Updated: Jul 12, 2021
Technology and operations management is a niche field, but one that directly impacts millions of workers. The technology is often overwhelming, even for companies that are highly motivated when managing operational technology in factories, warehouses, and other workspaces. Gartner’s latest Hype Cycle for Managing Operational Technology explores dozens of helpful tools, but knowing where to begin could be challenging.
Looking at the technologies that Gartner expects to become mainstream in this field reveals something important. Namely, everything revolves around the Internet of Things. IoT is what makes the 2020s different from the 2010s, and why so much new technology is necessary.
Let’s look at why IoT technology is so important, and how the other key technologies Gartner has recommended fall into place beside IoT. From this angle, the Gartner recommendations are not overwhelming; begin with solidifying your IoT network, and the rest falls into place.
IoT and the Rise of 5G
Gartner identifies five especially prominent technologies that will soon transform the field of technology and operations management. These include the Internet of Things, digital twins, event stream processing, machine learning, and system engineering software. The Internet of Things has received a major boost with the advent of 5G technology. One result is the ability to utilise massive machine type communications (mMTC). This allows many devices to send data at once without the overall network becoming overwhelmed as a result. Now many IoT devices can now continually inspect factories and other high-risk areas and send that data to admins. While this is great news for anyone who wants extensive data for technology and operations management, it means the act of actually managing operational technology is complicated.
Key Operational Technology Management Tools
Raw data by itself is of very little use. This is where event stream processing (ESP) comes in. Effectively, ESP organises the data that comes in through IoT sensors and makes into coherent information that programmers can actually use.
At that point, admins can apply ESP-parsed data to a digital twin. This term references the creation of an elaborate virtual model designed to look and respond exactly as a real-life subject (such as a machine) does. Admins can then subject the digital twin to real-world conditions. If the digital twin shows failure points, workers can secure the real-life object in advance.
Still, the connection between the data and the digital twin is not automatic. Companies also need machine learning and system engineering software. Machine learning is the act of training artificial intelligence (AI) to recognise and extrapolate sets of data. This means that given some data, a trained AI can make smart predictions, and admins can respond accordingly. System engineering software is the means by which admins can model the entire system, and is therefore equally essential to technology and operations management.
IoT, Technology, and Operations Management – The Essential First Step
Of course, each one of these technologies is necessary for the others to function. However, none of them can work if companies cannot effectively manage the Internet of Things (IoT). Viewed from the opposite perspective, this means that managing IoT is a relatively straightforward first step in implementing everything else.
While your Industrial Internet of Things (IIoT) likely includes a lot of recently-created technologies, as well as technologies you’ve yet to purchase, you may well have older technologies that you need to manage. This can be a problem, as those technologies weren’t designed with the modern IoT ecosystem in mind.
Thankfully, there are ways to manage every IoT device from a central console, including outdated ones. For example, you can use the Things Management feature in SureMDM by 42Gears, which allows you to track and monitor printers and other pre-IoT devices from a central console.