What happens when feed mills encounter an equipment malfunction? Traditional maintenance models rely on periodic inspections and scheduled repairs, often identifying issues only after they disrupt operations. When equipment fails, mills must conduct diagnostic testing, implement repairs and work overtime to compensate for lost productivity.
Today’s technology offers a proactive alternative: predictive maintenance. As feed mills adopt automation and data analytics, the next step is artificial intelligence (AI)-driven predictive maintenance and goal-based control systems that enhance efficiency. By continuously monitoring factors like temperature, vibration and energy use, AI systems detect potential failures before they occur, preventing downtime and costly repairs.
According to Rockwell Automation, predictive maintenance minimizes maintenance frequency, avoids unplanned outages and reduces unnecessary preventive costs. As facilities integrate more sustainable and cost-effective solutions, predictive maintenance paves the way for a more resilient industry.
From reactivity to proactivity
Traditional maintenance is reactive — fixing problems after they happen. AI-driven predictive maintenance, however, detects subtle process anomalies that human operators might miss.
Machine learning, a subset of AI, is already optimizing industrial processes. Machine learning enables a system to monitor itself, assess performance and improve efficiency autonomously, according to Matthew Boland, territory sales manager at CPM Automation.
The feed industry is adopting predictive analytics to extend equipment life and improve reliability. By analyzing vast amounts of historical and real-time data, AI systems detect patterns and anomalies, enabling proactive maintenance scheduling.
Instead of reacting to breakdowns, mills can prevent failures, reducing downtime and improving performance.
“Predictive maintenance is like upgrading from a rearview mirror to a GPS,” said André Magrini, director of feed and food platforms at AGI. “Instead of reacting to breakdowns or replacing parts on a rigid schedule, you’re using real-time data to predict when something will actually need attention.”
Applications of predictive maintenance in feed mills
Catching wear-and-tear in equipment: Feed mills depend on machinery such as hammer mills, pellet presses and conveyors. Predictive maintenance sensors monitor real-time vibration, temperature and electrical patterns, identifying issues such as overheating motors or failing bearings. Companies like Bühler Group and AGCO offer solutions based on the internet of things (IoT) to track and address potential problems in equipment before they escalate.
For example, sensors in a grain dryer can detect temperature fluctuations signaling motor failure. Immediate intervention prevents costly downtime, especially during peak production periods.
Energy efficiency in pellet mills: Pellet production requires significant energy. Predictive analytics optimize energy use by detecting inefficiencies. If rollers in a pellet press show uneven wear, the system can suggest corrective actions, improving efficiency and reducing costs.
Data-driven decision making: AI-powered analytics enhance maintenance and operational strategies. Many feed mills store extensive data in enterprise resource planning (ERP) systems but struggle to extract actionable insights.
AI instantly analyzes real-time and historical data, providing recommendations on repair schedules and ordering parts before failures occur.
With AI-driven automation, maintenance teams transition from reactive strategies to data-driven decision-making, ensuring smoother, more reliable operations.
Challenges addressed by predictive maintenance
1. Labor shortages
As many industries continue to feel the impact of workforce challenges and rising costs of labor, feed and grain organizations are looking for ways to continue to increase their output.
“By reducing unplanned downtime and scheduling maintenance more effectively, predictive maintenance can address labor shortages and operational inefficiencies,” said Nick Malott, analytics architect at Interstates.
2. Equipment malfunction
AI-powered systems identify issues early, such as motor failures or conveyor blockages, maximizing equipment lifespan and return on investment.
“Predictive maintenance leverages real-time data to detect potential failures before they occur,” Malott said. “This minimizes downtime, reduces maintenance costs and optimizes performance.”
3. Rising expenses and budget constraints
Predictive maintenance addresses budget concerns by extending equipment life and minimizing unexpected breakdowns. Optimizing equipment health and lifespan reduces frequent capital investments in new machinery and helps ensure smoother operations during peak demand periods.
“You’re not wasting labor hours on unnecessary inspections or repairs, and you can plan your budget more effectively because you’re not constantly firefighting,” Magrini said.
4. Sustainability demands
AI-powered maintenance supports sustainability efforts by optimizing energy consumption, reducing material waste and extending equipment lifespan — helping the industry work toward net-zero targets.
The future of predictive maintenance
Predictive maintenance is evolving alongside AI and IoT advancements, transforming facility operations. AI-driven systems will autonomously flag issues, optimize settings and coordinate repairs more efficiently. Future predictive technology will also recommend prescriptive actions to resolve emerging problems.
“Over the next several years, the shift toward autonomous operations will reduce reliance on specialized personnel,” Malott says. “Maintenance systems will become more intuitive and integrated, allowing supervisors, operators and maintenance teams to run and optimize plants without requiring data scientists and engineers.”
Integrating AI with ERP and business systems will be a game-changer. Whether deployed on-site or via cloud platforms, AI will serve as an intelligent assistant, recommending preventive maintenance actions and process improvements. Goal-driven control systems will allow AI to autonomously determine the best ways to meet production targets while maximizing efficiency.
Although AI cannot completely prevent mechanical failures, it can adjust processes to maintain productivity until repairs are completed.
However, slow integration is key to successful adoption. Experts suggest starting with targeted implementations — such as monitoring a single machine — before scaling up to full-facility coordination.
“Companies need to align goals, train teams and build processes that maximize the value of predictive maintenance,” Magrini says. “Mills that embrace this shift will become more resilient and sustainable.”
Beyond maintenance: AI-driven production optimization
Predictive maintenance is just one way AI will have an impact on the feed industry. In the future, feed mills will leverage AI-driven scheduling and efficiency optimization, Boland said.
AI can automatically adjust production schedules based on historical data, real-time inventory levels and demand forecasts. For instance, if a rush order is placed, the system can instantly recalibrate schedules to accommodate new requirements while maintaining peak efficiency.
AI systems will also analyze a facility’s historical performance, ensuring seamless coordination of raw material usage, equipment efficiency, and order fulfillment. With real-time adjustments, feed mills can maximize uptime and profitability.
As AI continues to evolve, the industry will transition from reactive problem-solving to proactive, data-driven decision-making — ushering in a new era of smart manufacturing.
For companies looking to stay competitive, predictive maintenance is a valuable investment in reducing costs, improving safety and enhancing operational efficiency.
