In association withLumen
Powering the future of manufacturing
It's the dawn of a new era in manufacturing: emerging automation technologies are ushering in the fourth industrial revolution, with promises of smart factories and warehouses that continuously collect and share massive data sets through connected devices and distributed infrastructure.
For manufacturers, which rely on data for rapid decision-making, it’s a game-changer. The data gathered through automation can be analyzed and used to improve processes, maintain systems, and respond to real-time issues on the factory floor. Sophisticated sensors, for example, can improve quality control and monitor maintenance. Industrial robots can operate autonomously and communicate with manufacturing systems. Augmented- or virtual-reality devices can help improve safety and training for industrial workers.
The question is, how can manufacturers harness the full potential of these advanced data-driven technologies, such as the internet of things, artificial intelligence (AI), and robotics, to boost productivity, streamline processes, and increase flexibility? How can they scale the smart factory initiatives that will help them stay ahead of the competition, while maintaining data privacy and security?
Existing on-premises and centralized cloud infrastructure can’t support the vast computing needs of these powerful applications, which require low latency—or data-transfer delay—to smoothly transport and get real-time access to data. To reduce latency and bandwidth use, as well as rein in costs, computing power and processes must be closer to the physical location of the data. The solution? Move computing power to local infrastructure at the "edge" of the network, rather than relying on distant data centers.
A whopping 90% of industrial enterprises will use edge computing technology by 2022, according to Frost & Sullivan, while a recent IDC report (registration required) found that 40% of all organizations will invest in edge computing over the next year. "Edge computing is necessary to enable the next-generation industrial revolution," says Bike Xie, vice president of engineering at AI technology vendor Kneron. The future of AI and other automation technologies depends on the decentralized edge, he explains, whether it is by connecting internet-of-things and other devices to distributed network nodes or implementing AI-enabled chips that can build algorithmic models autonomously.
"Edge computing is complementary to the cloud," Xie says. "Like cloud, edge technology enables applications manufacturers need to both gain and apply the data-driven knowledge that will power smart factories and products."
Manufacturing moves to the edge
The move toward edge computing is the result of a sea change in manufacturing over the past two decades. Manufacturers, whether they make industrial products, electronic equipment, or consumer goods, have transitioned slowly but steadily to increased automation and self-monitoring of systems and processes to drive greater efficiency in producing products, maintaining equipment, and optimizing every link in the supply chain.
As manufacturers implement more sensor-based, automation-driven devices, they also produce more data than ever before. But often, data sets from sensor-based devices to centralized systems can quickly grow unwieldy, slowing down automation and making real-time applications inoperable.
Edge computing allows manufacturers to make flexible choices about processing data to eliminate time lags and decrease bandwidth use, as well as about which data can be destroyed right after it is processed, says Xie. "Manufacturers can process data quickly at the edge if data transmission to the cloud is a bottleneck, or move certain data to the cloud if latency and bandwidth are not an issue." Not only does processing data closer to where it's used save bandwidth and reduce costs, he adds, but data is more secure because it's processed right away.
An example of toggling from cloud to edge comes from Paul Savill, senior vice president for product management and services at Lumen, a technology company that offers an edge computing platform. Lumen recently did an installation at a newly built, million-square-foot factory. Robotic systems from about 50 different manufacturers rely on edge computing "because they needed to be within 5 milliseconds of latency to accurately control the robotics," Savill says. The deployment provides secure connectivity from the edge applications to the robotics manufacturers' data centers, "where they collect information on a real-time basis."
But for long-term storage of data and for machine-learning and analytics applications—all that goes in the public cloud, says Savill. Other, larger workloads are processed in big data centers "with vast computational power" that can process enormous sums of data quickly.
"That chain from the public cloud to the edge compute to on-premises is very important," says Savill. "It gives customers the ability to leverage the latest advanced technologies in a way that saves them money and drives tremendous efficiency."
The edge also boosts efficiency and quality in the manufacturing product pipeline, says Xie. "For example, a diaper manufacturer needs to put in the right amount of water absorption material and make sure those materials are equally distributed." AI capabilities at the edge can detect anomalies and help monitor quality control, he explains, by processing machine-learning algorithms on a local hardware device in real time. "This ultimately saves money and reduces wasteful errors," he says.
The edge-driven factory of the future
IDC predicts that by 2023 more than 50% of new enterprise IT infrastructure deployed will be at the edge rather than in corporate data centers, up from less than 10% in 2020. But edge computing technology, with its distributed, open IT architecture and decentralized processing power, is still in its early days. In fact, only 27% of manufacturers have said that edge computing is currently in production. But that’s changing fast: Within the next two years, 56% of manufacturers will kick off pilots, and 17% will move from pilot phase to full production.
These changes are happening quickly, says Xie, because the manufacturing industry demands the low latency, reduced bandwidth use, and real-time data capabilities edge computing provides. "Edge computing technology is still evolving and improving, but manufacturers need those essential capabilities to enable better decision-making," he says. "They need to resolve the issues and limitations that exist in cloud computing, such as reducing latency and bandwidth requirements, which reduces overall costs."
According to IDC’s research, data collection and asset tracking are the most popular edge computing applications in manufacturing, and that will expand rapidly—edge investments in field service and labor management will see significant growth, as well as order tracking and security systems.
"There are so many different applications for edge computing to power the massive potential of automated manufacturing," Xie says. "Manufacturers need this technology to build the factory of the future, so they are ready to take advantage of everything edge computing has to offer."