Data has a story to tell--at least to those who know how to listen to it. However, data scientists who can connect the dots and bring forth insight from a mashup of unstructured and structured data are a rarity in today’s world. Many businesses are looking to supplement the capabilities of data science pros using the latest technologies available, such as analytics platforms empowered with artificial intelligence (AI) and machine learning.
One such business is Japanese aviation manufacturer AeroEdge, which was faced with critical engineering and manufacturing challenges that could only be addressed by garnering a better understanding of data. The Ashikaga Tochigi, Japan-based company was founded in 2016 and is now poised to become a leader in the global aviation industry, thanks to improved data analytics.
“Analytics proves crucial to ensure every step of the manufacturing process is optimized,” said Shingo Yamamoto, CIO and Chief of IT Strategy at AeroEdge.
To address the challenges posed by a large volume of data in a highly critical production environment, AeroEdge turned to an Australian company called Yellowfin, which specializes in building analytics platforms, exemplified by the company’s Signals and Stories offerings.
Turbine Blades Made with Titanium-Aluminum Alloy
AeroEdge is one of only two manufacturers in the world producing low-pressure turbine blades, which are made from a titanium-aluminum alloy. The turbine blades are destined for next-generation LEAP jet engines and must meet exacting standards, in which there is no room for error. The precision of each blade is measured to within thousandths of a millimeter. These are exacting standards that are dictated by extensive regulations. The challenge for AeroEdge was one of maintaining those standards while also scaling to mass production volumes.
“With Yellowfin implemented, our operators now have an increased cost awareness, and we have improved our business profitability, so everything has been moving in a great direction,” Yamamoto said. AeroEdge uses Yellowfin analytics to identify patterns that lead to manufacturing issues and address them 80% faster, which in turn reduces production time.
“Yellowfin has played a significant role in enabling AeroEdge to meet the demand of their sizeable new contract with a French aviation engine manufacturer,” said Yugo Hayashi, Managing Director of East Asia at Yellowfin. “Yellowfin’s augmented analytics capabilities are changing the BI market and empowering organizations to make the most of their huge volumes of data. They are now being notified of patterns in their data that they could have never uncovered with manpower alone.”
While Yellowfin’s technology plays into the success of AeroEdge, other businesses can learn from that success and begin to focus on more critical elements to help the bottom line. The key here is to use technology that can provide meaningful analysis at the speed of business. For example, AeroEdge is looking to build on its data analytics success.
AeroEdge Now a Fully Data-Driven Business
“AeroEdge is now a fully data-driven manufacturing business with a strong and healthy data culture among all its employees and is now exploring predictive analysis,” Hayashi said.
The company is now able to analyze huge amounts of data from operators, equipment, measurement points and arrival dates of materials in real time; it also automatically generates a list of incompatible items in the Aeroedge production workflow.
Yuta Uchiyama, manager of the IT Strategy Department at AeroEdge, added: “We are now considering using data-driven analytics to help us predict incompatibility.“ AeroEdge is looking to make the most of their huge volumes of data, aiming to automatically identify patterns in the data that they could have never uncovered with manpower alone.
“The core material is titanium aluminum. However, a slight variation in the content of aluminum and oxygen changes the processing performance. So we are exploring ways to reduce the incompatibility ratio in the future. If analytics, such as YellowFin Signals helps to analyze these incompatibility issues, I believe it will lead to further reduction of the manufacturing processing time and improve the product quality,” Uchiyama said.