In an era where manufacturers are asked to be faster, more flexible, and more connected than ever before, the architecture of industrial control systems is undergoing a fundamental transformation. At the heart of this shift lies the growing adoption of intelligent edge devices, systems that do far more than collect data or pass it along. These are devices capable of running complex logic, heavy CPU workload, making decisions in real time, and seamlessly integrating IT and OT environments.
Edge computing has been around for some time, but its potential is now being fully realized in the industrial domain. The reason is clear: centralized architectures, while robust, are increasingly unable to meet the demands of modern, decentralized production models. From the need for ultra-low latency to the importance of data sovereignty and security, the drivers for edge are both technical and strategic.
The Rise of the Intelligent Edge
So, what makes an edge device “intelligent”? The answer lies in its ability to unify multiple roles traditionally handled by separate systems.
An intelligent edge platform typically integrates:
- Real-time control capabilities, replacing or supplementing traditional PLCs.
- Web Visualization framework, such as Web HMIs
- Data processing and storage, allowing for pre-aggregation, filtering, and enrichment.
- Application hosting, often via containerization (e.g., Docker), enabling deployment of AI models, analytics, user task or traditional control logic.
- Secure communication, both upward (to Cloud or MES systems) and across the shop floor.
- Industrial DevOps with Edge
In other words, intelligent edge devices move from being data conduits to decision-making nodes. This changes the role of the edge from a technical add-on to a strategic foundation for digital transformation.
Control System Setups: Then and Now
Traditionally, industrial control systems have followed a layered, hierarchical model. Sensors and actuators feed data to PLCs, which in turn communicate with SCADA systems or centralized servers for processing and control. This model has served the industry well, but it also creates bottlenecks and dependencies, especially when rapid response is required.
Intelligent edge platforms introduce a more distributed architecture, where control and decision-making are closer to the machine, reducing latency, enhancing system resilience, and allowing for localized autonomy. It is particularly beneficial in applications such as:
- Predictive maintenance, where models run at the edge to detect anomalies in real time.
- Edge Control and Motion control, where immediate response is critical to performance.
- Energy optimization, where consumption data is analyzed and acted upon instantly.
Moreover, with modern edge platforms, control logic is becoming software-defined and this allows engineers to deploy, update, and scale applications without reconfiguring physical hardware, a leap forward in terms of flexibility and lifecycle management.
Best Practices for Edge Deployment
While the benefits of edge computing are compelling, successful implementation requires careful planning. Here are some key considerations:
1. Choose hardware that is purpose-built for the industrial edge
Edge devices must withstand harsh environments, but they also need to support modern software stacks. Look for platforms that integrate real-time operating systems with support for containerized applications and modern security protocols.
Secure elements and roots of trust are fundamental components for ensuring compliance with cybersecurity requirements.
Exor International designed new edge platforms like Xedge and Microedge families, with a new concept of modularity that assures great flexibility and scalability. Edge environments vary significantly (e.g., industrial plants, remote sensors). Modular hardware allows customization based on specific requirements (like adding GPUs for AI processing or specialized I/O modules for data acquisition).
2. Adopt a layered cybersecurity approach
The decentralization of intelligence creates new challenges in cybersecurity. Edge platforms should offer encryption, user access management, secure boot, and OTA (over-the-air) updates as standard features. The goal is to protect not just data but also control logic and device integrity.
3. Focus on interoperability
Intelligent edge devices should not lock users into a single ecosystem. Support for open protocols (such as OPC UA, MQTT, Modbus, and REST APIs) is essential for seamless integration into existing architectures. This ensures that edge deployments can coexist with legacy systems as well as cloud-native infrastructures.
4. Leverage hybrid architectures
While edge computing enables local autonomy, its full value emerges when paired with cloud-based services. A hybrid architecture allows manufacturers to keep sensitive control logic on-site while using the cloud for large-scale data analytics, fleet management, or cross-site coordination.
5. Empower your team with new skills
The edge introduces concepts more familiar to IT than OT—such as DevOps, containerization, or remote orchestration. Investing in training or choosing platforms that offer simplified, low-code environments can help accelerate adoption.
The Bigger Picture: A Foundation for Software-Defined Manufacturing
Ultimately, intelligent edge devices are more than just a response to current technical limitations. They are enablers of a larger transformation in how industrial systems are conceived and managed.
By shifting control to the edge and adopting modular, software-driven architectures, manufacturers can move towards a composable and agile production environment, where machines, processes, and applications can be reconfigured on the fly, securely and remotely.
At Exor International, we see the intelligent edge not as a standalone solution, but as a bridge between the deterministic world of industrial automation and the dynamic, data-centric world of IT. Our approach is to combine rugged, high-performance edge hardware with open, cloud-native tools (such as our CORVINA platform), giving users full control over their digital evolution, on their own terms, and at their own pace.
We also need to ask ourselves a question about the profile of the programmers who will configure the new edge platforms. Machine builders are still managed by OT programmers, while Edge technologies are designed to be configured by IT Programmers. For this reason, with the CORVINA platform, further than Cloud native IT software, we also still integrating in pure web technology a zero-code environment for programming dashboards and OT control logics languages.
Conclusion
The transition to intelligent edge computing is well underway, and its impact on industrial control architectures is profound. By bringing computing power closer to the source, edge platforms are enabling faster decisions, greater flexibility, and more resilient systems.
For manufacturers this evolution represents a chance to rethink how automation is designed, deployed, and evolved. In doing so, the edge becomes the foundation for a truly intelligent, adaptive, and connected industry.
