Early Industrial IoT Adopters Will Clean Out the Competition

Early Industrial IoT Adopters Will Clean Out the Competition

The old way of industrial business may be dead, but most manufacturers aren't ready for what is being called the next Industrial Revolution.

Traditionally, manufacturers of combine-harvesters, blood analyzers, wind turbines, locomotives or coal mining machines were focused on building machines which were mechanically sophisticated but rarely connected, and not software enabled. In our consumer world, consider the shift in automobiles, where we used to talk about horsepower and torque, and now we talk about the latest driving assistant downloaded as a software update.

But most manufacturers of agricultural, construction, healthcare, transportation, or oil and gas machines are just at the beginning of making their devices smarter and more connected. The industrial companies of the future will be those that tap the power of connected technology to fuel not only better service at a lower cost but entirely new business models, says Stanford lecturer Timothy Chou, author of a new book titled Precision: Principles, Practices and Solutions for the Internet of Things.

But for the Industrial Internet to live up to its potential, there needs to be a widespread shift in its machines. Most of the machines today are pretty dumb, Chou says. There is not much computational capability in most of them.

By contrast, a smartphone has about a dozen sensors, and all of them are connected to a high-performance CPU, significant memory, and at least three different types of networksBluetooth, WiFi, and 3G or 4G. If you think about it, if I took out the battery and the fancy display and case, the rest might cost $35, he says. In the end, I would not be surprised to see a smartphone in every industrial machine.

But hardware is just part of the puzzle. Machine builders need to become software savvy and understand how connected devices can give their products the ability to offers services. Machine builders will lead the charge with the IoTnot the people who use machines, Chou says. But right now, their frame of reference is still mostly product oriented rather than service oriented, he explains. And at present, most machine builders dont understand software as well as they will need to for this shift to happen.

Most machine builders have backgrounds in mechanical engineering and are used to thinking of software as just a single piece of the puzzle. The hardware comes first, and the software runs on top of that. This is precisely the way we used to think about computer hardware. The dominant computer companies of a generation agowith names like IBM, DEC, Wang Labs, Prime, Tandem, CDC, Unisyswere all hardware-centric. People who built computers would say: We build hardware. I know you need a little software to run it. Chou explains. Today, all of the most valuable companies are software companies.

Things vs. People

Most of the technology we have built has all been for the Internet of People, Chou says. But if we want to talk about the IoT, we have to recognize that things are not people, as obvious as that might sound.

First of all, there are way more things than there are people. Cisco estimates that 500 billion things will be connected to the Internet by 2030. By contrast, the UN projects the global population to be 8.5 billion that same year. If Ciscos projection holds true, there will be more than 50 times more things than people 14 years from now.

Things are already beginning to outnumber people, and things can be everywherefrom smart pills within your stomach, a coal-mining machine a mile underground, or a train in the middle of the Australian Outback.

The volume of things and information coming off of them requires a mindset shift. In the past, the only way that we could talk to a machine was through a keyboard or a mouse, Chou says. Now we have wind turbines with 200 sensors.

Digital Exhaust

Another challenge that the broader technology industry is facing is data overload. Chou reflects that he recently spent time with Chevron where he learned about a new oil and gas platform that has 40,000 sensors. I asked what they were doing with that data, and they said: Well, I have to tell you: most of it is digital exhaust, Chou says.

The reason that much of the data we gather serves no purpose comes down to arithmetic. If you can do the simple math of how much storage it would take for me to keep all of the data from 40,000 sensors, you quickly say: That is going to be a big number, Chou explains. If you go to your boss and say: Well, Id like to keep all that data, she is going to say: What are you going to do with it all?

Current BI, analytics tools are ill-equipped to solve the problem. Instead, were going to need artificial intelligence, machine learning, neural network, and deep learning technology to learn from our things. Some of the same software used to power facial recognition at Facebook or Googles AlphaGo will form the basis of us extracting meaning from 10,000 wind turbines, with 200 sensors pushing data out every second.

Once companies become adept at finding meaningful information, they can start to transition business models and start making more money from services. A growing number of firms that have traditionally offered only products are beginning to provide services and software alongside them. GM, for instance, projects that it will earn $350 million of incremental profit over the next three years from connected-car services.

I always tell people this looks a lot like the business Ive been in, Chou says. Oraclebefore the Sun acquisitionmade $15B. $3B was licensing for new product, $12B was service/maintenance revenue on the products that had already been sold, he adds. In the industrial world, check out GEs revenue and youll see a similar picture.

In our enterprise software business, we also began to connect to the machines and provide assisted services to increase the reliability, performance, and security of the machines. As soon as you can start assisting someone, you can start shifting the business model, Chou says. You can tell your customers: you dont need to own this anymore, I will manage everything for you, which is what you think of today as all of the SaaS companies, he adds. In the future, if you build combine-harvesters, compressors, gene sequencers, forklifts or solar arrays, you can do the same thing. An air compressor company can just sell you air-as-a-service. As we have seen in the software business the companies who moved to powerful new business models are the winners, Chou explains. Every power, water, agriculture, construction, transportation, healthcare machine company has the opportunity to build their next product as a precision connected machine powered by software. You may not want to be the first, but you certainly dont want to be second.

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