The solution uses natural language processing and advanced content analytics to interpret and cross-reference text, finding connections that humans may miss, such as the effect of changing seasons on particular component replacement and chronic defects.
“This changes the equation for airlines, allowing them to spend less time on maintenance and more time in the air.”
Business challenge
Korean Air needed a way to unlock vast amounts of information in its maintenance records, enabling aircraft technicians to diagnose and solve problems quickly.
Transformation
Korean Air uses a cognitive discovery system to analyze structured and unstructured data from its ERP environment, detecting patterns in how and why equipment fails to diagnose and fix problems.
Results
90% shorter lead times for problem resolution by simplifying data discovery and unearthing root causes
Reduced delays and cancellations leading to higher customer satisfaction and operating efficiency
Alleviates decision making by finding solutions to complex problems without manual search and analysis processes
Solution components
Watson Discovery
Business challenge story
Business challenge
Aircraft maintenance is a crucial part of keeping planes in the air and passengers safe, and technicians are under immense pressure to fix problems as quickly as possible to avoid flight delays and cancellations. But human capacity is limited.
At Korean Air, technicians handle 170,000 maintenance cases every year, keeping extensive records on equipment malfunctions, corrective actions and routine work as reference points for diagnosis and problem solving. Most of the data was unstructured and accessible only through keyword search; therefore technicians had to know where to look, what to look for and how to recognize the right answer when they found it. As a result, the process was slow and often yielded inaccurate or incomplete insights. Korean Air needed a way to unlock the vast amounts of information in its maintenance records, enabling its aircraft technicians to understand and solve problems quickly.
Transformation
Cognitive transformation
Built on cognitive data discovery technology, Korean Air’s maintenance defect analysis system offers a superhuman ability to rapidly synthesize vast amounts of historical data and find subtle clues on how to solve complex mechanical problems. The IBM Korea Lab team provided consulting and training services, while a project manager from IBM Global Technology Services - Infrastructure Services managed the rollout of the hardware systems required to support the software. Ingesting and analyzing structured and unstructured data from the airline’s ERP environment, the solution detects patterns in how and why equipment fails and recommends courses of action to help technicians implement the most effective fix.
The system uses natural language processing (NLP) and advanced content analytics to transform raw unstructured data, mainly in the form of technician notes, into insights focused on the problem at hand. For instance, Korean Air can compare the performance of different flights, types of aircraft and maintenance crews to isolate mechanical, environmental and human variables that may affect equipment, such as the increased need for tire replacements in the summertime, given higher temperatures and passenger volumes, and potential solutions for reducing wear and avoiding failures.
Results story
Quantifiable benefits
Korean Air shortened its maintenance lead times by 90 percent by dramatically simplifying how aircraft technicians search and analyze data to determine root causes and solutions. The airline also reduced the number of flight delays and cancellations caused by maintenance issues, which made a positive impact on customer satisfaction and operating efficiency. In addition, the cognitive solution alleviates much of the burden on technicians to know exactly what to look for as they seek answers to complex problems, and reduces the time they spend searching, exporting and analyzing the data.
Aircraft technicians are trained to understand complex systems and notice patterns of equipment failure, but human capacity is limited, especially when it comes to using big data to understand rare or novel situations. Korean Air is supplementing human intuition with a cognitive computing solution that can ingest larger, more diverse data sets, detect more subtle connections and reach conclusions faster. This changes the equation for airlines, allowing them to spend less time on maintenance and more time in the air.
In the past, aircraft technicians relied heavily on experience to solve maintenance challenges, having to know what to look for and how to analyze it for diagnostic purposes. Now, the cognitive search and discovery solution does the work for them, sifting through years of unstructured historical records to find connections that illuminate root causes and point to the right solution.
The solution creates a system of cognitive discovery and decision making by extracting insights that can be applied to the puzzle at hand, helping aircraft technicians understand the problem faster, make better decisions about how to fix it and keep flights running smoothly.
About Korean Air Lines Co. Ltd.
Korean Air Lines Co. Ltd. is South Korea’s largest airline as measured by fleet size, international destinations and international flights. From its headquarters in Seoul, the company operates 166 airplanes and flies to 127 destinations, including 114 international locations in 50 countries on six continents. Korean Air reported revenues of over USD 13 billion in 2014.
About Incede.ai
Incede.ai is the AI division of Locus Solutions, Inc.; harnessing the power of Data, AI and Machine Learning
With more than half of the world’s data in natural language, Incede.ai solves business problems and creates a competitive advantage by leveraging the power of Watson AI and machine learning.As thought leaders, we collaborate with stakeholders to deploy AI solutions that deliver real value.