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Information technology (IT) leaders have traditionally focused their efforts on improving and modernizing information systems. However, as production environments become more automated, leaders are placing more emphasis on creating value from the data supplied from operational technology (OT) systems. Gartner defines OT as “hardware and software that detects or causes a change, through the direct monitoring and/or control of industrial equipment, assets, processes and events.”
Copious amounts of data are produced on the factory floor every second. However, for all this data to be useful for those on the plant floor, it requires an additional layer: context. By adding context to the data collected on the factory floor, OT teams can obtain a better understanding of the insights the data holds and how it affects the equipment and processes they are responsible for.
Let’s look at how manufacturers can enhance outcomes along the value chain by gathering and contextualizing information from OT systems.
With operational technology, context is king
The push for automation in the IT field is well underway. Business process technologies like robotic process automation are well-established methods of enhancing efficiency and lowering the risk of human error. And now emphasis is being placed on improving operational efficiency to the same degree. Establishing OT connectivity provides factory floor workers with real-time information on everything from product quality and variance to machine performance, process efficiency and manufacturing operations.
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However, OT data without context only represents meaningless streams of measurements. Obtaining context from OT systems requires gathering data from the machine layer — including the health and performance of those machines and the quality of the manufactured products — in a way that can be tied back to the organization’s operations and business metrics. Integrating operational data into business decisions is key for improving processes and managing variability in extremely complex environments.
Data can signify various things depending on how it’s used and interpreted, so it’s critical that information derived from manufacturing systems is contextualized and related to pertinent information to deliver the required insights. Since OT data comes from multiple sensors and devices, it can amount to large volumes of raw data streams that require manual tagging.
This can be a laborious process — unless the contextual information is readily available from the source or data context is inferred automatically through machine learning (ML) and intelligent pattern matching.
Better data: Better decision-making
Here’s an example of the value of contextualized data. An automotive supplier deployed an alarm application to monitor its ovens. Coupling an oxygen intake model with the oven sensor data, they now predict oxygen intake alarms 24 hours in advance, with over 90% accuracy. This translates into less unplanned — and costly — downtime and reduces maintenance and equipment costs.
By delivering OT data context, all systems that rely on this data can be connected by a digital thread, also known as the value chain. This is a seamless flow of data that unifies systems, processes, equipment and products across the product lifecycle.
Improving the quality and integration of data results in better decision-making at all levels, from the early discovery phases of a product through development, manufacturing, and its final delivery to market. Having this data available across production operations and the supply chain helps business leaders to easily identify and address bottlenecks and inefficiencies.
Integrating IT and OT
Manufacturers gathering and acting on contextualized OT data as part of their integrated decision making are already witnessing enormous efficiency gains. But tighter integration between IT and OT is becoming more essential as OT infrastructures like plants, devices and production lines are tied into digital transformation initiatives.
OT and IT are not as effective when they are isolated from each another. By breaking down these siloes and allowing a free flow of data and analytics sharing between OT and IT, manufacturers can free up resources and provide insights that power rapid, scalable and secure connectivity.
Integrating OT and IT can directly impact whether an organization remains competitive or advances its competitive standing in the global manufacturing landscape. The integration of OT and IT not only delivers more data context, it enables analytics that helps manufacturers understand and improve their factory operations. Comprehensive analytics gathered from insights from sensors, inventory systems, automation systems, robots and safety equipment provide manufacturers with a complete, 360-degree view of their operations.
Adopting a data-driven strategy across both IT and OT systems and directly connecting them to decision-making processes is a sure-fire way for manufacturers to quickly and easily reach their transformation goals. From product design to plant management, the information gathered from these systems can help manufacturers strengthen the effectiveness of their operations and better meet their customers’ expectations and demands.
Claudia Chandra is chief product officer for data, AI, edge, and analytics at Rockwell Automation.
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