What Are Operational Technologies?
In industrial facilities, there is an immense flow of data often invisible yet continuously influencing every moment of production. This ocean of data, generated from sensors, machines, process control systems, and even operator inputs, can easily turn into mere “noise” if not properly managed. This is exactly where Operational Technologies come into play, transforming scattered data streams into meaningful information through advanced systems and establishing a technology driven management culture across all layers of production.
The role of Operational Technologies begins with collecting all data generated in the field. From sensors on production lines to equipment performance logs, energy consumption metrics, quality results, and more data continuously flows into the system. However, the process does not rely solely on field data; user-generated inputs such as Occupational Health and Safety(OHS) reports, operator entries, forms, and audit records also form a critical part of the ecosystem. All these data streams are consolidated, aligned, and transformed into a holistic structure, providing a unified and accurate view of production.
Once the data has been collected, the process of creating real value begins. Operational Technologies do more than just store data; they organize, cleanse, categorize, and present it in a clear and actionable way through dashboards and reports. This enables different departments to monitor current operations, managers to track performance, and teams to identify improvement opportunities.
However, the process does not end there. The collected data serves as the foundation for a broader transformation. Operational Technologies leverage this data to develop advanced analytics solutions such as process optimization, predictive maintenance, digital twin modeling, and AI-driven quality control. Each initiative is grounded in real field data, helping organizations anticipate future needs. As a result, data evolves from a recorded output into a strategic input that shapes the future.
All these efforts establish a structure that monitors the pulse of production, anticipates issues before they occur, and enables smarter processes. In this sense, Operational Technologies act as the “digital brain” of the facility.
This journey starts with data collection, deepens through interpretation, and is ultimately completed through projects that drive the digitalization vision of the organization. The Operational Technologies Department plays a critical role in sustaining this transformation by ensuring system integration, data standardization, dashboard development, early warning mechanisms, and continuous improvement.
These initiatives not only address today’s needs but also build a smarter, more proactive, and data-driven production culture aligned with the future of manufacturing. In this context, Operational Technologies serve as a strategic technology partner driving transformation in production processes.
Process Optimization: Smart Decision Systems in Rotary Kilns
Achieving more stable and efficient operations in clinker burning and cement grinding processes is made possible by evaluating process data through a holistic approach.
In this context:
- Aggregating large volumes of process data from production lines,
- Analyzing relationships between different process variables using advanced control algorithms,
- Actively utilizing these analyses during operations
enable better balance and control in kiln and mill operations.
Advanced Process Control(APC) systems continuously monitor process parameters and provide decision support to operators. By evaluating interactions between variables, they contribute to a more stable production process. This helps reduce process fluctuations, improve energy efficiency, and ensure sustainable production performance.
At Çimsa, APC systems are actively used in clinker production across all locations, with ongoing efforts to expand their use in grinding processes.
AI Support in Advanced Process Control
Laboratory analyses to monitor product quality in clinker firing are generally performed at specific intervals, and it takes a certain amount of time for the results to be available. This makes it difficult to see the current state of production, but it provides information about production that took place a certain time ago. However, predictions obtained by evaluating numerous process data from production together can provide forecasts at short intervals without waiting for laboratory results. With this approach, the current state of production can be seen, and necessary interventions can be made without delay. These outputs can contribute to more balanced kiln and mill operations and offer a more efficient production opportunity in terms of fuel consumption. More agile management of production processes can make it possible to reduce both quality fluctuations and energy and fuel costs.
One of the steps taken at Çimsa to improve production processes has been to predict critical quality values without waiting for laboratory analysis, thus making quality predictions more agile in production. This development enables more frequent quality monitoring of production. In this way, while quality stabilization is ensured, a more stable production process is created, reducing equipment wear periods and fuel consumption fluctuations. It also aims to make a significant contribution to increasing efficiency and reducing carbon emissions.
Predictive Maintenance: Preventing Unplanned Downtime
An Integrated Approach to Maintenance
In the industry, equipment reliability and uninterrupted operation are among the key factors determining the competitiveness of businesses. At this point, AI-powered predictive maintenance solutions are creating a significant transformation. Advanced predictive maintenance applications operating with IoT (Internet of Things) infrastructure proactively develop maintenance strategies for facilities by detecting the earliest signs of equipment failure.
Early detection of minor malfunctions in equipment reduces operational costs and minimizes unexpected downtime. By analyzing real-time sensor data, potential risks can be identified in advance, necessary maintenance activities can be planned in a timely manner, and the lifespan of the equipment can be naturally extended.
Thus, both operational reliabilities can be increased, and safety risks can be prevented. It should be remembered that a planned repair is always more economical than an unplanned failure.
One of the strongest aspects of the predictive maintenance system we first implemented at the Afyon Cement Factory is its ability to predict equipment failures before they occur, thanks to artificial intelligence (AI) algorithms. This allows maintenance teams to intervene before a failure occurs, and production lines continue to operate stably without interruption. Thanks to AI-powered early warnings, sudden stoppages on production lines are prevented, process continuity is maintained, and production efficiency is significantly increased. This proactive approach enables businesses to manage both quality and operational reliability sustainably.
The system architecture is based on the transmission of sensor data via Bluetooth to a transmitter, followed by storage and processing in the cloud. Designed as a central platform, the analysis infrastructure allows users to easily access all data via web browsers. Thanks to the cloud-based structure, data is securely stored, processed quickly, and offers a seamless monitoring experience on both web and mobile interfaces.
This system, which we continue to expand, significantly increases operational efficiency by enabling the analysis of all our equipment, integrated domestically and internationally, through a single control panel. Users can examine historical data in graphs at different time intervals, which strongly contributes to trend analysis and decision support processes.
The Power of Centralized Data (CMS)
In today’s manufacturing environments, data to hold real value on its own, it must be properly processed, interpreted, and integrated into decision-making processes. In this context, the Çimsa Manufacturing System (CMS) platform operates as an integrated analytics infrastructure that consolidates, processes, and interprets data collected from production and operational processes. Its core functionality is based on the following steps:
1. Data Collection
CMS collects data from various sources within the facility. This data is typically streamed to the platform in real time.
- Sensors (air and water consumption)
- PLC / SCADA system integration
- Production equipment
- OHS data (near misses, risks, accident records)
- Maintenance data
- Quality laboratory results
- Operation logs
- Energy consumption data
- Fuel and raw material data
2. Data Storage and Structuring
All collected data is securely stored in a database, organized with timestamps, and standardized across different formats. This step is critical to ensure that data analytics processes are conducted reliably and effectively.
3. Data Analytics Process
Within CMS, various analytical techniques are applied to transform raw data into meaningful information. This enables better visibility into overall process behavior, variability, and performance trends.
- Descriptive statistics
- Trend analysis
- Correlation analysis
- Anomaly detection algorithms
- Deviation forecasting
4. Visualization and Reporting
Users can easily access the following interfaces through the CMS platform. The clear and concise presentation of information accelerates decision-making processes.
- Real-time charts
- Trend graphs
- KPI dashboards
- Customized reports
5. Action Management and Continuous Improvement
CMS is not only an analytical tool but also a platform where insights are transformed into actionable outcomes. This enables the system to enhance operational efficiency while fostering a sustainable data-driven culture.
- Alert notifications
- Root cause analysis
- Performance improvement recommendations
- Production – Maintenance – Energy reports
Descriptive statistical analyses performed on the high-volume data collected through CMS provide companies with a powerful starting point by revealing overall process behavior, variability, and key trends. These analyses enable a clearer understanding of production process dynamics while also establishing the robust data foundation required for the successful implementation of machine learning projects.
With this comprehensive data infrastructure, relationships between different process parameters can be examined in detail, and predictive models can be developed to forecast specific performance indicators. As a result, CMS has evolved beyond a platform that merely collects data into an advanced system that helps analyze production processes more effectively, strengthens decision-support mechanisms, and promotes a data-driven management culture.
This transformation accelerates process improvement initiatives while elevating operational efficiency, quality management, and decision-making capabilities to a higher level. CMS is not simply a data collection platform; it is a holistic management solution that enables organizations to better understand their processes, identify risks at an early stage, and strengthen a culture of data-driven decision-making.
Delayed Quality Problem and Strength Estimation
Intelligent Prediction Mechanisms for Proactive Quality Management
Increasing efficiency and sustainably controlling quality in industrial processes has become one of the most critical goals of today’s production technologies. In this regard, intelligent analytics platforms that transform high volume data from various sources into meaningful insights offer a powerful guide for businesses.
These systems, operating with advanced statistical methods, machine learning algorithms, and predictive modeling techniques, provide an early warning mechanism that detects quality deviations, energy inefficiencies, and process imbalances before they even occur. Thus, instead of intervening after problems arise, potential risks are predicted in advance, and processes can be managed proactively. A data-driven forecasting study was conducted to enable earlier predictions of 28-day strength values, one of the most important indicators of cement and clinker quality.
With the SmartCem project, models were developed that can predict the final strength performance of the product before the result is obtained, by combining data obtained from the production process, laboratory results, and process parameters. Thus, instead of waiting a long time to evaluate quality, it became possible to obtain predictions at an earlier stage and take necessary actions in a timely manner. This approach contributes to more proactive management of quality processes, while allowing production decisions to be supported by a stronger database and enabling closer monitoring of product performance.
AI-Powered Microscopy
One of the most critical elements determining quality in cement production is the mineralogical and structural properties of the clinker. Petrographic examinations, traditionally performed with optical microscopes, are a time consuming process based on expert interpretation. However, generative artificial intelligence supported analysis systems can add a new dimension to this process.
Analyzing clinker petrographic images with artificial intelligence models is possible without requiring direct access to the microscope interface. Thanks to this approach, images are processed in seconds, minerals are automatically classified, and structural features are revealed with high accuracy.
This transformation project at Çimsa both accelerates quality control processes and strengthens the production quality interaction. Generative artificial intelligence-supported analysis allows operators to make faster decisions, detect process deviations early, and ensure more stable quality management on the production line.
Energy Management and Carbon Reduction
Artificial Intelligence in Energy Management
The planning of more efficient management of electricity production from Solar Power Plants (SPPs) and Waste Heat Recovery Systems(WHRs) in relation to the factory’s energy consumption is now possible with the development of artificial intelligence-supported prediction models. From this perspective, by combining data from different sources, creating predictions for SPP production using weather data, and including electricity market price predictions in the model, the energy needs of a production line or facility can be modeled with high accuracy. By considering the daily and hourly work plans of factory units, the energy needs are calculated, and the most cost effective balance can be established between electricity from SPPs, WHRs, and the grid. Thus, it becomes possible to use energy resources more effectively, manage energy costs better, and conduct production processes in a more planned way in terms of energy.
At Çimsa, this technological structure has been implemented in our Eskişehir Factory because of the perspective of effectively using operational technologies and artificial intelligence in energy consumption and production processes. In production planning, thanks to our energy optimization application, we are able to predict the production of our solar power and waste heat recovery plants and strongly support our goal of minimizing daily energy consumption costs in line with intraday energy price information.
Following the commissioning of our project, we completed our patent application processes. In addition to these developments, in the coming period, it is aimed to include the newly established battery energy storage infrastructure in the Energy Optimization model, enabling the storage of generated energy for use when needed and achieving a more flexible energy management structure.
Digital Twin: Virtual Factory for Risk Management
Today, industrial facilities are transforming with a new concept at the heart of data-driven decision-making processes: the digital twin. This technology, which creates a virtual copy of a physical facility or equipment fed by real-time data, offers businesses the opportunity to optimize their production processes more safely, quickly, and at lower cost. A critical scenario no longer has to be tested directly in the field; it is first modeled in a virtual environment, the results are analyzed, and risks can be eliminated before they even arise. This increases process stability, improves efficiency, and makes decisions more scientifically sound than ever before.
The digital twin approach plays a significant role not only in performance improvement but also in achieving sustainability goals. It becomes a powerful tool for monitoring every step of production, optimizing energy use, and reducing the carbon footprint.
At Çimsa, a digital twin study was conducted, combining two different approaches to represent both the operational behavior and physical structure of the production line in a digital environment. Using process data collected from the production lines, a simulation model reflecting the operational dynamics of the facility was created, thus enabling the analysis of how the operation might behave under different conditions in a digital environment. To complete this study, the facility site was imaged in detail using a drone, and a three-dimensional model of the factory was created. This resulted in both a simulation model operating based on data and a realistic three-dimensional visual twin of the site. This structure allows for a more holistic examination of the facility in a digital environment and enables the evaluation of operations from different perspectives.
Computer Vision: Smart Detection Systems in the Field
In industrial facilities, Computer Vision (CV) has become a critical technology for monitoring production processes in real time and strengthening automated decision-making mechanisms. This approach enables the analysis of not only digital sensor data, but also visual data streams generated throughout production facilities. By analyzing camera images using artificial intelligence models, processes such as raw material identification, safety equipment monitoring, equipment health monitoring, and autonomous operations management can be performed faster, more reliably, and in a more data-driven manner.
These systems provide significant advantages in production processes due to their ability to perform real-time analysis, detect details that the human eye might miss, and operate continuously. As the Operational Technologies Department, we develop projects aimed at increasing production, safety, and operational efficiency by evaluating image processing technologies in various application areas.
A. Raw Material Analysis-Based Image Processing Applications
The accurate identification and appropriate quality of raw materials used in production processes are critical for both product quality and equipment health. In traditional approaches, such controls are typically carried out through operator observation or laboratory analyses. However, Computer Vision based image processing techniques enable the development of autonomous applications for tasks such as raw material identification, conformity checks, classification, automation of pre-process quality inspections, and early detection of potentially risky situations that may affect equipment health.
Through this approach, time losses associated with manual controls can be minimized, data-driven decision making becomes possible, and field operations can be standardized. Processes can be guided rapidly through real time alerts. Production quality and equipment health can be managed more effectively.
Image processing provides a consistent control mechanism, particularly in facilities where raw materials exhibit variability, and makes a significant contribution to maintaining process stability.
✔ Kiln Feed Raw Material Image Processing Project
(Bauxite–Limestone Ratio Detection)
An image processing model has been developed to determine the bauxite–limestone ratio of materials entering the kiln feed line. This model analyzes the color and texture characteristics of raw materials to distinguish between material types and can even estimate the tonnage based on this classification.
This enables rapid evaluation of whether the feed mixture is within the desired proportions. With this model:
- Changes in raw material composition can be monitored in real time,
- Operators can be continuously guided with accurate data,
- Imbalances in kiln feed can be prevented.
This approach strengthens quality stability while also reducing energy consumption and process fluctuations.
✔ Weighbridge Raw Material Detection Project
This system has been developed to automatically identify the raw materials carried by trucks entering the weighbridge area. Cameras positioned in the weighbridge zone capture images of the truck loads, which are then analyzed. AI models classify different types of raw materials based on visual characteristics and automatically determine the material being transported.
This system improves both speed and accuracy in material intake processes while ensuring more reliable operational records. Additionally, the solution is expected to contribute to the optimization of logistics operations.
✔ Crusher Feed Suitability Detection Project
Raw materials fed into crusher equipment may sometimes contain oversized or unsuitable materials. This can lead to equipment wear, failures, and negatively impact production continuity.
By installing cameras at the crusher feed point, images of incoming materials are analyzed to detect oversized particles. The system identifies materials exceeding predefined size limits and can either alert operators or trigger intervention.
This approach contributes both to protecting equipment’s health and enabling early detection of supplier-related material non-conformities.
B. Occupational Health and Safety (OHS) Applications
Ensuring a safe working environment in industrial facilities is not only about defining rules, but also about effectively enforcing them in the field. In large-scale production environments with intensive operations, continuously monitoring safety compliance manually can be challenging.
Image processing technologies provide strong support in this area. Visual data collected through cameras is analyzed by AI algorithms to automatically detect the use of Personal Protective Equipment (PPE), compliance with safety rules, and potential risk situations.
This approach enables systematic monitoring of safety compliance, reduces risks, accelerates OHS inspection processes, and allows real-time reporting of non-compliance. As the Operational Technologies Department, we have developed several image processing applications in this domain.
✔ PPE Detection System at Vehicle Entry Points
A computer vision system has been developed to detect whether mandatory personal protective equipment such as helmets and reflective vests is present inside vehicles entering the facility.
Cameras at entry points analyze images and automatically verify the presence of required equipment before drivers exit their vehicles. This system simplifies the enforcement of safety rules at entry points while also reducing waiting times.
✔ Smart OHS and Notification System Within the Facility
This system integrates with existing cameras in operational areas and analyzes footage in real time. It automatically detects safety violations such as absence of PPE (helmets, vests, goggles), unauthorized entry into restricted zones, proximity between humans and forklifts, and risks such as slips and falls.
With this system:
- Risks can be monitored in real time,
- Alerts can be sent to safety teams,
- Operational safety performance can be managed in a data-driven way.
It contributes to reducing workplace accidents and strengthening the safety culture, especially in high-risk areas such as production and logistics. As a result, faster interventions are enabled and a stronger safety culture is established.
C. Autonomous Filling System
Silo-based filling operations require precise alignment. Improper vehicle positioning can lead to:
- Dust leakage,
- Operational delays,
- Safety risks,
- Equipment damage.
✔ Silo-Based Autonomous Filling Alignment System
Our Computer Vision supported autonomous filling system uses image processing technologies to guide bulk trucks (silo trucks) in aligning correctly with the filling spout. The system analyzes the real time position of the vehicle and provides instant directional guidance to the driver via a screen. Once proper alignment is achieved, confirmation is provided.
This system offers real time guidance to drivers and enables faster, safer filling operations. By reducing human intervention and shortening alignment time, the filling process becomes more efficient and standardized. Most importantly, it contributes significantly to reducing OHS-related risks.
Operational technologies not only analyze the millions of data points generated on the production floor but also provide a strategic intelligence mechanism that transforms this data into a competitive advantage. Every step we take at Çimsa strengthens our data-driven decision-making culture, enabling us to adapt to the production model of the future today. As an extension of this approach, we are shaping not only the present but also the future of the production world with the sustainable solutions we develop.
