Self-Healing, Autonomous Systems
Through machine learning, IIoT technology is becoming capable of intelligently identifying and addressing issues without requiring human intervention.
For example, Duke Energy - a Florida-based power holding company - claims that their grid system can automatically reconfigure itself wherever power is lost in the home.
Using sensors across the entirety of its power-grid, outages can be detected and mended in seconds.
Previously, an outage caused by a lightning storm - for example - could take several hours to resolve because engineers need first to identify the root and impact of the issue.
This is all achieved in seconds with autonomous systems that can heal themselves.
The implications for industry are huge - allowing employers to focus on other mission-critical functions while the machines take care of themselves.
It wasn’t that long ago that the perception of the factory floor was one of a dirty, noisy, dangerous workplace where life-changing injury was not an unlikely outcome.
This image has caused real issues in attracting new talent into the work pool - especially Millennials, whose expectation of the workplace is very different from that of our forefathers.
That’s certainly changing.
Manufacturing is, of course, a potentially dangerous place, but with IIoT sensors and connected devices, it's possible to detect malfunction that could cause injury in environments that once would have created substantial hazards.
IIoT has made it possible for firms to monitor — through video analysis or by kitting employees out with sensors — anomalies and hazardous situations that can cause harm.
For example, Laing O’Rouke - an international engineering firm - have introduced smart helmets to monitor their employee's health in hot climates.
One of the hazards of heatstroke is that if you start to feel the symptoms, you already have it.
So, the smart helmets have been designed to monitor heart rate, temperature, humidity, and other life-sensitive readings to help identify and prevent heatstroke when working in hostile, desert environments such as in Qatar.
The art of increasing employee productivity has never been a particularly exact science.
The microscopically dissected activity of employee productivity has been under the management microscope for years. However, most attempts to continually monitor progress tend to meet a diminishing return because most processes in the past have required manual input and buy-in from workers. Until now.
There's a growing range of machine monitoring technologies that use the IIoT to help monitor continuous activity while encouraging engagement from workers in the form of gamification.
For example, Canadian machine monitoring firm, FreePoint Technologies, has developed a suite of IIoT smart monitoring systems that give real-time productivity data on the macro level, as well as at the micro.
Their systems allow individual workers to monitor their own progress and compare their output with the contribution of their peers. Not only this, but machine monitoring and learning facilitate absolute synchronicity over the production function of the entire factory.
Managers can monitor productivity from anywhere on the planet that has an internet connection — providing distance decision making, ensuring that deadlines are always met.
And while being consistently observed could sound intimidating to the average employee, the productivity data produced gets used for incentive schemes and to demonstrate competencies during staff evaluations.
Buy-in from employees is achieved through game-play style competitions, reaching specific milestones for special rewards.
The IIoT is transforming the way that modern manufacturing runs production; in ways that already feel like sci-fi.
It's changing the way we work now - increasing efficiencies, productivity, engagement, and safety, so that manufacturing can continue to thrive.