Making Edge Computing Work for Local Production During the COVID-19 Economy

The COVID-19 pandemic has caused a seismic shift in the global economic landscape as manufacturers move toward more local production and distribution. As supply chains become shorter and more transparent, the combination of edge computing and Industrial IoT (IIoT) devices will help meet the demands of changing markets by increasing safety, optimization, and situational awareness — all things that were important before the pandemic, but are essential during a crisis. 

Edge Computing Market

Although the edge computing market has existed for decades, it is forecast to have a major growth spurt, rocketing 30 percent a year from $3.2 billion to $44.0 billion by 2030. Meanwhile, the Internet of Things (IoT) Business Index 2020, a survey and report created by the Economist Intelligence Unit (EIU), shows that over 10 percent of manufacturers have doubled their IoT investment over the last three years while 64 percent are between the early and advanced stages of IoT planning or implementation.

These numbers may increase, as, amid the health crisis, enterprises aren’t scaling back their digital transformation projects, but are in fact, accelerating them. The high level of digital intelligence found at the convergence of edge computing and IIoT devices will profoundly impact companies by giving them better insights into supply chains while enabling more efficient local production.

Taking the Edge Off the Remote Industrial Workforce

The health crisis caused global economic activity to grind to a halt as warehouses, factories, and businesses shut down to protect workers. This caused companies to press forward with their automation strategies — in March, 41 percent of bosses across 45 countries said they were investing in automation in preparation for a post-COVID-19 world.

Edge computing will help many companies reach their automation goals. Its decentralized framework doesn’t replace but rather complements cloud computing by enabling data processing at the production site (the “edge”), resulting in lower latency, higher bandwidth, and reduced network overheads. Equipping IIoT devices with edge-enabled data storage and computing capabilities gives manufacturers insight into their operations by allowing even the smallest IIoT sensors, instruments, and other devices to connect to wireless networks via gateways, gatherings and share real-time data that leads to rapid decisions and fast responses.

By taking immediate action, machine performance is optimized, and predictive analytics can identify and prevent equipment failure saving high costs. Smarter predictive maintenance is not the only IoT-derived benefit to find its way onto the production line; insights gathered from these sensors can optimize processes or performance of assets at a time when it’s needed most.

Although edge data centers contain network equipment and servers that power cloud computing services and various video and social media platforms, they also house mission-critical data, applications, and services that support enterprises' emergency systems. Many of these data centers are placed near the areas they serve, enabling companies to make autonomous decisions without human intervention. The centers have proven particularly important during the current health crisis. Many data center operators continue to limit employee and vendor access to their facilities in favor of remote management. They may also assist in reshoring efforts as global supply chains reconstitute regional production capabilities.

Edge Computing and 5G Help Compute Local Traffic

Shorter supply chains are inherently risky — production lines can be set up to meet increased market demand, but demand can dissolve quickly. Yet, there are also great benefits to regionalizing production, and IIoT systems, devices, and sensors can help the manufacturers position their operations to respond to fluctuating production demands.

To compute local traffic, 5G allows tens of thousands of devices to access individual cells while edge devices perform complex processing tasks. This helps prevent the slightest loss of connectivity or speed, rendering digital services useless, impacting mission-critical systems, or causing dangerous problems for services such as driverless transportation or industrial machinery.

Before the pandemic, networking technologies such as 5G were already preparing for surges in increased network traffic and big data. The recent disruptive global events shone a spotlight on the need for intelligent edge computing technologies to keep networks from overloading while transporting data from the cloud to the edge.

Although 5G is still a developing technology, it’s expected that it will help create more agile networks tailored to different enterprises' varying needs at both global and localized levels. For example, the Port of Rotterdam, the largest port in Europe, has worked with Huawei,  KPN, ExRobotics, Accenture, Shell, and ABB to test the first industrial 5G applications use sensor data to optimize operational performance and automate the movement of vessels and goods. Whereas previous generations of wireless technology connected people and the internet, 5G connects things to people, to the internet, and, importantly, to other things.

One of the latest developments in 5G is O-RAN (Open Radio Access Network), which expands mobile networks' performance and efficiency even more broadly. In 2019, Vodafone launched O-RAN trials in the UK after previously launching trials in the Democratic Republic of Congo and Mozambique to lower the cost of network equipment, making wireless networks more democratic in both rural and urban areas, and increasing the potential for 5G.

Offsetting Costs

The global supply chain is at a pivotal stage in its evolution, with all signs pointing to manufacturing becoming more regional. Faster processing at the edge fits perfectly into many enterprises’ digital transformation projects. In fact, it may accelerate them by giving manufacturers better insights into the entire supply chain while supporting data-driven local production.