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Manufacturers’ slow, but steady migration to the cloud

Most are opting for a hybrid approach that pairs cloud-based analytics and production applications with traditional on-premise control systems.

Inductive Automation sees its MQTT-based Ignition IIoT platform as a cloud-based data collection hub for on-premise controls systems.
Inductive Automation sees its MQTT-based Ignition IIoT platform as a cloud-based data collection hub for on-premise controls systems.

While businesses of all types make a steady march to the cloud, manufacturers are pursuing a hybrid strategy, opting to retain some foundational plant-floor systems on premise while earmarking analytics and production applications for migration to the new environment.

Similar to mainstream productivity applications, the cloud promises a host of benefits for certain manufacturing platforms. Easier collaboration across multiple plant sites, unlimited capacity and scalability—particularly for peak cycles—and, in some instances, reduced costs are the primary benefits building a case for cloud migration, experts say. At the same time, ease of deployment and ongoing maintenance of systems is another big upside given that most cloud-based systems are quicker to deploy and easier to learn, and don’t require a dedicated staff or hardware resources to keep up and running.

Nevertheless, cloud adoption for manufacturing applications, though accelerating, still lags behind mainstream applications in part because of lingering concerns surrounding security, especially at the programmable logic controller (PLC) level, notes Travis Cox, co-director of sales engineering at Inductive Automation, a provider of web-based industrial automation software. “There is still skepticism about moving to the cloud in control systems, in particular, primarily due to security reasons,” he says, explaining that many legacy PLCs don’t have the real estate or processing horsepower to accommodate built-in security capabilities, which increases their vulnerability.

Although the cloud paradigm delivers clear advantages for systems like enterprise resource planning (ERP), manufacturing execution systems (MES) and analytics, it doesn’t make sense for many traditional plant-floor systems, including control systems and PLCs, experts say. “I don’t think you’re ever going to see a full migration to the cloud for all plant-floor technologies,” says Matt Newton, director of technical marketing at Opto 22. “There’s always going to be a need for local monitoring and control—a lot of these applications are real time or mission control and thus can’t rely on decision-making in the cloud for everything.”

Consider, for example, the ramifications of a cloud-based ERP system going offline because of an Internet connectivity failure. ERP downtime might translate into lost orders or an inability to get up-to-date reporting on consumption or production status for a time, but many companies have manual workarounds in place to compensate for such disruptions. However, that kind of resiliency is much more difficult to replicate with supervisory control and data acquisition (SCADA) or similar mission-critical plant-floor systems, which are far more dependent on a real-time response, according to Matt Wells, general manager for automation software at GE Digital.

“The cloud doesn’t have the level of reliability customers expect out of shop-floor systems,” he explains. “If you move SCADA to the cloud and you lose a connection, the [production] line literally stops and you are dead in the water, losing millions of dollars a minute. There is no way to recover the serious cost impact.”

What works, what doesn’t
Rockwell Automation is eyeing a hybrid approach for its strategy, moving the manufacturing intelligence and production management components of its FactoryTalk suite to the cloud to benefit from increased processing power and scalability, says Todd Montpas, business manager in Rockwell’s Information Solutions Group. Though quality systems or warehouse and inventory management applications can run effectively in the cloud, it’s a different story for overall equipment effectiveness (OEE) applications related to specific plant-floor equipment. These scenarios require edge-based computing capacity to close the loop on analytics, he says.

“Some data collection and data orchestration services need to remain at the plant level to provide instant feedback to plant personnel,” Montpas says, explaining that OEE cycle time on a machine is 20-45 seconds, so there needs to be a service or piece of software that sits on premise to handle that data collection. “We still need to be able to close the loop at the automation layer or edge devices for things that are time-critical.”

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