Predictive maintenance for feed mills leverages Artificial Intelligence and Internet of Things to move beyond reactive repairs, enabling early fault detection, improved energy efficiency, and optimized machine performance. With autonomous systems on the horizon, these technologies offer real-time insights and strategic decision-making. But how can feed mills begin implementation while overcoming integration and environmental challenges?
Head of Research Centre for Feed Technology
Norwegian University of Life Sciences
In today’s rapidly evolving feed manufacturing industry, where operational efficiency and sustainability are essential, digital transformation is reshaping how facilities manage their assets. At the forefront of this shift is predictive maintenance for feed mills, a forward-looking strategy powered by artificial intelligence (AI) and the Internet of Things (IoT). This approach goes beyond traditional maintenance by enabling feed mills to anticipate issues, optimize performance, and build more resilient operations.
SMARTER MONITORING FOR COMPLEX EQUIPMENT
Feed mills operate a wide range of machinery such as conveyors, pumps, hammer mills, mixers, pellet presses, extruders, and expanders, all of which are subject to continuous mechanical stress. Historically, maintenance was reactive, with teams responding only after equipment failed. Today, embedded sensors collect real-time data on temperature, vibration, and electrical activity. AI algorithms analyze this data to detect subtle deviations like increased motor heat or irregular shaft movement, that may indicate early-stage faults. This allows maintenance teams to act proactively, reducing downtime and maintaining consistent production output.
BOOSTING ENERGY EFFICIENCY IN PELLETING AND EXTRUSION
Pelleting, extrusion, and expanding are among the most energy-intensive processes in feed production. Wear or misalignment in components such as rollers, dies, or screw elements can lead to inefficiencies that are difficult to detect manually. AI-powered analytics continuously monitor equipment performance, identifying patterns that suggest energy waste or mechanical imbalance. When anomalies are detected, the system can recommend timely interventions such as recalibration or part replacement helping to reduce energy consumption, extend equipment life, and lower operational costs.
TRANSFORMING DATA INTO STRATEGIC INSIGHTS
Modern feed mills generate large volumes of data, often stored in Enterprise Resource Planning (ERP) systems. Without intelligent tools, much of this data remains underutilized. AI bridges this gap by analyzing both historical and real-time data to forecast equipment failures, optimize maintenance schedules, and streamline operations. When integrated with ERP platforms, AI can automate spare parts procurement, simulate maintenance scenarios, and support data-driven decision-making that enhances reliability and efficiency.
INDUSTRY EXAMPLES: PRACTICAL AI INTEGRATION
Several companies are already demonstrating how AI can be effectively integrated into feed and pet food production:
• AGI SureTrack: This system monitors critical equipment like pellet presses. When early signs of wear, such as bearing degradation are detected, it alerts the maintenance team and checks inventory levels. If the required part is low in stock, the system automatically triggers a reorder through the ERP, ensuring timely delivery and preventing unplanned downtime.
• Bühler Insights: In pet food manufacturing, Bühler’s AI platform monitors energy usage in extruders. If abnormal consumption is detected, the system compares it with historical maintenance data, schedules a service window, and updates the ERP system to adjust production planning and spare parts logistics accordingly. The Center for Feed Technology at the Norwegian University of Life Sciences is trying to integrate AI technology into the moisture control and management with small steps (Figure 1).
SOLVING KEY INDUSTRY CHALLENGES
Predictive maintenance addresses several persistent challenges in the feed and grain sector:
• Labor shortages: Automated monitoring reduces reliance on manual inspections, easing the burden on limited technical staff.
• Asset longevity: Early detection of wear extends the lifespan of expensive machinery and improves return on investment.
• Cost control: Proactive maintenance planning helps avoid emergency repairs and supports more predictable budgeting.
• Sustainability: Improved energy efficiency and reduced waste contribute to environmental goals and regulatory compliance.
TOWARD AUTONOMOUS MAINTENANCE SYSTEMS
As AI and IoT technologies continue to evolve, predictive maintenance for feed mills is becoming increasingly autonomous. Future systems will not only detect potential issues but also recommend and, in some cases, execute corrective actions. These platforms will be capable of adjusting machine parameters, coordinating service schedules, and optimizing production in real time. Cloud-based integration will further enhance these capabilities, enabling AI to function as a virtual assistant that continuously monitors performance and suggests improvements. Most implementations begin with a single high-value asset and gradually expand to full-facility integration as teams build confidence and supporting processes.
BEYOND MAINTENANCE: AI AS A STRATEGIC ASSET
Predictive maintenance is just one of many applications of AI in feed manufacturing. Looking ahead, AI will play a central role in optimizing production schedules, managing inventory, and improving supply chain coordination. By analyzing trends, inventory levels, and demand forecasts, AI can dynamically adjust production plans. For example, if a sudden order is received, the system can reconfigure operations to meet the demand without compromising efficiency. This marks a shift from reactive management to proactive, data-driven decision-making.
IMPLEMENTATION CONSIDERATIONS
Despite its advantages, implementing predictive maintenance for feed mills presents real-world challenges. Integrating AI with legacy systems may require custom solutions and cybersecurity enhancements. Initial investments in sensors, cloud infrastructure, and workforce training can be significant, especially for smaller operations. Environmental factors such as dust and humidity can affect sensor accuracy, and not all failures are predictable. While AI can recommend actions, fully autonomous systems remain rare in feed manufacturing, where human oversight is still essential due to operational variability and safety requirements.
About Dr. Dejan Miladinovic
The head of the Centre for Feed Technology at the Norwegian University of Life Sciences, Dr. Dejan Miladinovic has been working at the university since 2005, teaching courses at both, M.Sc. and Ph.D. levels related to feed and food technology. He has held various job positions at the Norwegian University of Life Sciences with overall topics related to feed technology, moisture management, innovation and new product development, novel raw materials, and rheological aspects of novel feed ingredients.
Miladinovic holds a PhD in science and technology, obtained at the Department for Mathematical Sciences and Technology, at the Norwegian University of Life Sciences. He also obtained his M.Sc. in Feed Manufacturing Technology from the NMBU in 2005 as well as an M.Sc. in Innovation and Entrepreneurship from the University of Oslo in 2009.
With dozens of scientific articles published and presented at various conferences, Dr. Dejan Miladinovic has considerably contributed the feed science and technology. Currently, he does research related to feed moisture management and characterization of single-cell-protein ingredients. Miladinovic lives in Oslo, Norway.
