Given the hype about IIoT, the ARC Advisory Group asks if it is really time to focus on the edge. The promise of edge computing in industrial environments means getting the right device data in near real-time to drive better decisions, and maybe even control industrial processes. For this to work, it means that the edge device, its embedded software, edge servers, the gateways and cloud infrastructure must all be up and running correctly all the time.
Let’s first define the edge as a place where computing occurs, in between the data center and the cloud. The growth of IIoT extends the edge to industrial devices, machines, controllers and sensors. Edge computing and analytics are increasingly being located close to the machines and data sources. As the digitization of industrial systems proceeds, so does analysis, decision-making, and control being physically distributed among edge devices, edge servers, the network, the cloud, and connected systems, as appropriate. These functions will end up where it makes most sense for them to be. This makes it essential that today’s automation assets be designed to leverage IIoT and the edge.
Edge computing and IIoT embody IT/OT convergence in their role of bridging these two areas of the architecture. This is particularly obvious as edge devices evolve beyond their traditional role of serving field data to upper level networks and emerge as an integral part of the industrial internet architecture. Today, the IT organization owns more and more of the architecture and standards associated with the industrial internet, including both clouds and networks.
With edge computing and analytics, data is processed near the source, in sensors, controllers, machines, gateways, etc. These systems may not send all data back to the cloud, but the data can be used to inform local machine behaviors as it is filtered and integrated. The edge systems may decide what gets sent, where it gets sent and when it gets sent. Placing intelligence at the edge helps address problems often encountered in industrial settings, such as oil rigs, mines, chemical plants, and factories. These include low bandwidth, low latency, and the perceived need to keep mission critical data on site to protect IP.
As manufacturers implement IIoT ecosystems that connect their machines, equipment, and production systems to the digital enterprise, both process and discrete end users would like to see real-time intelligence at the edge. In today’s connected factories and plants, edge computing will provide the foundation for the next generation of smart connected IIoT devices and the digital enterprise. These intelligent edge devices can aggregate and analyze sensor and other data and stream information to support predictive analytics platforms.
Hybrid approaches utilizing edge computing and the cloud will enable process and discrete end users to provide actionable information to support real-time business decisions and support asset monitoring, data analytics, process alarming, and process control, as well as machine learning and the emerging AI ability for machines to make sense of and act on complex data patterns. Increasingly, the computational capabilities from both edge and cloud computing are migrating into the gateways and edge devices for IIoT networks.
It comes as no surprise that many end users expect to perform data analytics at the edge. If industry is to move to ecosystems of smart connected machines and production systems, the first step is to create a digital environment that securely connects factories and plants using intelligent devices that can access, capture, aggregate, and analyze data at the production process and provide actionable information to enable operations, maintenance, and plant and product engineering and support groups to optimize how products are designed, manufactured, and supported.
Factors Driving Connectivity at the Edge
Operational issues, such as analyzing and controlling devices, improving process speed/reducing latency issues, and reducing data security risks, will drive end users to deploy edge computing, as well as the need to improve asset performance and maintenance to reduce unplanned or unscheduled downtime, and the need to improve and optimize production. However, for edge computing and devices for machines, equipment, and production systems to continue to proliferate, cybersecurity concerns must be addressed. While IIoT and edge devices afford a way to connect factory ecosystems, products and equipment in the field, and even the manufacturing supply chains; these devices and connections must be made secure and reliable or manufacturers will slow down the deployment of edge and cloud technologies.
Smart manufacturing and edge computing with information-enabled operations offers virtually infinite potential to improve business performance. Companies will be able to use data that has long been stranded inside machines and processes to quickly identify production inefficiencies, compare product quality against manufacturing conditions, and pinpoint potential safety, production, or environmental issues. Remote management of this edge infrastructure will immediately connect operators with off-site experts to be able to avoid or more quickly trouble-shoot and resolve downtime events.
Finally edge and cloud computing architectures will accelerate IT and OT convergence. As a result, IT and OT professionals who previously only oversaw their own individual systems are learning about the counterpart technologies. IT professionals must have the skills to transfer their experience of enterprise network convergence and ubiquitous use of Internet Protocol into manufacturing applications. OT professionals must possess the skills to migrate from yesterday’s islands of automation to today’s plant-wide, information-centric edge and cloud architectures to enable the secure flow of information throughout the manufacturing enterprise and beyond. These skills are critical for end users to source to fully leverage their hybrid edge and cloud infrastructure.