By: Rohan de Beer -  End User Sales Director at Schneider Electric

Despite the rapid growth of cloud computing, driven by the hype around key factors such as skills, budget and security, the industrial edge has remained a core component of industrial IT environments. The unique requirements of industrial operations have ensured that edge computing continues to be integral, as it supports the real-time, mission-critical demands of industrial settings that the cloud often cannot match. 

In years since the advent of cloud computing, many organisations have realised that not all applications and workloads can be moved to the cloud, due to legacy systems, compliance and security and performance and latency issues. 

In most cases, the application will dictate whether it can be moved to the cloud or not and this is where the question of latency comes into play in industrial settings. If an application needs a quicker reaction time, it makes sense to move data closer to where it is required.

This is because tasks that require Artificial Intelligence (AI), automation and swift reactions benefit from computing power located near the source of data generation, reducing latency and enhancing decision-making speed.

Remote mining operations

For example, in the mining sector, operations are usually situated in rural or remote areas where there is a certain amount of latency on the line to the big data centre. So, an edge node or data centre is often established near the mine from which everything can be managed, and the IT team can perform data replication into the cloud at the end of each day.

The decentralised nature of edge computing significantly enhances the reliability and resilience of industrial systems by distributing data processing across multiple edge devices, thus reducing dependency on a central server and eliminating single points of failure. This local data processing ensures that critical functions can continue uninterrupted even if connectivity to the central server is lost. 

Additionally, edge computing allows for better load balancing, preventing any single device from becoming overwhelmed and leading to system failures. This makes edge computing crucial for Industrial IoT (IIoT) applications that generate massive amounts of sensor data.

Edge computing also enables real-time AI and machine learning (ML) applications, such as predictive maintenance, quality control and process optimisation, making it ideal for intelligent and immediate decision-making. Because edge computing processes data locally on devices or near the data source, it reduces the need to send data to centralised servers. This reduces latency, enabling AI and machine learning models to make real-time decisions. 

Efficient use of resources

By processing data at the edge, computational resources are also used more efficiently, which is particularly important for AI applications that require significant processing power, such as predictive maintenance. 

Real-time AI is able to analyse sensor data to predict equipment failures before they occur, minimising downtime and maintenance costs, which in turn ensures that equipment operates efficiently. Sending only relevant information to the cloud, edge computing optimises bandwidth usage and reduces costs. 

There are several attributes of edge computing that make it essential for modern industrial IT architectures, namely resilience, scalability and security. These make edge computing indispensable for modern industrial IT architectures, enabling more efficient, as well as secure and responsive operations.

Ultimately, edge computing is here to stay and the architecture around it is expected to continue evolving as technology improves. Schneider Electric’s EcoStruxure IT Data Centre Infrastructure management (DCIM) 3.0 architecture is very well suited to distributed industrial environments, offering unparalleled monitoring and management for hybrid IT environments, ensuring operational continuity and security.