Mar 09, 2015
We all know IoT is going to be huge, and as with most technology, the industrial IoT will eventually eclipse its consumer equivalent. But it is certainly in no rush. From smart socks to smart homes, consumer tech is flying off the shelves, but industrial IoT is taking its time.
There’s a good reason for that. Consumer product cycles are often measured in months, while industrial hardware changes over a period of years or more. An oil pipeline or a stretch of train track may last several decades before retirement or major servicing. That’s typically a good thing, but with the IoT, reliability can get in the way. Sure, adding sensors to industrial systems can add value, but is the payoff worth a rip-and-replace? If you’re managing lots of expensive, perfectly reliable systems spread over a large area, the answer is often “No.” In Employing Industrial IoT: A Framework for CIOs, Gigaom Research Analyst Adam Lesser outlines a number of inhibitors to industrial IoT adoption: standards, security, staffing and power efficiency. These are tough enough on their own, and any industrial IoT implementation will have to cope with them, but for companies with large sunk costs in facilities and physical infrastructure, the very act of upgrading equipment can cost money and time the business just isn’t prepared to absorb.
But what if instead of sensor-enabling the environment we want to measure, we put the sensors on the people already moving through it?
I’m not talking about Google Glass or the HoloLens. Most augmented reality platforms are great for visualizing 3D spaces, providing contextual documentation, or otherwise enhancing your field of view, but they’re usually focused on helping us do one thing more efficiently. They’re primarily push tools, with very limited perception beyond the vision and hearing we already have. But if we enhance perception beyond human limits, we’ve just put legs on some pretty powerful data collection, and we can put that to use without upsetting a single apple in the cart.
A few weeks ago, I visited DAQRI’s 4D Expo conference in Los Angeles. While the show featured everything from 3D printing to augmented coloring books, the centerpiece of the show was the DAQRI Smart Helmet, a wearable AR platform designed exclusively for industrial use. While most of the attendees were focused on the AR output of the device (admittedly, the Predator-like holographic wrist controls were pretty cool), the most impressive part of the helmet was the sensor package pulling in data that unassisted humans could never access. Unlike people, the test unit had built-in thermal sensors and 360-degree cameras. Both of these could assist workers in accomplishing their usual tasks more safely or efficiently (e.g., “Don’t touch that until it cools down” or “Watch out for that beam behind you”), but they could also provide passive scanning for entirely different use cases. For example, equipped with similarly-specced devices, security guards with no additional effort or training could scan factory equipment for excessive heat or out-of-bounds behavior while they do their nightly rounds.
Of course, not everything can be assessed externally. If you want to know what’s going on inside a data center, nuclear reactor, or deep-sea cable, there are no shortcuts. You’re going to need to put a sensor inside. But there are plenty of opportunities to enhance the perception of existing employees and get a lot of bang for a comparatively small buck, with no disruption in business flow. There may even be opportunities to scan for data entirely unrelated to the task at hand on behalf of a third party, creating new revenue streams while increasing the safety and efficiency of core business processes.
Large, established businesses have a lot to lose, making true ground-up innovation a tough sell. But big industry has one massively underutilized resource—knowledgeable people. After decades of trying to automate them out of existence, humans may turn out to provide just the bridge we need to get a jump on the next 10 to 20 years.
Source: GIGAOM