The Industrial Internet of Things (IIoT) is the application of IoT in an industrial setting. IIoT is sometimes referred to as Industry 4.0, though the latter primarily focuses on the manufacturing sector, using upgraded technologies to reduce waste and increase value in this field. Here are the IIoT trends and challenges to watch.
Like Industry 4.0, IIoT will revolutionize processes through connected machines that can optimize productivity and revenue. IIoT can be seen in a variety of industries, from transportation to public safety and from energy to, of course, manufacturing.
There are new trends in this space and we need to see why challenges leaders are trying to manage these trends.
More and more data is coming in for anyone using IoT, but this is especially true in the world of IIoT. Operators have become overwhelmed by the massive amount of data, making it difficult to harness its power for decision-making. One reason why it’s so difficult to make sense of the data is that it comes from so many disparate systems.
Angie Sticher, COO/CPO of UrsaLeo, the only company to offer photorealistic 3D digital twins combined with live sensor data, asset data, maintenance data, notes, “the varying types of data streams from different systems that don’t connect to one another and can’t give a realistic view of what’s happening in a given environment.
To help manage the deluge of data, technology is being deployed and creating a workflow that moves between these systems giving employees and managers the tools to triage issues quickly and get to problem-solving.
Ultimately this also helps in getting to the resolution phase of an incident more quickly.”
Though current trends do not indicate an uptick in US manufacturing, some in the IIoT industry think this may change. Joy Weiss, President, and CEO of Tempo Automation, a smart factory startup for printed circuit board assembly (PCBA), has seen this trend come to light. “We have seen a growing trend among companies preferring to switch to US-based manufacturing partners.
Using these partners instead of contracting overseas for a number of reasons, including the recent global health crisis due to Coronavirus,” she said. “Some of these advantages include geographic proximity, added IP and security certifications and standards, as well as the use of US-sourced, authentic components, and parts.”
Christine Kyle-Remmert, CEO and Founder of LoneStarTracking, a company that provides telematics solutions that include the latest Cat-M1 cellular technology and cellular-free LoRaWan deployments across North America, explains that power consumption, transmission distance, and price are three factors that play a role in the successful deployment of this technology.
“As technology progresses, sensors are getting smaller, more lightweight, and more affordable. However, no one has time to run around and replace batteries. Just a couple years ago, IoT sensors would only last 1-2 years; however, today, we are deploying sensors that can last 10+ years on a single coin cell battery,” Kyle-Remmert explains.
“Using technology like LoRaWAN, IoT sensors can now communicate 10+km and even further, with very little power. If you can develop a sensor that is a low cost, then there is nothing restricting you from deploying more sensors to get denser coverage.”
Like many new technologies, a skills gap permeates through this industry. Ekaterina Lyapina, Solutions Architect and AI and IIoT Consultant at Zyfra, a company that develops industrial digitalization technologies for machinery, metallurgy, mining, and oil and gas notes, “The qualifications needed to install new smart robots in production lines are often not available in most companies.
Facilities and factories lack free time and robot technicians to update their ongoing production. This leads them to a fall behind AI and IIoT trends, as they are not capable of using the latest robotics technology. They are missing skills in integration, implementation, and debugging artificial intelligence enhanced systems.
So, the hindering factor in AI automation is workers’ qualifications at the foremost front. Especially the training and customization of neural networks require deep specialists’ knowledge to dig the treasures of AI.”
Sticher offers a potential solution to this skills gap, noting, “virtualization is driving cost reduction for training in a number of sectors. Digital Twins and 3D Models make it easier to train staff because they mirror real-world environments and shorten the learning curve.
Coupled with combining and scaling data from many systems, digital twins also offer a realistic and readily accessible information hub to an environment’s current status.”
Especially with the new order of the world, due to new restrictions and regulations brought on by Covid-19, it will be interesting to see where IIoT stands at the end of 2020.
While innovation in this industry continues, companies are grappling with the changes and safety precautions that need more immediate attention.
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