TECHNOLOGY 86 FEED & ADDITIVE MAGAZINE July 2025 • 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. ShutterStock | Mehmet Dinler
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