Analytics and prognostics in plants used to be the terrain of large, leading-edge companies. The ability to crunch plant data to predict failures, optimize throughput, and determine best-practices was the domain of huge concerns such as Procter and Gamble, a company that can save a fortune by making a plant half a percent more efficient.
That’s changed as analytics and prognostics -- now called by the trendy term Big Data -- has gone mainstream. GE Intelligent Platform spells out a number of benefits that come from using big data to analyze and optimize plant operations. Analytics can coordinate operations with reliable, timely, and contextualized information. You can reduce operating costs with real-time monitoring and data analysis. You can improve enterprise connectivity with anywhere, anytime information, and you can enhance decision-making for improved performance with integrated history, alarming, and trending.
Those using analytics to improve plant efficiency are going deeper into the use of data. Where they used to use analytics to tweak equipment for improvements, they’re not using data to truly optimize their systems. “Companies are using analytics to go from making their equipment highly available to fully optimizing it,” Brian Courtney, GE Intelligent Platform’s general manager of industrial data intelligence, told Design News. “You can monitor the equipment to get better availability and better throughput, or you can go further to the optimization side.”
While analytics is becoming more available through PLCs, it’s mostly the big companies that are taking advantage of it. For one thing, with high-volume production, small tweaks make a big difference. “On the big-data side, it’s the large companies that are doing it. Big companies know they have a lot of data. The small and medium companies are not doing it so much,” Courtney told us. Also, newer equipment is also easier to manage through analytics. “The adoption is mostly by plants that are newer. If you’re running a printing press from 30s technology, you’re not going to be doing analytics. But if you are running a printing plant that is using new technology, you’ll be interested.”
Big data can also be used to compare one plant to another on the equipment-to-equipment level. “If you have 50 to 200 plants, you want to see the best practices across all of the plants. You can see what aspects of the equipment breaks down the most. The big data gives you temperature, pressure, and maintenance records. You can see shop floor notes that offer insight into why some equipment is breaking. When you’re producing a lot of data, you can use that data to get your efficiency up and your defects down.”
While North America and Europe are gaining a reputation for highly technological manufacturing, it’s the Asian plants that are best equipped to use analytics -- simply because Asian plants are newer. “Geography isn’t an issue as to whether plants are using analytics. How new the plant is makes the most difference. More companies in Asia have newer equipment, since they’ve built their plants more recently. New plants are making it easier to collect analytics, even though analytics can be used to get the most value out of older equipment.”
As for what industries are best equipped to use big data, Courtney points to consumer products, mining, and the extractive industries. “We see a lot of analytics used by consumer goods companies. They’re using analytics to see who is buying what and why,” he says. “Mining is also picking up in analytics use. We’re seeing mining use analytics in South America, Australia, and South Africa. The oil and gas industry is using big data across the board.”