Assoc. Prof. Secil UZEL
Hitit University
Faculty of Engineering
Dept. of Food Engineering

In today’s world—where global food demand is steadily increasing, climate change is heightening uncertainties in agricultural production, and energy resources must be utilized more sustainably—the flour industry is undergoing a new evolutionary phase. At the heart of this transformation lie Smart Milling Systems. Traditional wheat milling methods are being replaced by digital infrastructure, advanced automation systems, data-driven decision-making mechanisms supported by big data analytics, and approaches based on multivariable process optimization.

The advancement of sensor technologies now enables real-time monitoring, while the analysis of production parameters through artificial intelligence-based algorithms has rendered process control independent of manual intervention. As a result, mills are evolving beyond facilities that merely convert grain into flour—they are becoming integrated production centers that make data-driven decisions, self-optimize, and are fundamentally guided by sustainability goals.

This article will present a holistic overview of the structural impacts of digitalization on wheat milling processes, the contribution of automation systems to production efficiency, the significance of data-driven decision-making mechanisms, and the strategic advantages that process optimization can offer.

THE IMPACT OF DIGITALIZATION ON THE MILLING INDUSTRY

Digitalization has become not a choice, but a necessity for any company in the milling industry aiming to remain competitive and achieve sustainable production. Factors such as the growing global population, shifting consumer expectations, tightening food safety regulations, and rising energy costs reveal the unsustainability of traditional production methods. At this point, digitalization is setting a new industry standard by making production processes more transparent, efficient, traceable, and controllable. With the advent of Industry 4.0, flour mills have embraced this trend, incorporating digital sensors, centralized data systems, and artificial intelligence algorithms into their production lines.

Digitalization plays a critical role in milling through the following dimensions:

Real-Time Monitoring: Dynamic Quality and Process Control

One of the core components of digitalization is real-time monitoring systems, which continuously track process performance and instantly detect quality deviations. For example, during the wheat milling process, inline NIR (near-infrared) sensors continuously measure parameters such as moisture, protein, and ash content in the flour. These measurements are transmitted to a central control unit via a SCADA system, where algorithms initiate alerts or corrective actions if any values exceed the predefined limits.

As a practical application, a NIR sensor integrated into the semolina line of a roller mill can detect if the ash content exceeds the target value of 0.55%. In response, the system automatically opens the bran return valves to stabilize low-ash flour production. This intervention prevents quality losses and allows the system to self-regulate without halting the production line.

Traceability: A Digital Journey from Raw Material to Final Product

As food safety regulations tighten, traceability has become critical in milling. Digitalization enables rapid backward tracking of details such as which farm the wheat used in a specific flour bag came from, which lot numbers were processed, in which silos it was stored, and which rollers it passed through.

With RFID (Radio Frequency Identification) tagging and lot number tracking, if contamination is detected in a flour sample, the system can quickly identify which shipment it was part of, which customers received it, and the raw material it originated from—allowing for immediate and targeted recalls. This significantly improves the prevention and management of food safety crises.

Decision Support Systems: Reducing Operator Dependency

Decision support systems are software-based solutions that analyze data from the production line and offer recommendations or take automatic actions. Especially where operator experience is limited, these systems help maintain process stability and product quality.

An AI module that collects data on motor speeds, airflow pressure, sieve vibration frequency, and roller gaps can analyze historical production data to identify which parameter combinations yield the highest flour output and lowest energy consumption. The system then either presents this optimization as recommendations on the operator panel or applies them automatically.

POTENTIAL CHALLENGES AND DOWNSIDES OF DIGITALIZATION

Despite its many advantages, digital transformation in the milling sector faces several structural and operational challenges:

• High Initial Investment Costs: Installing digital infrastructure, integrating sensors and software, and upgrading SCADA/PLC systems require significant investment, which can be discouraging for small and medium-sized mills.
• Lack of Skilled Personnel: There is a limited number of technical staff capable of managing automation systems, interpreting data analytics, and intervening in digital systems. This shortage can hinder the effective implementation of digitalization.
• Data Security Risks: Reliance on digital infrastructure exposes systems to cybersecurity threats. In particular, remote-access systems can be vulnerable to cyberattacks that may halt production or result in data loss.
• Resistance and Adaptation Issues: In traditional operations, resistance to digital systems or difficulties in staff adaptation are common. This prevents the full realization of the systems’ potential.

Considering all these factors, it is clear that digitalization is inevitable for the milling industry. However, this transformation cannot be achieved through technological investments alone. It also requires restructuring organizational systems, training employees, and redesigning processes with a data-driven mindset. When properly planned, digitalization can deliver significant benefits across various areas of milling—from quality assurance systems to resource optimization.

INTEGRATION OF AUTOMATION SYSTEMS

Modern wheat milling has evolved beyond a purely mechanical production approach. Today, flour production lines are equipped with fully integrated automation systems, enabling centralized control of production, quality assurance, energy management, and maintenance operations. At the core of this automation framework are PLC (Programmable Logic Controllers) and SCADA (Supervisory Control and Data Acquisition) systems. Working in tandem, these systems ensure both process control and real-time monitoring and analysis within the mill.

PLC Systems: The Brain of the Process

PLC systems are programmable industrial computers that automatically manage the machinery and processes within the mill. They are typically responsible for real-time tasks such as motor control, roller gap adjustments, sieve vibration frequencies, bran return flows, and pneumatic valve transitions.

Advantages:

• Fast and consistent response time: Can execute commands within milliseconds.
• Durability: Operates reliably in dusty, humid, or high-vibration environments.
• Flexibility: Easily reprogrammable to accommodate process changes.
• Automation: Reduces the need for manual intervention by enabling autonomous control of operations.

Disadvantages:

• High software development and maintenance costs.
• System-wide failure risk if hardware malfunctions.
• Susceptible to human error: Incorrect software updates may lead to critical issues.

SCADA Systems: The Eyes of the Entire Process

SCADA systems collect, visualize, and record data from processes controlled by PLCs through a centralized interface. Real-time graphs, alarms, production reports, and trend analyses are all managed through SCADA platforms.

Advantages:

• Real-time monitoring: Operators can instantly observe production parameters and respond quickly to deviations.
• Data logging and reporting: Detailed records of production history, faults, and energy consumption are maintained.
• Remote access: Allows operators or managers to access system data from mobile devices or control centers without being physically present at the facility.
• Alarm and warning systems: Visual and audio alerts assist in rapid intervention when critical parameters deviate from defined limits.

Disadvantages:

High setup and licensing costs.

• Cybersecurity risks: Remote-access capabilities may expose the system to malicious attacks.
• Data overload: Transforming raw data into actionable insights requires expert interpretation.

The integration of automation systems has brought a revolutionary transformation to the milling industry in terms of both quality and efficiency. PLCs ensure that processes are executed safely, consistently, and quickly, while SCADA systems enable monitoring, analysis, and continuous improvement. The synergy between these two systems helps minimize operator errors, reduce production losses, and enhance traceability.

However, to sustain the benefits offered by these technologies, proper installation, regular maintenance, and comprehensive personnel training are essential. Additional considerations such as cybersecurity, energy management, and the development of user-friendly interfaces must also be addressed. Ultimately, automation systems represent more than just a technological investment—they are part of a broader organizational transformation. Companies that successfully navigate this shift will gain both a competitive edge and long-term sustainability.

Data-Driven Decision-Making Mechanisms: The Role of Intelligent Algorithms

Data has become one of the most valuable resources in modern industrial production. As in the energy, automotive, and pharmaceutical sectors, data-driven decision-making mechanisms in the milling industry play a strategic role in achieving operational efficiency, quality control, cost management, and sustainability goals. Data that is not transformed into knowledge remains a raw and idle asset. Therefore, the foundation of the “smart milling” concept lies not only in collecting data but in meaningfully analyzing and integrating it into decision-making processes.

Data enables businesses not only to monitor their production processes but also to predict, optimize, and automate them. This is especially critical in milling, where process variability (due to raw material quality, environmental conditions, or operator experience) requires analytical precision beyond human perception or intuition. With data-driven systems, deviations can be detected before they occur, waste rates can be reduced, energy consumption optimized, and product quality consistently maintained.

In milling, data is collected from a wide variety of sources and across multiple layers. The main data categories include:

• Raw Material Data: Physical and chemical characteristics of wheat such as protein, gluten, moisture, ash content, hectoliter weight, and falling number.
• Process Data: Operational parameters like motor speeds, air pressures, sieve vibration frequencies, temperature readings, roller gaps, and tempering duration.
• Product Quality Data: Indicators such as final flour’s protein content, water absorption, viscosity, color, and particle size.
• Energy and Efficiency Data: kWh per ton, production time, loss rates, waste amounts, equipment faults, and downtimes.
• Supply Chain and Operational Data: Stock levels, batch number tracking, shipment details, maintenance records, and more.

Real-time collection and analysis of these data points form the foundation of intelligent decision-making mechanisms. These mechanisms operate on three main analytical levels:

1. Descriptive Analytics – What happened?

• Visualization of historical production data through graphs.
• Periodic comparisons of production logs and quality analysis results.

2. Predictive Analytics – What will happen?

• AI-supported algorithms can predict the quality outcomes of specific wheat batches.
• Trend analyses allow for early detection of potential faults or quality deviations.

3. Prescriptive Analytics – What should I do?

• Systems that recommend optimal blending ratios.
• Automated suggestions—or direct implementation—of process settings such as roller gap or air pressure.

For example, when a batch of wheat with low protein content is received, the system may suggest blending it with the most suitable high-protein batch based on historical data. It can also recommend how to adjust roller settings to improve flour’s water absorption capacity—or apply those adjustments automatically. Furthermore, the system records post-production quality data, contributing to a continuous learning cycle.

Advantages of These Applications

• Process Stability: Enables consistent product quality independent of operator variability.
• Quality Forecasting: Provides insights into quality metrics before laboratory tests are completed.
• Waste Reduction: Early fault detection helps prevent contamination, over-grinding, or energy loss.
• Time and Energy Savings: Optimized processes reduce both production duration and energy use.
• Knowledge Transfer: Veteran expertise can be embedded into data models and passed to new staff.

Limitations and Challenges

•  Data Quality and Consistency: Poor sensor calibration can lead to misleading data.
•  High Infrastructure Costs: Significant investment is needed for data collection, storage, and processing systems.
•  Data Overload and Management: Extracting meaningful insights from large volumes of data requires time and expertise.
•  Human-Machine Interaction: Decision systems must be well understood and properly used by operators, requiring comprehensive training.
•  Cybersecurity: Cloud-based storage systems need protection against malicious attacks.

Data-driven decision-making mechanisms lie at the heart of the milling industry’s digital transformation. With advanced sensor infrastructure, machine learning algorithms, and AI-supported systems, it is now possible not only to control processes, but to understand, predict, and optimize them. However, the success of these systems relies not only on technological tools but also on the quality of data, operator training, management vision, and strategic integration capacity.

The mills of the future will be managed not only by machines but by data. Therefore, data literacy must become a core competence not only for IT professionals but for all members of the production team.

PROCESS OPTIMIZATION: DOING MORE WITH LESS

In industrial production, the efficient use of resources is not only a means to reduce costs but also a strategic necessity for ensuring environmental sustainability and consistent product quality. In milling, “process optimization” refers to the efficient management of energy, time, labor, and raw materials in flour production. The primary goal of this approach is to produce higher-quality output with the same input or maintain the same quality using fewer resources. With rising energy prices, raw material fluctuations, and increasingly stringent quality demands, process optimization has become a key factor in staying competitive.

Process optimization enhances both economic and environmental performance by enabling more efficient resource use in the milling industry. Reducing energy consumption, minimizing flour loss, and decreasing waste rates all contribute to increased resource efficiency. At the same time, consistent product quality can be maintained despite variable raw material characteristics, ensuring quality stability. Moreover, by improving existing production lines, capacity can be increased without the need for new investments, helping reduce operational costs. Additionally, by cutting down on energy and water use, mills can directly contribute to their environmental sustainability goals by lowering their carbon footprint.

Critical Points: Key Considerations in the Optimization Process

• Monitoring Raw Material Quality: Optimization is only effective when the physical and chemical properties of the raw material (wheat) are thoroughly analyzed. Wheat quality should be continuously monitored using online or offline analysis systems.
• Control of Process Variables: Parameters such as roller gaps, sieve vibrations, pneumatic air flow, and tempering duration must be constantly measured and statistically analyzed.
• Energy Efficiency: Motor power consumption, pneumatic line pressures, and mechanical friction must be examined to achieve maximum production with minimal energy use.
• Time Management: Grinding time, line downtimes, maintenance planning, and product changeover times should be optimized.
• Data-Driven Feedback: A digital infrastructure is required to allow automatic adjustments based on system data.

Common Application Areas

 Quality-Oriented Optimization with Inline Measurement Systems:

Near-infrared (NIR) sensors measure ash, protein, and moisture values in the flour in real time along the production line. Based on the data, flour flow is redirected, blending ratios adjusted, or roller pressure optimized.

 Energy Consumption Analysis:

The energy consumption of each machine is monitored. Machines with excessive energy use are flagged for maintenance, or process parameters are adjusted to reduce consumption. For instance, reducing the speed of roller motors by 5% can save up to 7% in energy consumption.

Product Flow Management and Automated Diverter Systems:

By preventing unnecessary blockages during product transfer, continuous production flow is maintained. Diverter and dosing systems allow blending of different flours without compromising quality.

Automatic Roller Adjustment Systems:

The gap between roller cylinders is automatically adjusted based on the product type. These systems help standardize product quality while reducing the risk of mechanical failure.

Statistical Process Control (SPC):

Deviation values of process parameters are tracked, and an early warning system is triggered when values fall outside upper or lower control limits.

Process optimization in milling is not merely an operational improvement—it is a strategic tool for competitive advantage. Today, producing at high capacity alone is not sufficient; what matters is doing so in a sustainable, traceable, energy-efficient, and quality-assured manner. The success of optimization is directly linked to measurability, digitalization, and data analytics. However, alongside technical infrastructure, operator training, implementation of decision support systems, and dynamic process monitoring are just as vital.

A well-structured process optimization program not only reduces production costs but also enhances customer satisfaction, environmental performance, and brand reliability. Therefore, the motto “doing more with less” should be a core principle of the mills of the future.

MILLS OF THE FUTURE: AUTONOMOUS SYSTEMS AND AI-POWERED MILLING

Over the past two decades, the milling industry has transformed from a mechanically driven production model to one increasingly governed by digital and integrated systems. In the past, the milling process relied almost entirely on operator experience and manual interventions. Today, thanks to automation, data analytics, and digital control technologies, production has become more controlled, measurable, and traceable. However, this progress still operates at a “semi-autonomous” level. The mills of the future will go beyond automation—embracing autonomous systems capable of learning, forecasting, and making decisions independently.

The next generation of mills will be shaped by the integration of artificial intelligence (AI), machine learning, the Internet of Things (IoT), and cyber-physical systems. In these smart mills:

Autonomous Milling Decisions: Based on the physical and chemical properties of the incoming wheat, the system will autonomously determine blending ratios, roller gaps, and sifting frequencies—constantly adjusting itself without human intervention.

AI-Powered Product Quality Forecasting: By analyzing input wheat characteristics and environmental factors (e.g., temperature, humidity, silo fill levels), the system will predict flour properties such as protein content, water absorption, and gluten quality in advance, allowing proactive adjustment of the production process.

Machine Learning for Process Enhancements: By analyzing large datasets—such as historical breakdowns, quality deviations, and efficiency metrics—the system will continuously update its algorithms to make better decisions in future operations.

Predictive Maintenance and Failure Detection: Continuous monitoring of vibration, temperature, and power data will enable systems to predict equipment failures before they occur and generate automated maintenance schedules accordingly.

Carbon and Water Footprint Monitoring: After each production batch, the system will calculate the environmental impact and adjust process parameters to align with sustainability targets.

Autonomous Logistics and Storage Systems: Tasks such as transferring products from silos to packaging lines and preparing shipments will be handled by AGVs (Automated Guided Vehicles) and robotic arms.

The mills of the future will not merely consist of automated production lines; they will be intelligent, adaptive systems capable of self-learning, self-regulation, and environmental impact management. This transformation will redefine traditional milling roles—requiring “process managers” with strong digital skills and data literacy instead of conventional operators.

Moreover, this evolution will usher in a new era not just of enhanced efficiency and product quality, but also of sustainability and corporate responsibility. Milling operations that fail to adapt to this shift risk falling behind—not only in terms of technology but also in competitiveness and reputation. Therefore, a well-planned integration of digitalization and artificial intelligence today will form the cornerstone of tomorrow’s milling vision.

Smart milling systems are playing a critical role in helping the wheat milling industry meet its goals for efficiency, quality, and sustainability. Digitalization, automation, and data analytics not only reduce human error but also enable more predictive and flexible production systems. However, the effective implementation of these technologies requires a well-structured digital transformation strategy, robust infrastructure investments, and a qualified workforce.

The future will belong not to mills that simply grind more wheat, but to those that grind more intelligently—prioritizing environmental and economic sustainability. Companies that act early in this transformation will not only gain a competitive advantage but will also contribute to global objectives such as food security, energy efficiency, and carbon footprint reduction.