Analytics: Converting IoT Data to Value
Bryan Saunders, chief industrial consultant for the global IoT practice at SAS, points out that SAS has deep experience in the analysis space. “We’ve been in that game for 40 years plus.
“The heart of the digital twin is the analysis,” he says. “It’s not just about ‘Can you collect the data’, but can you turn them from data into valuable transformative information? The main driver for this is analytics.
“This means that you must be able to collect and move the data in efficient ways. Then you have to understand what it tells you. But beyond that, you have to drive the action so you can achieve the expected result on the flip side. “
Saunders concentrates on the industrial side. “My work is focused on using heavy industry-connected assets to improve accessibility, efficiency, security and reliability in Energy and manufacturing sectors. Take for example. A powerful gas turbine. Since I can effectively understand the baseline position of the asset, I can look at how it has historically failed, and ways that I think it may fail in the future, and use these analytics techniques to make predictions for maintenance activity so you don’t has a catastrophic failure.
“When you have visibility into what the future may have, it drives significant efficiencies. Real-time analytics detection and health assessments have proven to be extremely valuable in predictable maintenance. Studies show that using the data in this way does not reduce scheduled maintenance by up to 80 percent. “
GE, a leader in virtual twin technology, uses a combination of artificial intelligence-driven analysis and visual sensors on matchbox-sized robots to look for cracks in work engines. Recent advances allow cameras to detect cracks, even on dirty or rusty turbine blades. Visual sensors on drones can inspect for corrosion on the 200-foot-high stacks that burn off excess gas at oil and gas production sites.
Tesla Motors is another example of a company deeply invested in digital twin technology to provide better service and reliability for car owners. Tesla creates a digital twin of every car it sells. Tesla then updates software based on individual vehicle sensor data and uploads updates to its products. This data-driven software development process enables a more efficient resource allocation and a significantly better user experience for the vehicle owner.
At its plants, Bosch compares sensor-driven production data with a digital twin of production lines running at 100 percent efficiency. As a result, production deviations can be quickly identified and trends can be more easily identified. These smart, connected production lines have enabled a 25 percent improvement in the company’s electronic stability program and automatic braking systems.
Saunders looks IoT and analysis being transformative in four major sectors: smart cities, connected vehicles, connected factories and smart grid or utilities. All of these transformations ultimately create value for connected consumers. For Saunders, IoT and analytics have proven to be an indispensable competitive advantage at every stage of a product’s life cycle, from design to design Production, maintenance and operation.
As he explains, “A multitude of industries are coming to understand that this is the way we must do business to succeed and to remain competitive in this new world of IoT.”