What’s going on in the automotive industry right now is similar to what’s going on in many other sectors: technology trends are changing too quickly for humans to keep up with. The Industrial Internet of Things (IIoT) holds enormous promise. Companies have the ability, in principle, to automate, calibrate, build, and deliver their products while collecting massive amounts of data to continue doing so better and faster. Even so, according to IBM research, a single manufacturing site can produce 2,200 terabytes of data in a single month, but the vast majority of this data is never analyzed. Most manufacturers lack the infrastructure—or organizational structure—necessary to realize their potential.
What we’re seeing with the IIoT is similar to what we’re seeing in other industries looking to automate and AI-enable their processes. Most IIoT users are only using a small portion of what AI and machine learning can do. Furthermore, rather than using the data at scale, they’re doing it in smaller, more constrained divisions and business units. To do so, better, more connected structures are needed, as well as a completely new way of looking at an organization’s network.
What is the Internet of Things (IIoT), and how is it being used now?
The automotive and manufacturing sectors’ use of the Internet of Things is referred to as the IIoT. It’s not so much a single network as it is a diverse ecosystem of companies that use sensors and networking to extract more data, insights, and safety from their manufacturing processes.
All are manufactured at some stage. Automobiles, chips, clothing, aircraft, food packaging, and electronics, to name a few. You name it, and it’s been made. Many of these businesses have been slow to embrace emerging technology trends, especially in industries stuck in the past, such as industrial machinery and aerospace.
Until now, the IIoT has been used for automation, predictive maintenance, and accident prevention—all of which are easy ways to keep businesses working more safely and efficiently. However, those “a-ha” moments that AI and machine learning have promised in adding value to the enterprise are not always realized.
What is the reason for this? There are a few key explanations for this. The first is that most businesses lack the necessary infrastructure to use AI fully. Even with how far we’ve come in digital transformation, most companies still have a jumble of systems and stacks interacting—and any design is only as good as its weakest component.
The second, and even more important, explanation is that we as humans have yet to arrive. It’s difficult for humans to “act” like AI. As a result, it’s difficult for us to imagine how to bring those processes in place so that they can function to their full potential. This entails deciding which tools to purchase and which infrastructure to build and figuring out how to coordinate our workflows and business processes so that they can work together seamlessly. Rather than thinking in terms of a single section of a company’s IIoT, all team members, visionaries, developers, and others must work together to optimize technology’s potential. The difficult part is that there is no template for it. Every business is unique, which is why it’s taking so long for us all to work out the kinks.
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Innovation By Strategic IT/OT Partnerships
At least on the technological front, there is reason to be optimistic. Manufacturing, as previously mentioned, requires emerging technology for data processing to be truly successful, and fortunately, big tech is stepping up to provide them with what they need. For example, Siemens, IBM, and Red Hat recently announced a partnership that will enable IIoT users to optimize their efforts by using a hybrid cloud solution. This partnership will expand the implementation and versatility of Siemens’ MindSphere IIoT solution, which can be used both on-premises and in the cloud. What difference does it make? Since the faster data can be processed, the quicker insights can be applied to reduce costs, improve protection, and save time.
The speed and agility needed to process data in real-time, edge computing, AI, and better storage solutions are required. The ability to use on-premises or cloud analytics is a big benefit for IIoT users.
They aren’t the only partnership that has made a difference in the last year. Honeywell and Microsoft announced a collaboration in October 2020 that will allow Honeywell to develop domain-specific applications on Microsoft Azure, allowing industrial clients to achieve new productivity levels through increased performance, simplicity, and better insights into managing processes. Honeywell is a prime example of the converging forces taking legacy manufacturing industries into the new IT age. Honeywell is best known for its industrial origins. Honeywell has changed its company to be more IT-centric, focusing on SaaS, Big Data, and enterprise performance management (EPM) through a technology-centric lens, with its Forge solutions now in the market.
By 2027, the IIoT is expected to be a $263 billion market. However, just because manufacturing has decided to invest in the IIoT does not mean that it will benefit any business that uses it. The industry must reimagine what manufacturing and the industrial revolution look like—not only in terms of robotics and automation but also in market structure and business models. When we, as humans, actually grasp that aspect of the equation, I believe technology would be much better suited to assist us in accomplishing our goals.