Edge Computing: The Key to Smarter Industrial Automation

Industrial edge computing is a key component of Industry 4.0. As technology continues to evolve, manufacturing organizations are under increasing pressure to meet higher demands and support resilient supply chains. Edge computing enables them to implement automation and collect and process data closer to the original source, increasing efficiency, scalability, precision and resilience.

In this article, we will guide you from ‘What is edge computing’, through the benefits and use cases of edge computing industrial automation, and finally to how SUSE can help you achieve industry 4.0.

 What is Edge Computing?

Edge computing is a distributed computing paradigm that brings data processing and storage closer to the sources of data, such as IoT devices, sensors or end-user systems. By reducing the need to transmit data to centralized cloud servers, edge computing enables real-time decision-making, lowers latency and improves efficiency. This approach is especially crucial in industrial automation, where speed and precision are vital for optimizing operations, ensuring safety and driving innovation.

How does Edge Computing Fit into Industrial Automation?

A few decades ago, industrial automation was largely built around rigid, mechanical systems that relied on manual controls and limited programmability. Any time a change was needed—whether it was a new product line or an updated process—the entire system often had to be halted for reprogramming, which required both time and technical expertise. These systems lacked the flexibility and adaptability needed for today’s modern innovation. 

Additionally, most data processing took place off-site in centralized cloud servers or data centers. While this worked for some applications, the delay in transmitting data to these remote locations and receiving responses back created significant bottlenecks in production. For fast-paced manufacturing environments, these seconds lost can add up to costly inefficiencies, slower production cycles or even product defects. 

As manufacturing technologies evolved, the need for more responsive systems became clear. Industry 4.0 has pushed this shift further, integrating more interconnected machines and smart devices that collect and process data in real-time. This evolution requires faster decision-making at the source of data—on the factory floor. Relying on cloud-based solutions for this type of immediate decision-making is impractical, as even the smallest lag can cause interruptions or delays.

This is where edge computing comes into play. By moving processing power closer to the data source, edge computing minimizes the need for data to travel long distances, slashing latency and enabling quicker, more accurate responses. For instance, machines can adjust parameters in real-time, improving not just productivity but also quality control. Additionally, with computing at the edge, manufacturers are able to achieve a level of responsiveness that traditional cloud-based systems simply can’t match. The result is more agile production processes, better utilization of resources, and the ability to adapt quickly to new demands.

The Role of Edge Computing in Industrial Automation

Edge computing involves handling data closer to where it’s generated, rather than sending it off to distant, centralized data centers for processing. With edge computing in industrial automation, this means that data from across the factory floor (including sensors or cameras) is processed locally, allowing for real-time adjustments. For example, a temperature sensor can instantly trigger a response to prevent overheating, reducing the risk of breakdowns or production delays.

On-site processing also reduces network strain. Instead of flooding the network with raw data, only key insights are sent to the cloud. This improves bandwidth efficiency and ensures critical operations continue even if the internet connection is slow or interrupted. By keeping production systems responsive and minimizing downtime, manufacturers can maintain higher productivity levels and avoid costly delays. 

Real-Life Industrial Edge Computing Use Cases

Edge computing in manufacturing is being utilized in numerous ways—all of which drive operational efficiency and reduce costs. Edge computing use cases include:

1. Predictive maintenance: Manufacturers are using edge computing to continuously monitor equipment performance through sensors embedded in machinery. Instead of waiting for signs of wear or relying on scheduled maintenance, edge systems analyze this data locally, identifying early indicators of issues like overheating or mechanical stress. This allows operators to perform maintenance precisely when needed, avoiding costly breakdowns and extending the lifespan of machines.
2. Quality control: In high-speed manufacturing environments, ensuring consistent product quality requires more than routine inspections. Edge computing enables real-time data analysis from cameras and sensors installed along the production line. These systems can detect minute defects or irregularities—such as deviations in shape, color or material—on individual machines as they’re produced. By catching these issues immediately, manufacturers can remove defective items before they advance further in the production process, reducing waste and rework.
3. Supply chain optimization: Edge computing enhances supply chain management by processing data from connected systems, such as warehouse sensors and delivery vehicles, on-site. This allows manufacturers to track inventory in real time and respond immediately to shifts in demand. For example, if a shipment is delayed, edge computing can automatically adjust production schedules or reroute resources to avoid production slowdowns. This ensures supply chain issues don’t ripple through the entire operation.
4. Condition-based monitoring: Traditional maintenance schedules often involve servicing equipment at set intervals, regardless of its actual condition. This can lead to unnecessary maintenance on perfectly good components or, conversely, overlooking potential issues that develop between scheduled checks. Edge computing facilitates condition-based monitoring, where sensors continuously assess the real-time health of machinery. This data is processed locally, allowing immediate detection of anomalies like unusual vibrations, temperature fluctuations, or changes in energy consumption. By pinpointing the precise time and nature of these deviations, edge computing enables maintenance teams to intervene only when necessary, optimizing maintenance schedules and preventing unexpected failures.
5. Improving energy management: Edge computing can play a crucial role in optimizing energy usage and reducing costs. By analyzing data from various sources like smart meters, environmental sensors, and production equipment, edge systems can identify energy-intensive processes and areas of inefficiency. This localized analysis allows for real-time adjustments, such as automatically turning off idle machines, optimizing lighting based on occupancy, or dynamically adjusting HVAC systems to match production demands. These granular energy-saving measures contribute to a more sustainable operation and reduce overall energy consumption.

Benefits of Edge Computing for Industrial Automation

The benefits of manufacturing edge computing extend far beyond just speed and accuracy. Edge computing enhances industrial automation by:

• Improving operational efficiency through real-time data processing, allowing for faster decision-making, less downtime and more productivity.
• Giving businesses more control over sensitive information and less exposure to cyber risks as data is processed locally rather than in the cloud, enhancing data security and privacy.
• Enabling scalable, flexible growth by adding localized processing nodes without requiring major infrastructure changes. 
• Lowering costs by optimizing resource allocation through real-time monitoring and control, ensuring machines operate at peak efficiency.
• Edge computing facilitates the deployment of AI and machine learning models directly at the edge, enabling predictive maintenance and advanced analytics for smarter automation.

Challenges of Edge Computing for Industrial Automation

While industrial edge computing does offer clear advantages to manufacturers, its implementation presents several challenges that must be addressed:

• Integration with existing systems: Migrating to edge computing requires complex integration with legacy equipment and industrial control systems (ICS), which may not be designed for modern architectures. This can be time-consuming and expensive.
• Data management: With local data processing, manufacturers must take on greater responsibility for data storage, handling large volumes of real-time information. Ensuring data is properly managed without overwhelming local systems can require significant investments in infrastructure.
• Security concerns: Though computing at the edge does reduce data transit to central surveys, it introduces new points of vulnerability. Each edge device becomes a potential entry for cyberattacks, necessitating strong security protocols to prevent breaches or regulatory non-compliance.
• Skilled workforce: Managing an edge infrastructure demands specialized expertise in maintaining and troubleshooting edge devices, meaning manufacturers may need to invest in additional training or recruit skilled personnel.  

However, with the right industrial edge computing platform, these challenges can be overcome effectively.

Activate Industrial Edge Computing with Help from SUSE

The right industrial edge computing platform gives manufacturers a competitive edge through features that simplify deployment and workflow management while supporting long-term scalability. Features like zero-touch provisioning allow for quick, easy setup of edge devices, minimizing the need for manual intervention and reducing configuration errors. Additionally, robust scalability and security features allow the organization to grow rapidly while safeguarding data from potential threats.

To learn more about edge computing and how SUSE delivers these capabilities, check out our guide: Gorilla Guide to Edge and Industrial IoT at Scale for a deeper dive into how you can optimize edge deployments.

Industrial Edge Computing FAQs

What is industrial edge computing?

Industrial edge computing refers to the practice of processing data locally at or near industrial equipment, such as factory floors or production lines, rather than relying solely on centralized data centers or the cloud. This approach enables real-time data analysis, faster decision-making, and improved efficiency in industrial operations.

What are the main drivers of industrial edge computing?

The main drivers include the need for real-time data processing, reduced latency for critical applications, enhanced data security by minimizing cloud dependencies, and cost efficiency through optimized resource allocation. Additionally, the adoption of IoT, AI, and predictive maintenance technologies fuels its growth.

Which industries use edge computing?

Edge computing is widely used in industries like manufacturing, automotive, energy, healthcare, and retail. These sectors benefit from real-time data insights for applications such as predictive maintenance, robotics, supply chain optimization, and smart infrastructure.