
Walk into a modern petfood factory, and you’ll see what looks like controlled chaos: massive extruders pushing out thousands of kibbles per minute, coating systems applying precise palatants, dryers running around the clock. Behind the scenes, dozens of variables are constantly shifting, including ingredient moisture levels, ambient temperature, equipment wear and operator expertise, all affecting whether the food meets the quality standards our pets deserve.
For decades, keeping this process running smoothly has depended on experienced operators who develop an intuitive feel for when something’s about to go wrong. But what happens when that veteran calls in sick? Or when subtle ingredient changes create problems that even experts don’t catch until it’s too late?
This is where artificial intelligence is starting to make a real difference, not as a futuristic concept, but as a practical tool that solves everyday production challenges right now.
What AI actually means in a petfood factory
Let’s cut through the hype. AI in petfood manufacturing isn’t about robots taking over or computers making all the decisions. It’s much simpler and more useful than that.
Think of it as pattern recognition on steroids. Modern production lines generate massive amounts of data: temperatures, pressures, speeds, moisture levels, energy consumption. Humans are excellent at recognising problems and making nuanced decisions, while AI excels at continuously monitoring dozens of variables simultaneously and spotting subtle patterns. Together, they make a powerful team.
Machine learning systems analyse thousands of production runs to identify relationships between process conditions and final outcomes. Once trained, these
systems can provide early warnings such as: “Based on current conditions, there’s a 78 percent chance the next batch will have density problems. Here’s what you should adjust.”
The key difference from traditional quality control? Traditional methods are reactive — you make the product, test it, then fix problems. AI enables proactive quality assurance by catching issues before they happen.
Managing consistency amid variability
Here’s a problem every petfood manufacturer face: You’ve perfected a recipe in your pilot plant. It meets nutritional specs, pets love it, everything looks great. Then you scale up to commercial production and suddenly consistency becomes a challenge.
Why? Because commercial production is messier than pilot plants. Ingredient moisture varies with the season. Different operators have different styles. Equipment behaves differently at 2 am than 2 pm.
AI systems excel at handling this complexity. By continuously analysing process data and predicting likely outcomes, they can recommend real-time adjustments like: “The chicken meal in today’s batch is running 2 percent higher moisture than usual. To maintain target density, increase barrel temperature by 3°C and reduce screw speed by 15 RPM.”
Manufacturers using these systems report up to 43 percent less production time wasted on quality issues and 50 percent reduction in nutrient variability batch-to-batch. That consistency matters—not just for meeting specs, but for the pets eating the food every day.
Making formulation smarter
Most petfood companies produce multiple products. When ingredient costs spike or availability gets tight, how do you adjust all those formulations efficiently?
Traditional approach: reformulate each product individually to minimise cost. But this misses bigger opportunities. Maybe you should reduce an expensive ingredient more in Product A (where it’s less critical) to preserve levels in Product B (where it really matters).
AI-powered multi-blend optimisation looks at your entire portfolio simultaneously. When fish meal prices jump, the system determines the best way to adjust all your formulations together—minimising total cost while maintaining nutritional standards across all products. Some companies are saving hundreds of thousands of dollars annually just by making smarter decisions about ingredient allocation.
Identifying equipment issues sooner
Equipment breakdowns are expensive: unplanned downtime, wasted product, emergency repairs, missed deliveries. Traditional preventive maintenance on fixed schedules means you’re either maintaining equipment too often (wasting resources) or not often enough (missing developing problems).
AI-based predictive maintenance changes the game. By continuously monitoring equipment sensor data, machine learning systems detect early warning signs humans would miss: subtle changes in vibration patterns, gradual shifts in power
consumption, small temperature variations. These early warnings enable fixing small problems before they become big ones.
A look at one implementation approach
One example of these technologies in practice is Bestmix Insights, a web-based platform designed to connect with production lines and analyse parameters such as moisture, pressure, temperature and energy use. Developed for use by production teams rather than data specialists, the system provides operators with clear indicators of process stability and suggests adjustments when necessary.
Early adopters have reported improvements such as up to 43 percent reduced time lost to quality issues, up to 50 percent lower nutrition swing between batches and annual savings up to US$500,000 from reduced waste and improved efficiency.
The system learns from the best operators and makes that knowledge available to everyone. New operators perform better, faster. Night shift produces consistent quality matching day shift.
By learning which ingredient combinations and process settings work best in production, the system feeds insights back to formulation teams. Nutritionists can design recipes knowing not just what’s nutritionally optimal, but what actually produces consistent quality on your specific production lines.
Why this matters now
The petfood market is growing, projected at 5.25 percent annually through 2032, but it is also becoming more competitive. Premium ingredients, grain-free formulas and health-focused products used to be differentiators. Now they’re table stakes.
When products are similar, operational excellence becomes the competitive edge. The manufacturers who can consistently deliver quality at lower cost, respond quickly to market changes and innovate faster will win.
AI enables this kind of operational excellence. It’s not magic—it’s systematic application of data to find efficiencies and improvements that human observation alone would miss.
The democratisation of these technologies matters too. Ten years ago, AI required massive investment in specialised expertise and infrastructure. Today, cloud-based platforms make sophisticated capabilities accessible to mid-sized manufacturers.
Getting started: Practical first steps
If you’re intrigued but unsure where to begin, here’s practical advice:
· Start specific, not comprehensive. Don’t try to transform everything at once. Pick one clear problem: maybe quality variability on your largest production line. Solve that specific problem first. Early wins build momentum.
· You don’t need perfect data to start. Many manufacturers delay AI projects waiting for ideal data infrastructure. That’s backwards. Start with the data you have. Learn what works. Build infrastructure incrementally as you prove value.
· Focus on helping people, not replacing them. The facilities seeing best results use AI to make operators more effective, not to eliminate them. Invest in training.
Create clear guidelines for when to follow AI recommendations and when human judgment should override them.
· Partner wisely. Unless you’re a very large organisation, building AI capabilities entirely in-house probably doesn’t make sense. Partner with providers who understand petfood manufacturing specifically. Bestmix Software welcomes open conversations about how these technologies can work for your operation.
What’s coming next
Current AI applications focus on production optimisation and quality control. The next frontier involves personalised nutrition. Smart feeders and pet health monitors providing continuous data could enable AI systems to develop increasingly personalised nutritional recommendations—not just for breed or age, but for individual pets.
Environmental sustainability represents another emerging application. By integrating life-cycle assessment databases with formulation systems, AI can help optimise recipes not just for nutrition and cost, but also for environmental impact.
The bottom line
Artificial intelligence in petfood manufacturing isn’t science fiction. It’s practical technology solving real problems today. Facilities implementing these systems are seeing measurable improvements in quality consistency, operational efficiency and cost management.
More importantly, they’re building organisational capabilities that create sustainable competitive advantages. In an industry where product differentiation is increasingly difficult, operational excellence through data-driven decision-making may be one of the last remaining ways to truly pull ahead.
The manufacturers who invest in the data infrastructure, train their teams, foster cultures that embrace evidence over intuition and partner strategically with the right technology providers will be best positioned for success.
The future of petfood manufacturing is intelligent, adaptive and continuously learning. The good news? That future is accessible starting today, with practical applications delivering measurable value right now.