How Tech Partnerships Are Driving The Expansion Of The Industrial IoT
What’s happening in the manufacturing industry right now is similar to what’s happening to many other industries: technology is moving too fast for humans to keep up with it. The promise of the Industrial Internet of Things (IIoT) is huge. Companies, in theory, have the potential to automate, calibrate, create, and distribute their goods while amassing tons of data to keep doing it better and faster. Still, according to research from IBM, a single manufacturing site can generate 2,200 terabytes of data in a single month, but most of that data goes unanalyzed. Most manufactures lack the necessary infrastructure—or organizational structure—to harness its value.
What we’re seeing with the IIoT is akin to what we’re seeing in other industries seeking to implement automation and AI into their processes. Most of the companies using the IIoT are still using just a fraction of what AI and machine learning are able to offer. What’s more, they’re doing it in smaller, more confined departments and business units, rather than using that data at scale. To do so would require stronger, more connected systems, and honestly a whole different way of seeing one’s enterprise structure.
The IIoT is a loose term for the industrial and manufacturing industries’ use of the Internet of Things. It isn’t so much one singular network as a wide ecosystem of separate companies using sensors and connectivity to glean more data/insights/safety from their manufacturing activities.
Everything at some point is manufactured. Cars, chips, clothes, planes, food packaging, electronics, etc. You name it, it’s been manufactured. But so many of these companies especially in industries stuck in legacy thinking like industrial machinery and aerospace, have been slow to adopt new technologies.
Up until now, the IIoT has been used for things like automation, predictive maintenance, and injury prevention—simple things that help keep companies running more safety and efficiently. But—not necessarily those “a-ha” moments AI and machine learning have promised in terms of bringing value to the enterprise.
Why is that? There are a couple main reasons. The first is that most companies simply don’t have a cohesive infrastructure in place to harness AI for all its worth. As far as we’ve come in digital transformation, most companies still have a mish-mash of systems and stacks working together—and every system is only as strong as its weakest part.
The second reason, and arguably just as important, is that we as humans simply aren’t there yet. It’s hard for us to “think” like AI. Therefore, it’s hard for us to envision how to put those systems in place so that they will work at their fullest capacity. This means not just which software to buy and which infrastructure to build, but how to organize our workflows and enterprise systems in such a way that they work seamlessly together.
