Industrial Automation

Industrial automation is the process of automatically conducting production through machines, control systems, and software while minimizing human intervention. Automation systems consist of sensors, control units (PLC, SCADA), actuators, robotic systems, and software components. Advanced automation systems integrate technologies such as artificial intelligence and big data analytics to enable real-time control and optimization of production processes.

Historical Development

The historical development of industrial automation is directly linked to the evolutionary process of the industrial revolutions. Initially, in the mid-18th century, the First Industrial Revolution initiated the process of mechanization. During this period, dependence on human and animal power in production decreased thanks to steam-powered machines, and the first steps toward factory-based production were taken. However, these systems were not fully automated; they were primarily mechanical structures designed to support labor.

Second Industrial Revolution: Beginning in the late 19th century with the integration of electricity into production, this phase accelerated with the development of assembly line systems. Particularly, Henry Ford’s implementation of the moving production line in 1913 established the foundation for mass production and laid the groundwork for the fundamental principles of automation. During this period, production speed increased, labor was divided, and specialized machines were developed for repetitive tasks.

Third Industrial Revolution: This refers to the integration of electronics and information technology into industry from the second half of the 20th century. This era laid the foundations for digitalization in production, with programmable logic controllers (PLC) and digital control systems becoming widespread. The first PLC system, developed in 1969, enabled production control without operator intervention. Concurrently, software-based control infrastructures such as SCADA (Supervisory Control and Data Acquisition) and DCS (Distributed Control Systems) emerged, expanding the scope of automation systems. One of the most significant milestones of this era was the deployment of the first industrial robot, “Unimate,” at a General Motors plant.

Fourth Industrial Revolution (Industry 4.0): Beginning in the early 21st century and still ongoing, this era encompasses the integration of artificial intelligence, big data, machine learning, the Internet of Things (IoT), augmented reality (AR), digital twins, and cyber-physical systems beyond classical automation systems. Factories have become not merely automated but also “smart,” with systems capable of collecting data, analyzing it, and making autonomous decisions when necessary. For example, on a production line, machine learning algorithms can detect faults, predict maintenance needs, and optimize production without human intervention.

Infrastructure and Technologies

The technologies enabling automation processes consist of distinct layers or levels. The sensing layer interacts with the environment by measuring physical variables such as temperature, pressure, and speed through sensors. In the control layer, systems such as PLC, DCS, or SCADA process the collected data to make decisions. The execution of these decisions into physical outputs occurs in the actuator layer, where components such as motors and valves perform movements according to commands.

The communication layer enables data exchange between operational technology (OT) and information technology (IT) systems, facilitating integration across different levels. At the top, the management layer ensures monitoring of production processes and corporate-level data integrity through software such as ERP and MES. The layers and their functions are briefly summarized below:

1. Sensing Layer: Measurement of physical variables such as temperature, pressure, and speed via sensors.

2. Control Layer: Decision-making mechanisms through PLC, DCS, or SCADA systems.

3. Actuator Layer: Motors and valves that execute physical movements according to commands.

4. Communication Layer: Integration of operational technology (OT) and information technology (IT) systems.

5. Management Layer: Data integration and process monitoring through management software such as ERP and MES.

Types of Automation and Application Methods

Automation systems used in industrial production are classified according to their functional characteristics and production flexibility. This classification directly affects not only technological preference but also investment costs, production capacity, and labor structure.

Fixed Automation (Hard Automation)

Designed for high-volume, serial production of a specific product, these systems are commonly used in the automotive industry for repetitive tasks such as pressing, assembly, and welding. The system is optimized for fixed, pre-determined tasks. Due to its rigid configuration, changing the product is difficult and costly. Therefore, return on investment depends on large-scale and stable production processes.

Programmable Automation

These systems allow multiple product types to be produced on the same production line. CNC machines and PLC-controlled robots fall into this category. Since production parameters can be altered via software, they are suitable for medium-volume production. Each product transition may involve a downtime and reconfiguration cost. Nevertheless, they are highly efficient solutions in sectors requiring customization according to demand.

Flexible Automation (Soft Automation)

This structure enables seamless transitions between products and incorporates technologies such as robotic systems and automated guided vehicles (AGVs). Through software, sensor data, and AI-supported systems, the production line can process different products without any physical intervention. Such automation systems offer advantages particularly in responding to customization, product variety, and rapid market changes.

Cognitive Automation

Supported by technologies such as artificial intelligence, machine learning, and big data analytics, these systems do not merely execute actions but also demonstrate decision-making capabilities. For instance, they can predict potential faults on a production line and initiate predictive maintenance. These systems are evaluated alongside concepts such as “smart manufacturing,” “digital twins,” and “cyber-physical systems” and constitute one of the foundational elements of Industry 4.0.

The implementation of these automation types varies according to the structural needs of the sector. For example, in sectors such as food production where rapid transitions and hygiene are critical, flexible and cognitive automation are preferred, whereas in high-volume industries such as automotive, fixed automation dominates.

Industrial Robots and Applications

Industrial robots are categorized into several main types. Cartesian (gantry) robots are widely used in applications such as material handling, assembly, and stacking. Delta robots are preferred for packaging and sorting operations due to their high-speed movement capabilities. Collaborative robots (cobots), which can safely work alongside humans, have gained significant importance in production processes due to their flexible structure. Autonomous mobile robots (AMRs) are effectively used in logistics transportation and routing tasks. Below is a summary of robots and their applications:

• Cartesian Robots (Gantry): Commonly used in material handling, assembly, and stacking operations.
• Delta Robots: Used in packaging and sorting due to their high-speed movement capabilities.
• Collaborative (Cobot) Robots: Safe and flexible robots designed to work alongside humans.
• Autonomous Mobile Robots (AMR): Used in logistics transportation and routing tasks.

AI-Powered Fault Detection

Data collected from industrial automation systems can be analyzed using machine learning algorithms such as artificial neural networks, random forests, and gradient boosting to predict faults. This enables fault classification, the development of predictive maintenance strategies, and the prevention of potential disruptions to ensure continuity in production processes.

Economic Impact and Efficiency

The economic contributions of automation systems are multifaceted and comprehensive. Primarily, they help achieve significant cost savings by reducing energy, raw material, and labor consumption. Simultaneously, their ability to operate continuously and without interruption reduces downtime, increases production volume, and significantly improves efficiency. When combined with fast and high-quality production processes, this provides businesses with a substantial competitive advantage. On a global scale, the economic impact of automation is substantial; for example, it is projected that automation in the United States will contribute approximately $12 trillion to gross domestic product by 2035.

Impact on Employment

Automation has triggered significant transformations in labor structure. As repetitive and low-skill jobs are taken over by automation systems, employment in these areas has declined. Conversely, demand has increased for high-skill roles such as data analysts, automation engineers, and cybersecurity specialists. This shift has led to a transition from physically intensive labor to cognition-based job domains. For instance, a case study conducted in the Mardin Organized Industrial Zone found that a factory transitioning to automation experienced increased demand for high-skill labor while employment in low-skill positions remained stable.

Challenges and Risks

Although the widespread adoption of industrial automation systems offers numerous advantages, it also brings challenges and risks. First, the installation and integration of these systems can require high initial costs. Additionally, increased digitalization has turned cybersecurity threats into a serious risk factor. A lack of technical expertise required to effectively use these systems can slow down enterprise adaptation. Moreover, resistance to automation among workers and insufficient training can negatively affect efficiency during the transformation process. Finally, the risk of complete production halt in the event of system failure constitutes a significant threat to operational continuity.

Future Trends

Future trends in industrial automation are moving toward more integrated, intelligent, and sustainable systems driven by technological advancements. Digital twin and simulation-based design approaches enhance efficiency by enabling the modeling and optimization of production processes in virtual environments. Smart factories detect potential failures before they occur through predictive maintenance practices, ensuring system continuity.

In conclusion, industrial automation is a strategic technology that transforms not only production processes but entire business models. This transformation, progressing toward high levels of integration, data-driven decision-making, AI-supported production, and sustainability goals, is reshaping the workforce and guiding economies.