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IBM and Siemens this week expanded an existing alliance to include deployments of MindSphere, a managed internet of things (IoT) service provided by Siemens, on an instance of the Red Hat OpenShift platform.
The goal is to make it simpler to build edge computing applications that process and analyze data as it is being created, Siemens senior VP Raymond Kok said.
As part of that effort, Siemens was looking for an application development platform based on Kubernetes that would enable the conglomerate to build and deploy applications based on a modern microservices-based platform. “We were looking for an industrial-grade Kubernetes platform,” Kok said.
Manufacturers currently use MindSphere to collect and analyze real-time sensor data from products, plants, systems, and machines. Siemens now wants to feed that data into analytics applications that would run locally versus in the cloud. That’s critical because it eliminates latency that would otherwise be created when data is transferred over a wide area network (WAN).
IBM claims a single manufacturing site can in one month generate more than 2,200 terabytes of data from 100 to 200 sensors, most of which is never analyzed. Of course, not all data is worth analyzing, but companies aim to maximize manufacturing output, particularly during uncertain economic times.
It’s becoming more feasible to collect and analyze data on the factory floor in near real time because of the rise of 5G wireless networks capable of providing the necessary network bandwidth, Kok noted. While IoT devices collect data in real time, the analytics applications deployed by IT teams have historically resided in a datacenter. With the rise of 5G networks, it’s now easier to push IT applications onto the factory floor, Kok added.
Some of the funding for this initiative comes from an IBM Cloud Engagement Fund that is part of a $1 billion investment to advance hybrid cloud computing. In fact, one of the reasons IBM spent $34 billion to acquire Red Hat is to drive adoption of edge computing applications in private clouds that will ultimately need to be integrated with applications residing on multiple clouds. The definition of hybrid cloud computing now encompasses a wide variety of edge computing platforms, in addition to local datacenters that all need to be integrated with backend cloud services.
In the case of manufacturing environments, Siemens sees an opportunity to expand the scope of the digital services it provides to include both IT and operational technology (OT) applications running on the factory floor. In fact, the traditional pyramid relationship between OT and IT applications is starting to collapse as these platforms become more integrated, Kok said. But the IT and OT cultures within many organizations are vastly different, and bridging that divide requires a significant amount of technical expertise and political capital.
Longer-term, IBM expects the data being collected at the factory floor to also be used to train AI models, said Pierre-Henri Gabriel, Industry 4.0 executive for IBM’s Global Industrial Sector. “We have AI in the line of sight,” Gabriel said.
It’s clear both Siemens and IBM are betting manufactures would rather rely on them to automate manufacturing processes than attempt it on their own.
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