With the acceleration of digital transformation in the manufacturing sector, businesses have begun to move beyond automation and toward intelligent decision-making systems. MES (Manufacturing Execution System) systems and AI-based production management solutions are two key technologies at the forefront of this transformation. MES systems have long been fundamental tools for controlling production processes. However, in today’s dynamic production environments, AI-based systems are increasingly emerging due to their more flexible structures, predictive capabilities, and continuous learning abilities. These two approaches offer different but complementary ways to increase production efficiency.
What is MES?
Defined as a production execution system (MES), MES is software that tracks all operational processes in a production facility, from order receipt to product shipment, in a digital environment.
The MES system collects real-time data from machines, workers, and other resources on the production line. Based on this data, it monitors and analyzes discrepancies between the production plan and real-time developments on the shop floor, ensuring smooth production progress.
MES systems manage production not as individual processes but as an end-to-end integrated structure. Evaluating various components, such as material movements, work order tracking, quality control steps, and equipment performance, provides managers with accurate and timely information. This increases production efficiency and minimizes issues such as defective production and waste.
MES works with higher-level systems, such as ERP, in modern production facilities to transfer data from the field to the management layer. This two-way communication eliminates disconnects between planning and execution. It also enables production plans to be updated in real-time based on current conditions and allows operational risks to be identified early on.
The comprehensive visibility MES systems enhance a company’s competitive strength while improving critical production parameters such as quality, flexibility, and traceability. MES systems are fundamental for achieving operational excellence, especially in industries with high-volume production, complex processes, or regulatory requirements.
Basic Functions of MES Systems
MES systems ensure that every movement on the production line is converted into a digital trace. The instant collection of operational data enables both the monitoring of the production process and the identification of opportunities for in-process optimization. MES standardizes production activities, reducing operator dependency and helping to prevent quality fluctuations.
Additionally, MES systems integrate with high-level enterprise solutions such as ERP to synchronize production-related data with finance, supply chain, inventory management, and sales departments. This facilitates production decisions that are aligned with all company processes.
Production Tracking, Planning, and Reporting
MES systems record every movement on the shop floor and allow senior management to monitor these movements in real time. This structure enables production plans to be quickly transferred to the shop floor, work orders controlled through the system, and all data generated throughout the process to be analyzed simultaneously.
For example, data such as how long a machine has been running on a production line, time spent on a particular work order, downtime, reasons for stoppages, and production quantities are recorded through the MES. This information is converted into strategic outputs directly affecting production efficiency and future planning. These processes enable the analysis of past performance while contributing to the data-driven shaping of forward-looking decisions.
Traditional MES Architecture
Traditional MES architecture has a layered structure that manages production processes according to fixed business rules and defined flows. Components such as the user interface, business logic, and data management work together to initiate production orders, monitor processes, and process data in an organized manner.
Each component in the system performs a specific operational function. Tasks such as tracking work orders, managing quality control processes, monitoring equipment performance data, and recording downtime are carried out through separate modules. Defined workflows standardize processes, while the role-based access structure supports information security by allowing users to access only the data relevant to their areas of responsibility.
Limitations of Traditional MES Systems
Classic MES systems have been used for many years as a cornerstone of digitalization in the manufacturing sector. However, the new requirements emerging with Industry 4.0 and beyond have highlighted some fundamental limitations of these systems. Traditional MES structures, which struggle to address dynamic, data-driven, and flexible production needs, face significant challenges compared to next-generation AI-based systems.
Rule-Based and Inflexible Structures
The operational logic of MES systems is based on predefined workflows and fixed rules. While this structure yields highly successful results in standardized and repetitive production processes, it offers limited flexibility in modern production environments characterized by variability.
As a result, human intervention is required for every change scenario. Dining processes, updating software parameters, or implementing manual approval mechanisms reduce the system’s agility. Additionally, such interventions are prone to human error, negatively impacting quality. This lack of flexibility can lead to significant performance losses, especially in production facilities that operate on a multi-product, short-term order basis.
Lack of Real-Time Decision Support
Traditional MES systems collect data from the production line and use it to generate retrospective reports. However, it is often not possible to analyze this data and convert it into real-time decision recommendations. In other words, the system does not make recommendations about what to do and when; it only records what has happened.
This structure directly results in decision-making processes becoming entirely dependent on human control. The operator or manager manually analyzes the system’s data, evaluates alternatives, and makes the appropriate decision. This situation causes both time loss and harms operational agility. In critical situations requiring quick action, the system’s passivity directly affects production performance.
Considering that even delays measured in seconds on production lines can result in significant costs today, the lack of real-time decision support becomes a serious disadvantage.
Challenges of Instant Adaptability
Production environments often experience unforeseen developments. A planned production schedule can change due to supply delays, machine failures, power outages, or staff shortages. In such cases, the production system must reconfigure itself according to the new conditions. However, traditional MES systems cannot automatically adapt to such variables.
Furthermore, this lack of adaptability can become a serious obstacle to growth for companies with a constantly evolving and changing product range. For manufacturing companies seeking a competitive edge, instant flexibility and rapid reconfiguration capabilities have become indispensable requirements.
What is AI-Based Production Management?
AI-based production management is a new generation of production approaches that go beyond classic production control systems and focus on a data-driven, predictive, and learning structure. These systems can optimize production processes in real time by analyzing historical data and real-time variables, environmental factors, and production conditions. AI-based structures detect deviations on the production line, provide appropriate solutions for these situations, and manage the process by taking automatic actions when necessary.
Supported by technologies such as artificial intelligence, machine learning, deep learning, and statistical modeling, these systems shift production management from reactive to proactive. AI-based production management enables businesses to intervene in problems and develop preventive strategies by anticipating potential disruptions in advance.
Data-Driven Decision-Making Mechanisms
The core strength of AI systems lies in their ability to analyze large and complex datasets to derive meaningful insights. Data points such as sensor data from the production line, machine performance metrics, quality data, supply chain information, and environmental variables are analyzed simultaneously. This data is used to analyze the current state and create the most appropriate decision scenarios based on probability calculations.
For example, if an increase in vibration levels in a machine has previously resulted in a malfunction, the system can detect this situation in advance and issue a maintenance warning. Or, in the event of a supply chain delay, it can predict how each section of the production line will be affected and suggest alternative plans.
Such data-driven decision-making mechanisms enhance human decision-making speed and accuracy while preventing cost losses.
Real-Time Adaptability
In classic systems, when a change occurs, the process may need to be stopped, re-planned, and manually intervened. This situation can lead to time loss and decreased operational efficiency in production processes. AI-based systems, on the other hand, have a flexible structure that can respond instantly to changing conditions.
These systems can dynamically reshape processes by analyzing real-time data. They can quickly implement alternative solutions to deviations in the production line and maintain system continuity. This capability provides a significant advantage in industries where production continuity is critical and contributes to maintaining operational stability.
Learning Systems and Continuous Improvement
AI-based production systems offer static decision-making mechanisms, analyze the results of the decisions they implement, and continuously improve their performance by learning from these analyses. As a result, systems begin to produce more accurate, effective, and production-specific solutions over time.
Additionally, artificial intelligence systems incorporate human feedback into their learning pool. Operators’ rejected recommendations are analyzed along with the reasons for rejection, and the system’s decision logic is updated accordingly. Over time, the AI system becomes more aligned with the production environment, not only in terms of technical proficiency but also culturally and operationally.
MES vs AI-Based Systems: Differences and Comparison
Production management systems are undergoing a significant evolution in the digital transformation process. Traditional MES systems have formed the backbone of production operations for many years, successfully performing process tracking, work order management, and reporting functions. However, in today’s dynamic, fast-paced, and data-driven production environments, capabilities beyond mere monitoring—such as prediction, adaptation, and continuous learning—are now required. AI-based production management systems are emerging to address all these needs.
Decision-Making Capability
MES systems collect and process data from the field and typically provide retrospective reporting. However, they cannot analyze this data, make decisions, or generate recommendations. Human operators make decisions based on the data provided by the system. This structure represents a data-driven but human-centric decision-making process.
The situation is different in AI-based systems. These systems actively contribute to decision-making processes by analyzing data and can take actions to manage the process on their own when appropriate. For example, when an AI detects a deviation on the production line, the system can update the maintenance plan, shift production to another line, or reduce production speed to stabilize quality. This active decision-making ability transforms AI systems from mere observers into intervening structures.
Flexibility and Scalability
MES systems are primarily defined within a fixed set of rules. This ensures that the system works effectively within a specific structure, but limits its ability to adapt to changing needs. When product variety increases, production volume fluctuates, or external factors (such as supply crises, sudden order increases, or power outages) come into play, reconfiguring the system can be time-consuming and require manual intervention.
AI-based systems can respond much more quickly to changing conditions because they operate based on data. They can analyze increases in production volume, optimize resource usage, and even provide recommendations for workforce planning. Additionally, thanks to their scalability, they can work with limited data in small-scale businesses while simultaneously processing millions of data points in extensive production facilities to provide management support. This versatility makes AI systems more sustainable and adaptive.
Maintenance and Update Requirements
Every new scenario, workflow, or production change must be redefined in classic MES systems. This requires both time and software resources. Processes such as new machine integration, product recipe updates, or changes to quality control criteria are often carried out through manual coding and configuration.
In contrast, due to their continuous learning capability, AI-based systems can adapt to changing production conditions without software intervention. Machine learning algorithms analyze the results of past decisions to automate updates. Additionally, the system’s overall performance can be enhanced through centralized updates. This translates to reduced maintenance requirements and lower technical intervention needs.
User Interaction and Automation Rate
MES system interfaces are typically table-based and process-oriented screens used by technical personnel. The user retrieves data from the system, reviews reports, and manually initiates actions. This structure requires trained users and has a low level of interaction.
In AI-based systems, user interaction is much more advanced. Thanks to natural language processing technologies, the operator can give the system written or verbal commands. The system can analyze user behavior and make recommendations based on habits. In addition, the automation rate is relatively high. For example, when a specific quality data point exceeds critical thresholds, the system can stop production or alert the quality engineer without human intervention. This active structure supports the workforce and ensures that processes run more safely and without errors.
Advantages Provided by AI-Based Systems
Unlike traditional digital solutions, AI-based production management systems go beyond data collection and offer the ability to make decisions, generate predictions, and automatically optimize processes using this data. These features provide significant advantages in terms of both efficiency and agility in production operations. This new model, based on the collaboration between human labor and artificial intelligence, contributes to the sustainable improvement of production performance.
Prediction and Recommendation Generation
One of the strongest aspects of AI systems is their ability to analyze past and current data and use this information to make predictions. Advanced prediction algorithms can identify the likelihood of equipment failures on the production line, periods when quality deviations may occur, or potential delays in the supply chain in advance.
For example, by looking at a specific machine’s past performance, it can predict when it may need maintenance, or it can optimize production parameters with recommendations based on the likelihood of a specific product group deviating from quality criteria. Such recommendations make it easier for human managers to make more strategic decisions. Additionally, the recommendations provided by the system can be updated in real time, enabling continuous optimization of production plans.
Reducing Production Losses
Artificial intelligence systems continuously analyze data from the field and immediately detect abnormal conditions. This early warning system allows intervention before a failure occurs, reducing unplanned downtime and extending equipment life.
Similarly, when deviations are detected in quality control data, the system quickly reports the situation and automatically adjusts production parameters if necessary. These early interventions reduce scrap rates on the production line and lower rework costs. This advantage directly impacts profitability, especially in high-volume production environments with tight tolerances. Additionally, reducing production losses aligns with sustainable production goals and makes resource usage more efficient.
Process Management Without Human Intervention
Another key advantage of AI-based systems is the ability to execute specific processes without human intervention. In particular, AI systems can independently make decisions and continue the process in repetitive, low-risk, or rule-defined operations.
For example, when stock levels fall below a certain threshold, the system can automatically place an order or generate maintenance instructions for a machine that requires maintenance at specific intervals. Managing such tasks autonomously speeds up operations and allows human resources to be directed toward more creative and analytical tasks.
Additionally, these systems analyze the approvals they receive from human intervention over time to learn what types of decisions are adopted in which situations. This enables them to make more accurate decisions in similar scenarios in the future, and their level of autonomy continuously improves. Thanks to this capability, production systems transform into intelligent structures that can analyze, learn, and act according to the situation, going beyond automation.
Transition Process: From MES to AI-Based Management
Although MES systems have laid the digital foundations for production management, AI-supported systems add a new layer to these structures, making production processes more intelligent, predictive, and flexible. The transition process should be approached as a planned and phased transformation rather than a sudden system change. The success of this transition depends not only on technology integration but also on the harmonious transformation of people, processes, and data.
Integration Processes
Transitioning from MES to AI-based systems does not mean completely phasing out the existing infrastructure. Instead, the existing MES infrastructure is preserved, and AI modules are added. This integration process is initiated within a hybrid structure. As a first step, data from the MES system is made accessible to the AI infrastructure. Then, AI algorithms are tested on specific production scenarios and configured to work in sync with MES.
Pilot lines or low-risk production sections are usually preferred in this process. Artificial intelligence recommendations are first monitored, then implemented with human approval, and the results are analyzed. The system is integrated into more production areas depending on the success achieved. Thanks to this evolutionary approach, the transformation is completed without interruption in production, and employees’ adaptation to the new structure progresses more smoothly.
Data Compatibility and Infrastructure Requirements
The most fundamental requirement for AI-based systems to function correctly is high-quality, integrated data. In MES systems, data is often stored for operational requirements. However, this data must be in-depth and consistent for AI algorithms. Therefore, data quality, format, frequency, and consistency are checked during the transition process. Cleaning and normalizing missing, corrupted, or incorrect data is essential to this process.
In addition, AI systems require high processing power, which necessitates a robust IT infrastructure. This infrastructure includes high-capacity servers, powerful database systems, cloud integration, and cybersecurity measures. In particular, systems with low latency are required for real-time decision-making. Adapting the existing MES infrastructure to meet these requirements forms the technical basis for the transition.
Training and Adaptation Process
The ability of artificial intelligence systems to demonstrate their true production potential is directly linked to technological proficiency and the understanding and acceptance of the people using these systems. Therefore, user training is one of the most critical stages of the transition. Special training programs should be prepared for all user groups, including operators, managers, maintenance teams, and IT personnel.
These training programs should clearly explain how the system works, what data it is fed with, its decision-making logic, and the user’s expected interaction steps. This enables users to see the system as an effective working partner supporting business processes, rather than just a technical tool.
Additionally, simple, clear, and intuitive user interfaces accelerate this adaptation process. By the end of the training process, users’ confidence in the system increases, adoption rates rise, and the likelihood of success for the transformation project multiplies.
Frequently Asked Questions
Can MES systems be entirely replaced by artificial intelligence?
Existing MES systems are integrated with artificial intelligence rather than being completely replaced. AI systems use MES data sources to provide more advanced analyses and decision recommendations. In some cases, AI systems can take over many of the functions of MES, but the transition must be gradual.
Which production facilities are AI-based systems suitable for?
Artificial intelligence systems are suitable for all production facilities that generate high volumes of data, require intensive process monitoring, and demand dynamic decision-making. They can be effectively used in automotive, electronics, chemicals, food, and pharmaceutical industries.
How much does the transition process affect the business?
If properly planned, the transition process can be carried out without interrupting production. However, data preparation, user training, and infrastructure updates may take time. Progressing with pilot projects ensures a smooth transition.
What data do AI-based systems use to make production decisions?
AI systems analyze a wide range of data, including sensor data from the production line, ERP records, past quality reports, inventory levels, and external data sources (weather conditions, supply status, etc.), to generate recommendations. These decisions are based on both historical and real-time data.
