Every second, the connected buildings, robotics, energy grids, and vehicles in the Industrial Internet of Things (IIoT) — that is, the Internet of Things as applied to manufacturing and other industries — are generating astonishing amounts of data. But the vast majority of it is being wasted. Consider the fact that, according to McKinsey & Co., an oil rig with 30,000 sensors examines only 1% of the data generated.
The issue is that on a day-to-day basis, most of this data isn't especially useful. "If the sensors tell you, 'Everything is good, everything is fine' multiple times a second, what is this information good for?" says Thorsten Milsmann, vice president of IoT at Hewlett Packard Enterprise in EMEA.
It's when things go wrong that a company needs to react to data immediately. When a wind turbine out in the field breaks down, Milsmann says, "You really want to know at that moment. This is what makes edge computing very relevant."
At the recent HPE Discover conference in Madrid, a key theme was how organisations are starting to use IIoT technologies such as edge computing and digital twins to draw deeper insights from data more quickly. We spoke to Milsmann about these two technologies and the ways they enable companies to react to problems in real time, fundamentally reshaping their production operations in the process.
Here's how they work.
Processing data at the network edge
Most IIoT sensors and meters can generate raw data, but they can't process it. Data generated at the network edge, meaning at the site of operations — a factory floor, a hospital, a campus, an oil field — is usually transferred to the cloud or a remote data centre for analysis.
Edge computing brings computing directly to the source of the data generation, instead of sending it to the cloud. "If you have an oil rig," Milsmann says, "you need to compute close to the drilling to know whether the drill is working properly. You can't send this data to the cloud because it's going to take you too long to receive the information back."
With edge computing, a network gateway device might gather information from nearby connected devices (all the robotics in a factory, for example) for processing and analysis, increasing speed and efficiency. It's a shift that gives businesses of any size the ability to acquire, analyse, and act on data in real time.
Because processing is decentralised across several touchpoints, additional benefits include lower costs related to managing data in the cloud and reduced risk of systemwide failure.
Particularly in industries such as manufacturing, energy, and transportation, more companies are starting to adopt the edge-computing model. IDC has predicted that by 2019, 45% of IoT data will be stored and processed on the edge.
Simulating reality with digital twins
Digital twins — digital copies of physical assets — are another IIoT technology playing a major role in helping businesses predict performance.
This is considered one of the biggest and most disruptive IIoT trends in recent years, even though the concept itself is not new: Michael Grieves introduced the term in 2003 at the University of Michigan, and NASA had been creating virtual models to monitor its space missions for decades.
Today, however, incredibly sophisticated digital twins are being used to make predictions about real-world behaviour. A digital twin acts just like the physical object, and it's constantly learning from the data that the object is generating. As a result, the digital twin can be studied to identify flaws, simulate real-life scenarios, and analyse performance in a controlled environment.
For example, a city's maintenance crew may use digital twins to inspect underground rail or pipe systems. A manufacturer could test adjustments to its operations without needing to stop production. Or the data from digital twins could help engineers recognise flaws and make design improvements in jet engines, race cars, or trucking fleets.
Embracing the new digital industrial age
Right now, manufacturing is most heavily invested in IIoT, with the industry expected to spend $189 billion toward IoT solutions in 2018. The use cases for IIoT, from predictive analytics to product innovation, will continue to expand across sectors, with potential for large-scale adoption in energy, transportation, agriculture, healthcare, and retail.
Yet even businesses with existing IIoT systems don't always understand what to do with the volumes of data they possess, and how to translate that data into action.
This should change over the next few years as business leaders continue to see the benefits of true real-time data analysis and embrace the models that make it possible. It’s estimated that 5.6 billion enterprise and government devices will use edge computing for data collection in 2020, up from 570 million devices in 2015. And Gartner predicts that by 2021 half of all large industrial companies will use digital twins, leading to a 10% improvement in effectiveness.
By bringing computing directly to where the data is generated and creating models that let the physical and virtual worlds speak to each other, IIoT is unlocking tremendous possibilities for businesses. In the coming years, look for more industries and organisations to adopt these models as they transform themselves for the new digital industrial age.
“This is the real next Industrial Revolution,” Milsmann says. “This is how we move into digital industrial production. It’s possible now.”
This post is sponsored by HPE.