Benefits of Edge Computing in Industry: How edge computing is becoming central to a new efficiency revolution.

Author:Walker, George
Position::DATABASE AND NETWORK INTELLIGENCE: OPINION
 
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In the age of the industrial internet of things (IIoT), the speed of data analysis is key to effective operation. Edge computing accelerates this process, allowing for industrial data analysis to be performed at the point of collection. Here, George Walker, managing director of industrial control and automation provider Novotek UK and Ireland, explains the core benefits of edge computing.

Edge computing is the term for when process data is collected, processed and analysed in a local device, as opposed to being transmitted to a centralised system. Supported by local cloud networks and MoT platforms like GE Digital's Predix, systems that support edge computing are proving increasingly popular as a means of streamlining the effectiveness of IIoT networks.

For plant and utility managers, this presents a range of opportunities to not only improve the efficiency of operations, but to also overcome some of the limitations of centralised IIoT networks. In fact, there are the three main ways that edge computing drives value in businesses.

Greater operational efficiency

Traditional analysis is undergone by transferring data externally, which can delay decision-making as errors take longer to be found. With edge computing capable systems, large parts of the analysis can be carried out by the devices collecting the data.

The benefits of this are two-fold. For one, this can allow plant managers to access partial deep analysis in real time without waiting on lengthy analysis to be carried out externally. This means action can be taken earlier, streamlining the decision-making process.

The second benefit is that the IIoT platform, such as GE digitals Predix, can automatically respond to operational data. The system will be able to automatically adjust processes in real-time. In effect, this would allow for a self-correcting system that is able to maximise uptime and reduce the need for manual maintenance.

Overcoming network latency and bottlenecks

Traditionally, data analysis is carried out by having smart sensors send all their data to a remote location where it is analysed and processed. This is data intensive and can create problems if a network is not robust enough.

Channelling large amounts can cause network latency, which interrupts working...

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