Why Manufacturing is an Excellent Use Case for Edge Computing

As IoT devices become more common, edge computing is rapidly making inroads into various industries. One of the most promising edge computing use cases is in industrial manufacturing, where the new technology could lead to huge productivity gains.

Although industrial automation has been going on for decades, annual productivity gains since the 80s and 90s have remained relatively small. The average factory in the United States is 25 years old and filled with machines that are almost a decade old. But as companies look for investment opportunities and areas that need upgrades, a new generation of smart machines powered by edge computing is becoming an attractive option for companies looking to bring their operations into the digital age.

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What is edge computing?

Traditional cloud networks form a centralized system for collecting and processing data. Data is collected by connected devices at the edge of the network and transmitted back to the central cloud server, where the server's computing resources then process the data, categorize it, and store it for later use. In some cases, the server sends instructions to the devices at the edge of the network.

Cloud-based computing involves a lot of data in motion. And all that data traveling over limited bandwidth channels, plus the latency caused by the distance it has to travel to get there, can slow down the entire system. In some cases, delays are just minor inconveniences, while in others, they can cause serious problems. A self-driving car can’t afford to wait a few milliseconds for the cloud to analyze its sensor data and tell it to stop at a red light.

Edge computing is a distributed open architecture that disperses processing loads. Instead of transmitting all the data collected at the edge of the network, devices process data locally or closer to the data source, which helps avoid serious "last mile" latency issues. (From iothome) For devices that need to make quick decisions, processing data locally allows them to respond faster. In addition, through local analysis, only relevant data can be sent back to the cloud server to reduce network load.

To make it easier to understand edge computing, imagine a camera used for billing on a toll road. In a cloud computing architecture, the camera takes a photo of the car license plate and transmits the entire photo to the cloud, where a program processes the image, recognizes the license plate number, and records the number into the billing system to charge the car owner for the toll. In this arrangement, a large amount of data is transmitted over the network because of all the images transmitted.

In an edge computing application, the camera immediately processes the image, identifies the license plate number, and then transmits only that number back to the cloud to begin the billing process. By doing this, very little data flows through the network, which frees up bandwidth for other applications. It also allows the camera to continue analyzing data if the camera's connection to the server is lost for some reason.

The benefits of edge computing in manufacturing

For industry, potential edge computing use cases are very important. Edge computing can greatly reduce the complexity of interconnected systems, making it easier to collect and analyze data in real time. It can also allow devices to collect critical data in remote sites where network connections are unstable or not cost-effective. Data can be collected and analyzed locally, and critical data is only transmitted back to the central network when a network connection is available. The combination of edge computing and industrial IoT devices will make it easier to streamline industrial processes, optimize supply chains, and create smart factories.

Edge computing will enable industrial equipment to make autonomous decisions without human intervention. Sensor data can monitor the health of machinery and then speed up or slow down operations to optimize usage. Smart factories equipped with motion, temperature, and climate sensors can adjust lighting, cooling, and other environmental controls to make the most efficient use of power. And this is just one of a range of edge computing examples that leverage Industrial IoT devices. Predictive analytics can determine when components are about to fail, ensuring they can be replaced without loss of productivity.

For manufacturing companies that are expanding their operations or launching new businesses, the decentralized nature of edge computing applications can significantly reduce startup time and costs. Smart machines will be able to operate without the help of large central data centers (cloud-based). Because data can be collected and analyzed locally, mobile devices can be set up on site with a minimal data infrastructure footprint, which will help shorten supply chains and create opportunities in markets that are difficult for people to enter.

More accurate asset management and increased operational visibility will allow manufacturing companies to identify processes that need improvement. Edge computing’s ability to provide an “always-on” form of connectivity will reduce the likelihood of system downtime and provide greater flexibility. These and other edge computing applications have also made their way into the agricultural sector to greatly improve efficiency and productivity.

Edge computing also forms the framework for machine learning networks, making robot-driven automated manufacturing possible. Robots that collect and transmit data through edge networks are able to identify irregularities and eliminate inefficiencies more quickly than cloud-based architectures. The distributed nature of the system also makes it more robust, ensuring higher uptime and productivity.

Industrial manufacturing is on the brink of a revolution thanks to the potential of edge computing. Combined with a new generation of intelligent IoT edge devices, edge computing applications will revolutionize manufacturing in the coming decades to improve efficiency and productivity while controlling costs.