Ali Magboul
CEO of ASM Process Automation
AI has dominated industry talk—but milling needs prediction, not hype. Real value comes only when AI is built on automation, a strong OT network, one validated PLC-based data source, and fully automated clean data—so mills can anticipate failures, quality drift, energy use, and bottlenecks before they hit throughput.
For the past few years, one word has dominated conferences, boardrooms, and industry discussions: AI. We hear it everywhere — “Add AI to it,” “Buy an AI agent,” “Use AI to solve the problem.” Yet in milling operations, the real challenge is not whether AI is important, but how to apply it in a way that delivers measurable value. AI should not be treated as a fashionable add-on. In flour and feed mills, AI only becomes powerful when it is built on the right foundations: automation, connectivity, digitalization, and clean data.
AI in Milling: Start with Prediction
In milling, the most practical and impactful use of AI today is prediction. Prediction of:
- Equipment performance (rollers, elevators, conveyors)
- Quality deviations (protein, ash, moisture trends)
- Energy consumption per ton
- Maintenance needs before failures occur
- Production bottlenecks before they impact throughput
This is where AI truly helps millers move from reacting to anticipating. Instead of asking “Why did this happen?”, AI enables the question “What will happen next, and how do we prevent it?” But prediction cannot exist in isolation.
The First Pillar: A Strong OT Network
OT stands for Operational Technology; the systems that directly monitor and control physical processes in the plant. In a milling environment, OT includes:
- PLCs controlling rollers, sifters, purifiers, elevators, mixers
- VFDs regulating motor speeds
- Weighing systems for batching and load-out
- Sensors for level, temperature, vibration, and flow
A strong OT network ensures that all these systems communicate reliably, securely, and in real time. Without this foundation, any digital or AI initiative will be fragile. For example:
- If elevator motor data is unstable or delayed, AI cannot accurately predict bearing failures.
- If batching weights are not reliably captured, AI cannot optimize feed formulations.
OT is not optional; it is the backbone of modern milling.
Connectivity: One Brain, Not Many Islands
One of the most common mistakes in modern plants is creating parallel networks. Adding IoT sensors that run on Bluetooth or Wi-Fi, while leaving the main PLC (the plant brain) out of the picture, creates data silos. Even worse, it creates multiple “truths” in the same plant. If a mill already has a PLC and most modern mills do, then everything must report through one brain.
- All sensors should feed the PLC
- All calculations should reference PLC data
- All higher-level systems should consume a single, validated data source
This approach ensures:
- Consistent data
- Easier troubleshooting
- Scalable digitalization
- Reliable AI outcomes
Disconnected IoT devices may look innovative, but they weaken the overall system if not properly integrated.
Digitalization: Turning Milling Data into Insight
Digitalization is not about dashboards alone. It is about context. In milling, digitalization connects:
- Production data (tons/hour, extraction rate)
- Quality data (protein, ash, moisture)
- Energy data (kWh/ton)
- Maintenance data (run hours, alarms, stoppages)
Once structured correctly, this data feeds dashboards with KPIs that actually matter to millers, such as:
- Yield efficiency per wheat blend
- Energy consumption per milling section
- Downtime causes by equipment type
- Batch accuracy and deviation trends
These dashboards are not the end goal, they are the gateway to AI.
Why AI Needs Fully Automated Data
AI in milling only works when data is fully automated. No manual inputs. No assumptions. No estimated values. If production quantities, quality readings, or downtime reasons are entered manually, AI predictions become distorted. The result is misleading insights and wrong decisions. For example:
- Manual quality adjustments hide real process drift
- Estimated downtime masks mechanical issues
- Assumed production rates corrupt energy KPIs
AI depends on truthful, continuous, and real-time data — and that only comes from a properly automated system.
Standards Matter More Than Custom Code
Another critical aspect is standardization. Automation systems built purely on custom logic may work locally, but they limit future connectivity and AI integration. Milling plants must follow industrial communication standards to remain future-ready. Standard protocols enable:
- Easier system expansion
- Integration with third-party analytics
- Long-term maintainability
- Secure and scalable AI deployment
Without standards, even the best AI model will struggle to connect reliably.
AI as the Final Layer — Not the First
In milling, AI should always be the last layer, not the starting point. The correct sequence is clear:
1. Automation — stable PLC-based control
2. OT Network — secure and reliable communication
3. Connectivity — one unified data source
4. Digitalization — KPIs and dashboards
5. AI Modules — prediction, optimization, decision support
When this structure is respected, AI becomes a powerful ally for millers — improving efficiency, reducing waste, saving energy, and enhancing quality consistency.
Conclusion
AI will not replace milling expertise. But when built on solid automation and real data, it amplifies it. The future of milling is not about talking more about AI — it is about using AI correctly, starting with prediction, grounded in automation, and driven by reliable OT systems. That is where real value begins.
Article Credits: Miller Magazine From talking about AI to using it: Why milling needs prediction, not hype | Miller Magazine