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9th May 2024

Edge Computing in Industrial Automation

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Within industrial automation, efficiency is extremely important. This is why many companies are now looking to Edge Computing to improve their operations. By bringing computing resources closer to the data source, Edge Computing enables real-time automation. It also allows for predictive maintenance and improved productivity. This approach reduces delays, making it ideal for monitoring and controlling connected devices in the industrial IoT landscape. 

Edge Computing also offers cost-effective solutions for data processing and storage. This reduces reliance on data centres and Cloud infrastructure. Join us as we explore how Edge Computing is revolutionising industrial automation, leading to long-term success in the real world.

Understanding the Basics and Importance of Edge Computing in Industrial Automation 

What is Industrial Automation? 

Industrial automation refers to the use of computerised control systems and advanced technologies. These are used to automate industrial processes, tasks and machinery, reducing the need for human intervention. The integration of various technologies like sensors, actuators, robotics and software streamline operations.

One key application area of industrial automation is manufacturing. Automated assembly lines and robotic arms perform repetitive tasks with precision and speed, leading to increased productivity and reduced labour costs. Another application is logistics and warehousing. Automated guided vehicles (AGVs) and conveyor systems optimise material handling processes, improving throughput and reducing errors. Industrial automation also finds applications in sectors like energy, automotive, pharmaceuticals and food processing. It enables tasks such as quality control, packaging, inventory management and equipment maintenance to be performed more reliably. 

What is Edge Computing? 

Edge Computing means processing and analysing data nearer to where it's created, rather than far away. This includes using sensors and connected devices, rather than relying solely on centralised data centres or Cloud infrastructure. In industrial automation, Edge Computing plays a crucial role in improving operations and enhancing productivity.

By processing and analysing data near where it is generated, Edge Computing reduces the need to transmit large volumes of data over networks to centralised locations for processing. This reduces downtime and bandwidth usage. It also enables real-time automation and decision-making. This is critical in industrial settings where immediate responses are often required.

Edge Computing is particularly significant in industrial automation because it allows for faster response times. It is also more reliable for operations and improves scalability. By using Computing resources at the Edge, industrial control systems can handle many tasks. These include monitoring and controlling machinery, collecting sensor data and performing predictive maintenance. This is all achieved whilst ensuring access controls and network security.

Edge Computing vs. Cloud Computing in Industrial Automation

Edge Computing and Cloud Computing play complementary roles in managing and analysing data. We’ve discussed that Edge Computing involves processing data closer to where it's generated. In comparison, Cloud Computing involves processing data in remote data centres accessed over the internet.

Edge Computing offers real-time processing capabilities. This is vital for time-sensitive applications like machinery control and safety monitoring. On the other hand, Cloud Computing provides vast storage and Computing resources for handling large volumes of data collected from multiple Edge devices. It's ideal for long-term analytics, predictive maintenance and centralised management.

While Edge Computing ensures low latency and productive local processing, Cloud Computing offers scalability, advanced analytics, and centralised control. In industrial settings, the symbiotic relationship between Edge and Cloud Computing improves data management and analysis. Edge devices preprocess data before sending relevant information to the Cloud for further analysis. This reduces bandwidth requirements and improves overall effectiveness. Moreover, insights from Cloud-based analytics inform Edge devices, enhancing their decision-making capabilities. This collaboration between Edge and Cloud Computing maximises productivity in industrial automation.

Why Edge Computing is Crucial for Industrial Automation

Edge Computing ensures efficient resource allocation. This optimises the use of computing resources for maximum automation benefits. By processing data locally at the edge, critical decisions can be made swiftly without relying on centralised cloud infrastructure. As a result, operational efficiency is improved and dependence on external networks is reduced.

Scalability and security are also important considerations in industrial automation. Edge Computing offers scalable solutions that can adapt to the evolving needs of industrial environments. This accommodates an increasing number of connected devices and data streams. Additionally, by processing data locally, Edge Computing enhances security by minimising data exposure to external threats. It also reduces the risk of data breaches during transmission to centralised servers.

Challenges in Implementing Edge Computing

Implementing Edge Computing presents several challenges that organisations must manage to harness its full potential. One significant challenge is managing the increasing number of devices and services at edge sites. As the number of connected devices and endpoints grows, managing and maintaining these distributed resources becomes increasingly complex. This requires robust management and monitoring solutions to ensure seamless operation.

Another challenge lies in the skills and capabilities required to build and maintain evolving Edge infrastructure. Implementing Edge Computing needs specialist expertise in areas such as networking, cybersecurity, data management and Edge Computing technologies. Organisations must invest in training and development programs to equip their workforce with the necessary skills to design, deploy and manage Edge Computing infrastructure.

Additionally, scaling Edge Computing infrastructure poses challenges as organisations seek to accommodate the growing number of devices at the edge. Scaling Edge infrastructure requires careful planning. This is to ensure that resources such as computing power, storage and network bandwidth can support the increasing workload without sacrificing performance or reliability.

Applications, Benefits, and Future Outlook 

Industry-Specific Use Cases and Examples of Edge Automation 

Edge Automation can be used across various industries. Within transportation, Edge Automation facilitates predictive maintenance of vehicles and traffic management systems. This reduces downtime and optimises routes for improved efficiency. In the retail sector, Edge Computing supports personalised customer experiences through real-time inventory management and targeted advertising based on customer preferences and behaviour.

Industry 4.0 uses Edge Automation to enable smart manufacturing processes. This is where sensors embedded in machinery and equipment gather real-time data for predictive maintenance and quality control. Telecommunications companies use Edge Computing to reduce network delays and improve the performance of services. These can include video streaming and online gaming to enhance the user experience.

Financial services benefit from Edge Automation by enabling faster transaction processing and real-time fraud detection. This ensures secure financial transactions. Smart cities use Edge Computing to enhance public services such as transportation, energy management, and waste management. This improves urban sustainability and quality of life. In healthcare, 

Edge Automation supports remote patient monitoring, real-time diagnostics and personalised treatment plans. This leads to better patient outcomes and healthcare delivery. Overall, edge automation offers industry-specific solutions that address the unique challenges and requirements of diverse sectors, driving innovation and efficiency across industries.

Addressing Industrial Environments and Real-World Applications

Designing Edge Computing solutions for industrial environments involves addressing the unique challenges and requirements of these settings. Industrial environments often have harsh conditions. Examples include temperature variations, humidity, dust and vibrations. These can impact the operation of computing equipment. Therefore, Edge Computing solutions for industrial settings must be reinforced and built to withstand these conditions.

One approach to designing Edge Computing solutions is to use durable hardware components. These must be specifically designed to operate reliably in harsh conditions. These components may include industrial-grade computers, routers, switches and sensors. These are built to withstand extreme temperatures, moisture and mechanical stress.

Edge Computing solutions for industrial environments often incorporate advanced monitoring and management capabilities. These capabilities ensure continuous operation and timely maintenance. In addition to resilient hardware, these solutions are designed to withstand harsh conditions. Remote monitoring and predictive maintenance systems can detect potential issues before they cause downtime. This allows for proactive intervention and reduces disruptions to operations.

Furthermore, Edge Computing solutions often integrate with existing industrial automation systems. These systems include SCADA (Supervisory Control and Data Acquisition) systems and PLCs (Programmable Logic Controllers). This integration enables seamless data exchange and real-time control.

Edge Computing Use Cases in the Industrial Sector 

By processing data closer to the source in industrial settings, Edge Computing enables real-time decision-making. This reduces delays and improves responsiveness. Moreover, running artificial intelligence and machine learning workloads at industrial sites empowers organisations to analyse large datasets locally. They can then extract valuable insights without relying heavily on centralised data centres.

One significant application of Edge Computing is improving energy management. Organisations can track energy consumption in real-time. They can do this by using Edge devices equipped with sensors and analytic capabilities. This allows them to identify inefficiencies and implement proactive measures to reduce waste leading to cost savings and environmental sustainability.

Furthermore, Edge Computing plays a crucial role in enhancing employee safety and site security. With Edge devices equipped with video analytics and machine learning algorithms, organisations can detect safety hazards. They can also pinpoint unauthorised access and security breaches in real time. This enables prompt intervention and reduces risks to personnel and assets. 


In conclusion, the adoption of Edge Computing in industrial automation is important for improving efficiency and productivity. By bringing computing resources closer to the data source, real-time automation and predictive maintenance is enabled. This approach reduces delays. It is ideal for monitoring and controlling connected devices in the industrial IoT landscape. 

Moreover, Edge Computing offers cost-effective solutions for data processing and storage. It reduces reliance on data centres and cloud infrastructure. With the symbiotic relationship between Edge and Cloud Computing, organisations can maximise productivity and realise long-term success in the real world.

To learn more, contact us today. Or, head back to our blog to read more industry insights.


What is Edge Computing? How does it differ from Cloud Computing in industrial automation?

Edge Computing involves processing and analysing data closer to where it's generated. Cloud Computing processes data in remote data centres accessed over the internet.

What are the benefits of using Edge Computing in industrial automation?

Using Edge Computing in industrial automation improves operational efficiency. It also reduces downtime, enhances scalability and increases security.

What are some challenges in implementing Edge Computing?

Implementing Edge Computing presents challenges. These include managing the increasing number of devices and services at Edge sites. Acquiring the necessary skills and capabilities for evolving Edge infrastructure is another challenge. Additionally, scaling Edge infrastructure to support growing workloads poses difficulties.

What are some industry-specific use cases of Edge Automation?

Edge Automation finds applications across various industries. These include transportation, retail, Industry 4.0, telecommunications, financial services, smart cities and healthcare.

How can Edge Computing address communication challenges between the physical and digital worlds in industrial automation?

Edge Computing reduces communication challenges. It does this by processing data closer to where it's generated, minimising delays and bandwidth usage. This enables real-time automation and virtual control, ensuring immediate responses to critical events. By bringing computing resources closer to the data source, Edge Computing improves communication reliability. This leads to increased productivity and operational effectiveness in industrial automation.

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