In my last piece, I explored how the Internet of Things (IoT) is changing the consumer product landscape. But even greater advances are being made in industry as Cloud computing and Big Data processing allows the enterprise sector to gain efficiencies never before realized.
When it comes to the “Internet of Really Big Things”, General Electric is an unquestioned pioneer in advancing new technologies and applications. As a company that generates revenue through the sales of the assets they manufacturer, and just as importantly, the servicing of those assets, they have a clear incentive to make sure they can get more efficiency out of each customer network. GE estimates that if they can improve performance by one percent, given GE’s scale, it will equal $20 billion each year, $30 billion over the next fifteen.
For decades, GE had installed sensors on its jet engines, gas turbines (among others) that generated data – much of which went unused, as it was too expensive to keep and sort its sheer quantity. But with the advent of Big Data processing, that information goes up to the cloud and comprehensive analytics can be applied to convert every data point into valuable, real-time, actionable intelligence.
“When you process data on thousands of machines, the behavior of those machines over time becomes pretty clear. And not from a laboratory standpoint, but based on real-world operating conditions and environment. For instance we know that gas turbines operating in a high humidity environment will degrade more quickly than the same model with similar use in another location,” says Greg Petroff, chief experience officer at GE Software.
A great example of the impact of big data analytics is found in airline travel. Jet engines shouldering the burden of long haul routes between the Middle East to Asia will degrade at a faster pace than those traveling similar distances in the relative pristine air of North America. With sand in the air over places like Dubai and construction debris in the air of China, the engines flying those routes will have different performance characteristics.
The greatest benefit of having such actionable, real-time intelligence is helping companies reduce unplanned downtime. Unexpected maintenance work on these machines is one of the most costly problems enterprises face, for airline carriers to global transport companies to energy providers. Being offline is extremely expensive and bleeds revenue from the bottom line.
“Optimization in the airline industry can avoid events that crater margins. With the ability to intelligently predict when critical assets might fail, we can help our customers avoid such instances from happening. By fixing something before it is broken, we make our products and services that much more valuable, and our customers a more resilient and profitable enterprise,” says Petroff.
Alok Batra, CEO of MQIdentity is also working on the business “middleware” application of IoT and what he refers to as Machine-to-Machine (M2M) enterprise solutions.
“A big challenge right now is helping companies make their machines more business aware. Location awareness is critical, beyond simply the consumer facing aspect of say a vending machine. Yes, the location awareness makes for better marketing via customized advertising, but what about the servicing of that machine? How about the real time data on sales that makes the maintenance of that machine more efficient. Instead of guessing how to restock a route, the delivery person can know what the inventory needs are before he enters a building to discover what needs to be replenished. That is a powerful efficiency improvement,” says Batra.
GE is in a unique position to capitalize on the IoT enterprise opportunity. They have designed, manufactured and installed these machines. They understand how they are operated and serviced. They have written the algorithms, so they know the machines communicate with each other. All of the data the field produces, they capture.
“We can provide context that nobody else can access or provide – it’s a complete history,” says Petroff. “The whole space is ripe for disruption. And we would rather be the disrupter.”
GE abides by five principles in creating these solutions. One, they design for context, meaning the right information at the right place at the right time moment. Two, they design to make the customer and themselves smarter, providing the ability to make better decisions.
“Our solutions design for experience. We can look at commodity costs and adjust recommendations accordingly. ‘Last time you were here you fixed this, I suggest you look at this… Palladium is cheap now, perhaps time to make a replacement part..,” says Petroff.
Three, they design to connect, specifically to the right human capital wherever they are through a vast network.
“Most of the systems that people use to control industrial equipment predate the Internet – and lots of older equipment is being retrofitted with sensors. Many of the machines we make last for 40 or 50 years… so by designing to connect, a younger engineer can access a senior engineer in a different location whenever he or she needs that expertise,” says Petroff.
Four, they design for operations, meaning whatever the function; it must fit into the greater ecosystem and possess the ability to be tracked from a central command.
“Once you have data in the cloud, you can maximize operations on a macro level. For instance, with hospital operations management, from a command and control you can track the status of all machines in a hospital, the exact locations of the machines, the patients, the doctors… You can enable better care at lower cost through the efficiencies gained by these solutions,” says Petroff.
And five, it must be designed for self-meaning, so that whoever the end-user, it is specific to their needs and what they need to do their job more efficiently.
“The way the platform is built is to optimize business outcomes. Apps can be built very quickly – in weeks, not months or years – and they are tailored to each and every industrial customer. We therefore need to create a simple experience so they can consume it intuitively,” says Petroff.
The Internet of Very Big Things is bringing minds and machines together. It’s about the performance of systems and making people smarter by delivering contextual insight in real time. It’s about creating greater efficiency and anticipating problems before they occur.
It’s pretty incredible. And it’s only just beginning.