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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.
What’s the IIoT—and How Is It Being Used Now?
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. Rather than thinking in terms of a single segment of a company’s IIoT, the company itself needs to operate as a finely tuned IIoT in itself, with all team members, visionaries, developers, etc., working together to maximize technology’s potential. The hard thing about this: there is no template for it. Every company is different. Which is why it’s taking so long for all of us—in general—to get out the kinks.
Maximizing the IIoT: Innovation Through Strategic IT/OT Partnerships
At least on the tech side, hope is coming. As noted above, to be truly effective, manufacturing needs better technology for data processing and luckily, big tech is stepping up to offer them what they need. For instance, just recently, Siemens, IBM, and Red Hat announced a collaboration that would allow IIoT users to use a hybrid cloud solution to maximize their efforts. This collaboration would extend the deployment and flexibility of MindSphere, the IIoT solution created by Siemens, to be used on-premises and with the cloud. Why does it matter? Because the faster the data is able to be processed, the faster the insights can be utilized for better cost, safety, and time savings. Edge computing, AI, and better storage solutions are necessary for the speed and agility required to process data in real time. Being able to enjoy either on-site or cloud analytics is a huge boost for IIoT users.
They aren’t the only collaboration in the past year to make a difference. In October 2020, Honeywell and Microsoft announced a partnership that would build its domain specific applications on Microsoft Azure to drive new levels of productivity for industrial clients delivering more efficiency, simplicity, and better insights into managing processes. Honeywell, which may be best known for its industrial roots, is a perfect example of the converging forces that are bringing legacy industrial businesses into the modern IT era. With its Forge solutions now in market, Honeywell has transformed its business to be more IT centric focusing on SaaS, Big Data, and enterprise performance management (EPM) through a technology centric lens.
The IIoT is set to become a $263+ billion industry by 2027. Still, the fact that manufacturing has agreed to invest in the IIoT is not a guarantee that it will bring value to every company using it. The responsibility of that is not on big tech, it’s on the industry as a whole to reimagine what manufacturing and industrial revolution really look like—not just in terms of robots and automation, but in terms of business structure and business models. When we as humans finally get a handle on that side of the equation, I believe technology will be even better suited to helping us get the job done.
Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice.
The original version of this article was first published on Forbes.