Edge computing and artificial intelligence (AI) are two of the hottest topics in technology today, and the power of each of these technologies to transform operational technologies (OT) is immense. But the real value is in combining these technologies to achieve something even greater in the operational world – enter OT Dynamic Inferencing.
Before we dive in, let’s define a few terms to guide our understanding.
- Operational Technology is the hardware, software, and firmware components of a system used to detect or cause changes in physical processes through the direct control and monitoring of physical devices. These are the technologies that run manufacturing, chemical processing, energy generation, transportation, and many other industrial processes.
- Edge Computing, in this context, is a connected gateway, on-premise, where local data processing and control happens.
- Dynamic Inferencing is utilizing an AI model, locally on the edge, to react and adjust ondemand in real-time to improve and automate OT systems.
AI is powerful and it’s transforming the way we use and extract insights from IoT data – from video AI to recognize a potential wildfire to predictive maintenance on critical machinery to reduce unplanned downtime at a factory. However, one of the issues with AI is that it takes a lot of data to build effective models, and the tools and AI runtime engines are mostly tethered to the cloud. To realize the immense potential of AI, we must unleash its power to run on the machines that need the knowledge contained in AI models created by data scientists. This is at the essence of what is the Industrial Internet of Things (IIoT) movement, the intersection of information technologies (IT) like AI with existing OT technologies like a water pump or an oil rig.
OT Dynamic Inferencing presents a paradigm shift for operations leaders. With this shift, operations can now leverage the power of Edge computing in combination with AI and advanced analytics for higher levels of operational performance and eff iciency. Edge computing allows processing to be moved out of the cloud and placed where it can be immediately executed, within on-premise gateways connected to AI-enabled machines that are in the water facility, in the manufacturing plant, or at the oil rig, processing massive amounts of diverse IoT data in real-time. Fast-moving data streaming from the Edge presents a new opportunity for operations leaders to uncover the right data at the right time at the source, where seconds matter and immediate actions can impact key metrics that drive performance and lower costs.
Some operators have done work toward optimizing their environments and have the ability to provide alerts ahead of failures. But stopping there leaves a lot of value on the table. A truly optimized operational environment can be achieved by employing AI and streaming analytics at the Edge so that no data streams are left used because they are not analyzed. By accessing the treasure trove of hidden insights in data streams, operations leaders can see emerging trends and gain a repeatable process that scales – no matter how many IoT-connected assets they have and where they are located. The value is in the ability to access the insights at the source, predict (not just provide an alert) undesirable situations that drive up costs and negatively impact performance such as unplanned downtime, rising scrap rates, and product defects, and prevent them from occurring in the first place.
So, when we can ignite the power of AI by moving the AI models down to the Edge, connected to the machine that is generating the data in real-time, we achieve the power of OT Dynamic Inferencing, enabling the machines to regulate the energy used at the water pump, to alert the appropriate personnel before a steel press fails, to course correct the drill bit reducing errors saving time and money. The industry applications and business benefits of OT Dynamic Inferencing are endless.
In summary, the convergence of OT and IT is the very definition of Industrial IoT. OT Dynamic Inferencing in OT environments is at the forefront of automating and modernizing existing industrial control systems (ICSs), distributed control systems (DCSs), and supervisory control and data acquisition (SCADA) systems. This movement within operations is just underway, and the companies that start now on these evolutionary projects, utilizing proven AI and advanced analytics and integration services that deliver incremental and immediate ROI specific to operational metrics, such as overall equipment effectiveness (OEE) quality and yield, will be the big winners over the next several years.
View this on-demand presentation from IoT Slam Day “Edge AI: OT Dynamic Inferencing” to learn more.
Founder and CEO