In today's competitive manufacturing environment, industrial automation systems have
become the core drivers of efficiency, quality assurance and cost reduction. Behind all this,
a well-designed system architecture is indispensable. The industrial automation system
architecture is the “nervous system” of the manufacturing industry, which coordinates
every step of the process from the shop floor to the management level, realizing seamless
data flow and intelligent decision-making. In this article, we'll take a closer look at the typical
architecture layers, key components, and future trends of industrial automation systems to
help you fully understand this technology cornerstone.
Hierarchical Model of Industrial Automation System
Architecture
Industrial automation systems are typically designed in layers to ensure modularity, scalability, and
ease of maintenance. The most common model consists of a five-tier architecture that integrates
hierarchically from the bottom device to the top management level.
Layer 1: Field Layer
This is the cornerstone of the architecture and interacts directly with physical devices. It includes
field devices such as sensors, actuators, motors, valves, etc., which are responsible for collecting
real-time data such as temperature, pressure, flow, etc., and executing control commands. The
key to the field layer is reliability and real-time performance, and it is usually connected via
fieldbus (e.g. PROFIBUS, Modbus) or industrial Ethernet protocols.
Layer 2: Control Layer
The control layer is the “brain” of the automation system, which consists of programmable logic
controllers (PLC), distributed control systems (DCS) or industrial computers (IPC). It receives data
from the field layer, runs the control logic (such as PID algorithm), and outputs commands to
regulate equipment operation. This layer emphasizes high-speed processing and stability to
ensure precise control of the production process.
Layer 3: Supervisory Layer
The Supervisory Layer provides operators with a visual interface through the Human Machine
Interface (HMI) and the Supervisory Control and Data Acquisition (SCADA) system. The SCADA system
centrally displays real-time data, alarms, and supports manual intervention. This layer bridges the
control layer with higher levels, enabling process monitoring and historical data analysis, and
facilitating rapid troubleshooting.
Layer 4: Production Management
This layer focuses on shop-floor optimization and is usually led by a manufacturing execution system
(MES). The MES integrates production orders, inventory, quality management, and equipment status
information to translate business plans into detailed production instructions. It analyzes real-time data
to optimize scheduling, reduce downtime, and improve overall equipment efficiency (OEE).
Layer 5: Enterprise Layer
The enterprise layer is the top of the architecture and involves enterprise resource planning (ERP)
systems, supply chain management, and business intelligence tools. It integrates company-wide
data to support strategic decisions such as financial planning, resource allocation and market
analysis. By integrating with the enterprise layer, automation systems enable end-to-end
collaboration from order to delivery.
Key Components and Technical Support
The efficient operation of an industrial automation architecture relies on a number of key technologies:
Network communications: Industrial Ethernet, wireless networks (e.g., 5G) and Internet of Things
(IoT) protocols ensure high-speed, secure data transfer.
Data integration: Standard protocols such as OPC UA enable interoperability between devices from
different vendors and break down information silos.
Edge computing: Processing in close proximity to the data source reduces latency and supports
real-time analytics.
Network security: Firewalls, access control and encryption protect systems from network threats,
especially in high-risk environments.
Future Trends: Intelligence and Flexibility
With the advancement of Industry 4.0 and smart manufacturing, automation system architecture
is evolving towards more openness and flexibility.
Cloud-Edge-End Collaboration: Cloud computing is combined with edge computing to achieve
centralized data management and distributed intelligence.
Artificial Intelligence Integration: AI algorithms are used for predictive maintenance, quality inspection,
and to enhance the system's autonomous decision-making capability.
Modular design: Module-based architecture allows for rapid reconfiguration of production lines,
adapting to low-volume, multi-variety production models.
Digital twin technology: Virtual models simulate physical systems to optimize design and O&M.
Conclusion
Industrial automation system architecture is not only a stack of technologies, but also a strategic
fulcrum for the digital transformation of the manufacturing industry. From field devices to enterprise
cloud platforms, each layer carries a unique mission and together they build an efficient and reliable
intelligent ecosystem. As new technologies continue to be incorporated, this architecture will continue
to evolve, injecting greater resilience and innovation into the manufacturing industry. For enterprises,
understanding and optimizing their automation architecture is a critical step in winning the
competition in the future.