Overcoming barriers: An evolutionary approach to edge computing

Pushing industrial control intelligence to the edge—closer to where manufacturing and production processes are happening—offers tremendous potential for increasing business efficiency and agility. Add in the ability to perform real-time analytics on the plant floor, and the possibilities for optimizing operations are endless.

This is not lost on operational technology (OT) professionals. According to a recent market report by ARC Advisory Group, 91 percent of industrial automation users surveyed said that having better systems and connectivity at the edge will improve real-time decision making. Early adopters are moving aggressively to push intelligence to the edge as part of a larger Industrial Internet of things (IIoT) strategy. So why isn’t everyone jumping on the edge computing bandwagon?

Change equals risk

A major challenge is mindset. Industrial enterprises tend to be risk-averse. Anything perceived as posing a risk of disrupting manufacturing processes will face an uphill battle. This explains why so many plants are filled with aging industrial control systems, including some applications, operating systems and hardware that are no longer supported. For the OT personnel responsible for keeping production lines up and running no matter what, change equals risk.

For some, the connectivity required to handle the flow of data essential to intelligent edge systems also equates to risk. Freestanding systems that run just a single process are difficult to hack, which is why you often find dedicated desktop workstations or servers running a single application or machine. Sure, it’s inefficient, but many OT professionals view this as the price of limiting risk.

Gradual approach

So how can industrial enterprises overcome these institutional barriers and begin to reap the benefits of the intelligent edge? In a word, gradually. Building out an edge strategy can be a multi-stage evolution, with each stage building on the last. Based on my experience working with companies across the industry spectrum, I see a four-step process.

• Step 1: Modernizing industrial control systems. The first, fundamental step toward the intelligent edge is replacing aging, end-of-life and proprietary control systems with modern technology. Implementing up-to-date, standards-based, fault-tolerant hardware and software and leveraging virtualization to consolidate servers, provides immediate benefits in terms of efficiency, reliability and availability while simultaneously providing a foundation for more sophisticated edge computing capabilities. In fact, implementing systems that are simple to deploy and that enable remote management, maintenance, updating and servicing is a critical priority for OT organizations where IT skill sets may be limited or absent.
• Step 2: Connecting systems. A key element of an intelligent edge strategy is enabling bi-directional data exchange to and from devices and sensors at the edge. Establishing connectivity at the edge also enables historical data to be captured and sent to the cloud for non-real-time analytics to spot important trends and perform business planning. To overcome concerns about security and stability, it is critical that the edge infrastructure is designed to provide appropriate levels of end-to-end security. That includes both physical security, controlling access to industrial control systems throughout the plant, as well as network security, protecting data access both within the corporate network and external networks.
• Step 3: Enabling real-time analytics. Deploying data analytics within industrial control systems represents perhaps the greatest potential benefit of the intelligent edge. Sensor data can be monitored and analyzed in real time, providing operators with instant insights on impending problems and enabling proactive interventions to issues that could impact product quality or production interruption.
• Step 4: Automating industrial control. Ultimately, edge systems could power fully autonomous production processes. Through artificial intelligence, these systems will analyze a wide range of sensor data in real time, initiating interventions to optimize efficiency or avoid problems, with no human interaction. Just as the autonomous car has the potential to reduce accidents and speed commutes, the autonomous plant holds tremendous promise for maximizing productivity and minimizing costs for manufacturing and process industries.

Start small…and start now

Starting small with limited proof-of-concept projects is a perfectly reasonable approach to beginning the intelligent edge journey. Modernizing and virtualizing your industrial control infrastructure will pay dividends in greater simplicity, reliability and maintainability. And it sets the stage for the powerful capabilities to come.

The important thing is to start now. Given the increasing intensity of global competition, the real risk is doing nothing.