If an enterprise hasn’t undergone a significant digital transformation and isn’t already well down the path to a cloud migration, something is wrong.
The events of the last year are accelerating the digital transformation in virtually every industry as businesses are prioritizing connectivity, data and analytics to help them back better business decisions and keep operating during economic downturns and global disasters.
That is even translating to industrial sectors with the proliferation of the Internet of Things (IoT) that enables companies to gather and analyze data about how machines are operating on the manufacturing floor, giving them the insight they need to better manage equipment and make necessary improvements.
A recently announced collaboration between IBM, Siemens and Red Hat are one of the best examples of how digital transformation is coming for the manufacturing floor.
The joint offering leverages Mindsphere, Siemens’ industrial IoT-as-a-service solution and applies IBM’s open hybrid cloud approach to expand the deployment and flexibility of the solution. It is designed to enable customers to run MindSphere on premise for enhanced speed and agility at the factory level as well as through the cloud for IT support.
According to Pierre-Henri Gabriel, Industry 4.0 executive of the global industrial sector at IBM, the collaboration adds a delivery model to the existing MindSphere solution, making it available on Kubernetes via Red Hat OpenShift in a full SaaS on-premise model to help reduce factories’ IT workload.
“We believe both Siemens and IBM that … the OT world is very much going to like the idea not to have to staff IT people in factories and consume the solution entirely as if it would be in the cloud,” Gabriel says.
MindSphere is used to collect and analyze real-time sensor data from products, plants, systems and machines. It enables users to optimize products, production assets and manufacturing processes along the entire value chain to build a real-time digital twin.
By adopting Red Hat OpenShift, customers will have the flexibility to run MindSphere solutions locally in a private cloud or through a hybrid, multi-cloud model, as well as enabling field to enterprise insights.
“We have a super reliable hardware and software platform that needs to be working 24/7 with high reliability, even if a server fails. At the same time, we have that super stable environment that collects data. We’re leveraging cloud technologies – but not from the cloud – to be able to cope with fast-changing lifecycles of IoT standards and connectivity.”
Where OT meets IT
According to Gerald Kaefer, program manager and principal key expert at Siemens, this is a perfect example of digital transformation permeating the industrial sector.
“That leads to a demand of an OT/IT convergence,” Kaefer says. “I think it’s a perfect fit that we use the IT capabilities of a leading brand and bring it together with the OT capabilities of a leading brand.”
According to Kaefer, those two sides of the equation “meet somewhere in the cloud-native space” for heavy analytics workloads, and this collaboration brings that solution down to an on-premise solution with Red hat OpenShift.
Solutions like this help bridge the gap between IT and OT. Historically, workloads and lifecycles of the two fields were vastly different.
On the manufacturing floor, the goal is to keep the technology stable for a long time, whereas IT represents more frequent updates, changes and transitions.
There are also safety requirements on the shop floor that require specific automated equipment that IT systems weren’t designed for.
“But now, if you bring analytics … you could do more and identify new types of data currently unleveraged at the shop floor where you then need the power of IT, equipment and cutting edge technology to analyze that,” Kaefer says.
Capturing new data
The digital transformation of the industry sector is generating tons of data. According to IBM, that’s about 2,200 terabytes of data each month, but much of that goes unused and unanalyzed.
What has historically gone uncaptured in the past is data about the condition of the machine that would have helped companies make better, faster decisions about fixing or replacing the machine. The shop floor just never had that kind of computing power, Kaefer says.
“But we noticed through the advent of IoT technology through the advent of protocols, the advent of AI and cheaper hardware for image processing, we could start to collect the data and leverage it to get further insights and keep machines up and running,” Kaefer says.
For example, factories could leverage this deployment to more readily analyze data about the energy being used to better comply with regulations and company goals on energy reduction and cost savings.
“You need to measure, measure, measure to understand the energy consumption of every process in real time,” Kaefer says.
According to Gabriel, that requires AI, which requires new technologies, computer power, platforms and networks.
“Then, you need a platform to host a new category of applications to collect the data produced by those technologies,” Gabriel says. “What we’re doing here is enabling the IT infrastructure and the technical infrastructure to enable this realm of new applications that are going to be developed either by providers like us or by clients.”