Industry 4.0

Can Edge computing accelerate the transition of manufacturers to 4.0?

A ticket from
Pierrick Boissel
Vector image of a cloud representing the edge connected to numerous icons (phones, headsets, location, emails, etc.)


In 2025 according to Gartner, more than 50% of critical business data will be created, processed and analyzed outside the cloud /data centers. This is a paradigm shift for data management and IT departments, but also business teams who should greatly benefit from this change.

This area outside cloud /datacenters, closest to the machines, is called the “ Edge ”.

But what exactly is “ Edge ”?

Edge computing is a concept of processing and analyzing data close to where it is generated, rather than sending it to a centralized data processing center. The goal is to reduce latency and improve responsiveness by processing data at the “edge” of the network, close to the sensors and devices that generate this data. We thereby ensure the protection of the latter.

Edge computing is often used in applications that require rapid response, such as industrial control systems, autonomous vehicles, IoT (Internet of Things) sensor networks, and mobile applications. It can also be used to reduce the amount of data to be transferred to a centralized data processing center, which can help reduce bandwidth costs and protect user privacy/industrialists' production secrets by processing data in a more confidential manner.

These technologies are increasingly used in industry because they correspond in every way to the very specific needs of the factory, in addition to offering additional capabilities compared to traditional technologies (machine learning training for example outside cloud and without costs for example).

Why install an Edge solution on one or more industrial site(s)?

Zero latency : By processing data close to the sensors and devices that generate it, it is possible to reduce latency and improve the responsiveness of industrial control systems. This is necessary when we exchange with machines that we must be able to calibrate to the microsecond.

Reliability: By processing data locally, it is possible to reduce dependence on a centralized data processing center which could be vulnerable to outages or computer attacks.

Cost savings: By processing data locally, the manufacturer reduces the amount of data to be transferred to a centralized data processing center, which reduces storage and bandwidth costs. By processing the data locally, we divide the cost of training machine learning models by 10.

Data protection : by processing the data locally, we protects its process data which are production secrets.

Scalability: Edge computing allows data processing systems to be deployed in a more flexible and scalable manner, adding new devices and sensors as needed.

How to deploy an edge solution simply?

Start by identifying your data processing needs and the applications that require rapid response. This will help you determine the necessary devices and sensors and choose the most suitable data processing technologies.

Use pre-integrated, proven solutions to simplify deployment and reduce development costs.

Use a no-code solution that your operational business users can use in the factory, you will thus reduce IS development bottlenecks and consulting costs.

Focus on flexibility and scalability by using Edge computing solutions that allow you to add new devices and sensors as needed.

Check that the solution has a 360 monitoring interface for your different deployed instances.

Check that the solution allows you to send data to your cloud to be able to unlock big data use cases for your data science teams at headquarters.

Remember to put security measures in place to protect data and devices from cyber threats.

And above all, make sure you choose a solution that will deliver Day 1 value!

Do you have questions (potential use cases, deployments, advice, etc.) or would you like to know more about our solution?

Do not hesitate to contact us to discuss!

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