Industrial spaces are being relentlessly transformed by Industry 4.0 / Industrial IoT (IIoT), and a whole new world of innovation is still to come. Making manufacturing and related facilities “smart” by connecting people, machines, and assets and enabling communication between them has already brought about unprecedented automation and optimization of workflows. This hyper-efficiency is behind the entire concept of “lean” operations as applied to a number of fields and, given that these ideas reached critical mass just a few short years ago, it all makes you wonder what we can look forward to just over the horizon.
But the driving forces behind this rapid progress are powered by an integral but often overlooked component that helps to deal with the incredible amount of raw data generated by connected devices and identify the most useful elements. Separating the signal from the noise is made infinitely easier by algorithms based on artificial intelligence (AI). It’s thanks to AI that data gets processed not only faster, but smarter and in a way that can deliver relevant and measurable business results.
Without some mechanism for sorting through massive amounts of information, IIoT would quickly drown in its own data. And it’s not just a matter of the volume of data points, which is quickly approaching the limits of our ability to comprehend large numbers, but the kind of information that’s being gathered. Industry 4.0 relies heavily on data generated from a place where it was previously unavailable—on the factory floor.
When every production asset is generating data, it becomes both a blessing and a burden. The blessing part is easy to see when equipment, workflows and processes can be optimized based on analytics informed by machine-level insights. Inefficiencies don’t have anywhere to hide when you can shine a light on the individual components of anything.
The burden side of huge amounts of data is equally obvious. What do you do with all those numbers? How can you process and analyze them fast enough and draw conclusions before the next wave starts flooding the decision-making process?
Edge computing has become a partial solution to this problem. The idea behind it is that not every single data point is relevant or needed, so you reduce the data generated on the edges by sensors to the most important ones. After all, you don’t need to know what’s happening to your machine every second, right? Obviously you want to know when a machine requires your attention but otherwise it’s best to just let it do what it does.
And that sounds like a good option when there are 125 billion devices connected to a network. Anything that reduces the need for human intervention is going to scale up to massive benefits on a network that big. Just think of a single factory with, say, 500 workers on site and hundreds of assets, all of them generating a constant stream of data. Even with edge computing, it’s still a lot to deal with. How do you use it all to improve and grow without spending hours on organizing, analyzing and discussing it? How do you use the data to make business-critical decisions when there’s so much of it and just.won’t.stop.coming.in?
This wave of numbers from the micro level of production processes would easily overwhelm attempts to understand it without some kind of filter for separating the meaningful from the meaningless. This is where artificial intelligence comes in.
Artificial intelligence, or machine intelligence, is essentially the field of computer applications that allows devices to mimic human intelligence by making decisions that lead to various outcomes. With the right inputs and direction, machines can learn and solve problems at a rate and volume that computing power takes far beyond human capabilities.
This is the ideal solution for situations like the data overload created by IIoT. Machines can wade through oceans of information and determine what’s worth keeping and what is simply distracting bits of nothing much better and faster than we ever could. With the power of AI, data gathered through IIoT can be leveraged into any number of beneficial applications that are manifested in use cases all over the business world.
Artificial intelligence is invaluable in workflow optimization processes. AI spots patterns and identifies inefficiencies, for example showing you weak spots in your floor design, routes, processes, and more. Similarly, it can boost inventory optimization, analyzing your materials, supplies, and work-in-progress goods to give you recommendations on reductions, purchases, and material flows.
But AI capabilities go beyond what isn’t working now. Algorithms also enable you to look into the future and see what is likely to stop working soon, or even further, prevent it from happening. A good application of this superpower is in predicting downtimes. AI analyzes the performance and availability of your resources and shows you current and future roadblocks that may cease the production if things keep progressing as they do. A similar use case is predictive maintenance, a term traditionally reserved for equipment. Here, AI predicts a potential failure of a machine and proactively schedules maintenance, saving you the time and burden of doing it on your own.
Finally, AI helps with forecasting and production planning, showing you how likely you’re to ship the order on time (or what to do if you’re currently not), when will be the best time to replenish materials, what will be the margin on the order you’re processing, and so on.
If AI sounds like the Holy Grail for a continuous improvement manager, well... it is. All of these AI applications not only keep your production running smoothly, they also help you achieve your best possible performance with the most efficient use of your resources. This means better productivity, reduced labor costs, lower inventory, and, consequently, better profitability.
The best part?
All these insights are proactive. Powered by AI, you don’t have to ask for the data and spend hours analyzing reports and spreadsheets just to spot a flaw that happened a week ago. The data comes in you actually need it so you can solve issues as they arise and rest assured because you know that as algorithms learn and improve, there’ll be less and less to fix.
If you’re interested in what else IoT can do for manufacturing facilities, take a look at our white paper on how it can help achieve truly lean operations and solve 2020 challenges. You can download the white paper here.
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