New software feature proven to increase the accuracy of human-based data entry from 34% to over 90% – greatly improving the quality of the information airlines rely on to make fleet maintenance decisions.
LONDON, UK – October 15, 2019 – ATP, a leading provider of information services and software solutions for the aviation industry, officially launched a new machine-based learning application, designed to help improve the accuracy of documenting Air Transport Association (ATA) codes. Revealed today at the 2019 Aviation Week MRO Europe show in London, the new feature is the latest in the continuing evolvement of the company’s ChronicX® software suite, a solution used by over 25% of the world’s commercial airline fleet to detect recurring or chronic issues on aircraft.
With a medium-sized airline fleet producing 1,000 new records each day, it is common for up to 40% of defects not being flagged correctly to maintenance control. The reason is that most defects are being reported under incorrect ATA codes, the standard used by pilots, aircraft maintenance technicians, and engineers within the industry. The new ChronicX® ATA recoding feature, can automatically predict the right 4-digit ATA code for a defect based on its description, regardless of how it has been entered or reported.
In addition to providing 4-digit accuracy at a 90%+ level, the new recoding application continuously learns from user feedback – allowing prediction accuracy to increase exponentially with continued usage. This breakthrough allows airline teams to recode all their defects with reduced effort and in a fraction of the time it would take with other systems and processes. The time saved by maintenance control analysts means that specialist personnel can focus on more critical and costly fleet maintenance issues. It also means that the data they are working with is more reliable, contributing to better and more well-informed decisions.
“The airline industry has struggled for years with the accuracy of the ATA codes being applied to maintenance issues and its impact on the data they rely on to ensure the safety of their aircraft”, claimed James Geneau, Chief Marketing Officer at ATP. “By working closely with our global airline customers, our product team identified this as an opportunity where our in-house machine-learning experts could develop a solution for the industry. Maintenance technicians are extremely busy and focused on quickly getting planes fixed and moving”, added Mr. Geneau. “This new feature allows them to stay focused on the job at hand while maintenance control can rely on technology to ensure a higher degree of accuracy in the overall data needed to do their job”.
After years of participating at the Aviation Week MRO Americas conference, this is the first time ATP has exhibited and presented its latest software updates at the European conference. Customers of ChronicX can begin using the new ATA recoding feature immediately by speaking with their dedicated account executive. Those attending the 2019 Aviation Week MRO Europe conference can also learn more by visiting the ATP display (Booth #210) between October 15-17, 2019.
ATP is a global information services and software solutions company focused on making flying safer and more reliable. ATP Information Services is the general and business aviation industry’s source for aircraft technical publications and regulatory information, connecting more than 45,000 maintenance professionals to the latest OEM content and airworthiness directives. ATP Software Solutions is the leading provider of repetitive defect and troubleshooting applications focused on reducing operating costs, improving reliability, and supporting technical knowledge sharing and collaboration for the military, commercial aviation, and OEMs.
The company has deployed solutions worldwide to support multiple Fortune 100 companies. As a global company, ATP has more than 6,700 customers in 137 countries, with almost 50 years of experience in the information services and software industries. For more information, visit www.atp.com.