{"id":31437,"date":"2018-11-13T12:30:16","date_gmt":"2018-11-13T17:30:16","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-and-ai-at-delta-air-lines\/"},"modified":"2018-11-13T12:30:16","modified_gmt":"2018-11-13T17:30:16","slug":"machine-learning-and-ai-at-delta-air-lines","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-and-ai-at-delta-air-lines\/","title":{"rendered":"Machine Learning and AI at Delta Air Lines"},"content":{"rendered":"

Right in the middle of the company\u2019s 2017 Investor Day presentation, Delta highlighted the following: \u201cExpand machine learning and artificial intelligence.\u201d[1]<\/a> As Anastassia Fedyk writes in 性视界 Business Review, \u00a0\u201cMachine learning is, at its core, a set of statistical models meant to find patterns of predictability.\u201d[2]<\/a> If large data is critical for machine learning than Delta is no stranger to it. Delta Air Lines\u2014with more than 180 million passengers flown annually, more than 15,000 flights per day, and more than 80,000 employees worldwide\u2014is no stranger to the AI and machine learning revolution. [3]<\/a> In fact, a 2017 survey of the industry reported that half of all global airlines plan to make significant investments in their AI and machine learning capabilities within the next three years.[4]<\/a><\/p>\n

Airlines generate immense amount of data and information which often goes unused. To better explain, Virgin Airlines reported that a single flight generates roughly half a terabyte of data.[5]<\/a> Until very recently, that was such a huge amount of information it was unusable. However, \u201cwith machines as sidekicks, though, people can more quickly find valuable insights buried in big data,\u201d writes James Wilson in a May 2016, 性视界 Business Review Article.[6]<\/strong><\/a> <\/em>Finding these valuable insights is critical in reducing delays and disruptions, improving customer experience, and increasing the bottom-line.<\/p>\n

Delta has been investing in the AI space for more than a decade. As of 2003, the company was using predictive fleet maintenance programs to \u201cfilter and integrate data from [the company\u2019s] physical assets, contextualize it and provide actionable insights on their current technical condition.\u201d[7]<\/a> Maintenance personnel are relayed signals on problematic parts long before these parts fail. In the twelve months ending March 2018, Delta\u2019s predictive analytics software prevented nearly 1,200 aircraft delays and cancellations.[8]<\/a><\/p>\n

AI and machine learning in the airline industry\u2014especially with Delta\u2014is extraordinarily complex. For example, airlines have been using AI algorithms in the ticket pricing space for over a decade now, but have only recently made large inroads in fully autonomous flying. First generation technology included flight management systems and automatic cabin pressurization technology. Under current development, with near-term deployment are machine learning initiatives such as predictive analytics around passenger behavior to better model revenue management, enhanced biometric screening to eliminate the need for boarding passes and baggage tags and improved rerouting due to maintenance and weather issues.[9]<\/a> Delta\u2019s Director of Innovation, Nicole Jones, stated, \u201cIn the long-term, passengers could transfer from the curb to the gate without the need of a printed or mobile boarding pass.\u201d[10]<\/a> Furthermore, long-term initiatives that the company has started to invest in include fully autonomous flights, and entirely self-serve experiences in which customers never interact with a Delta employee, from a check-in agent, to a gate agent to a flight attendant, machines and AI would usher him or her through the process.<\/p>\n

Airlines\u2014Delta included\u2014only have limited means to process this immense data and fully understand it today. Though systems have become increasingly good at collecting and storing critical data, how best to put it to use can remain elusive. Furthermore, existing legacy systems can often clash with new-modern systems. An Oliver Wyman report on the space states, \u201clegacy systems not flexible enough to accommodate more sophisticated analytics and artificial intelligence systems,\u201d will inevitably create issues. Further investment and hiring the right people who understand these issues can greatly reduce friction as the industry grows into itself.<\/p>\n

One major roadblock remains: are customers truly comfortable with a fully autonomous airline industry or will humans always be necessary? As fliers, will customers feel safe knowing that there may not be a pilot on the plane, or that a mechanic never checked the engine because a machine did instead? Will we feel comfortable handing our luggage with our valuables to a robot that scans our face and promises delivery of the baggage to a final destination? That final question is particularly potent because Delta is currently using machine learning algorithms to tie a customer\u2019s face to his or her bag. Gone away are the day of luggage tags.[11]<\/a><\/p>\n

The end goal of all of this is to create a seamless passenger experience while delivering maximized profit to the company. That means fewer delays, a more enjoyable traveling experience, near-zero aircraft issues and perfect customer service. The airline industry hopes in many ways machine learning and AI can deliver on these long desired of goals.<\/p>\n

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Word Count: 732<\/p>\n

[1]<\/a> \u201cInvestor Day, 2017,\u201d Delta Airlines Investor Day, https:\/\/s1.q4cdn.com\/231238688\/files\/doc_presentations\/2017\/Delta-Air-Lines-Investor-Day_2017.pdf<\/p>\n

[2]<\/a> \u00a0Anastassia Fedyk, \u201cHow to Tell if Machine Learning Can Solve Your Business Problem,\u201d\u00a0性视界 Business Review<\/em> (November 25, 2016)<\/p>\n

[3]<\/a> \u201cWorldwide Service,\u201d Corporate Stats and Facts, Delta, https:\/\/news.delta.com\/corporate-stats-and-facts.<\/p>\n

[4]<\/a> \u201cAirlines Turn to AI as They Up IT Spending,\u201d Business Travel News, http:\/\/www.businesstravelnews.com\/Research\/Artificial-Intelligence\/Airlines-Turn-to-AI-as-They-Up-IT-Spending<\/p>\n

[5]<\/a> \u201cBoeing 787s to create half a terabyte of data per flight, says Virgin Atlantic,\u201d ComputerWorld UK, https:\/\/www.computerworlduk.com\/data\/boeing-787s-create-half-terabyte-of-data-per-flight-says-virgin-atlantic-3433595\/<\/p>\n

[6]<\/a> James Wilson, Sharad Sachdev and Allan Alter, \u201cHow Companies are Using Machine Learning to Get Faster and More Efficient,\u201d\u00a0性视界 Business Review<\/em> (May 3, 2016)<\/p>\n

[7]<\/a> \u201c7 Ways Airlines Use Artificial Intelligence and Data Science to Improve Operations,\u201d AltexSoft: Software R&D Engineering, https:\/\/www.altexsoft.com\/blog\/datascience\/7-ways-how-airlines-use-artificial-intelligence-and-data-science-to-improve-their-operations\/<\/p>\n

[8]<\/a> Ibid<\/p>\n

[9]<\/a> \u201cAirlines Turn to AI as They Up IT Spending.\u201d<\/p>\n

[10]<\/a> Ibid<\/p>\n

[11]<\/a> Kristina Velan, \u201cHow Artificial Intelligence Will Change the Airline Passenger Experience,\u201d Apex, <\/em>January 4, 2018, https:\/\/apex.aero\/2018\/01\/04\/artificial-intelligence-change-airline-passenger-experience<\/p>\n","protected":false},"excerpt":{"rendered":"

Understanding how AI and machine learning are affecting Delta and the world's airlines.<\/p>\n","protected":false},"author":11693,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,346],"class_list":["post-31437","hck-submission","type-hck-submission","status-publish","hentry","category-artifical-intelligence","category-machine-learning","hck-taxonomy-organization-delta","hck-taxonomy-industry-air-transportation","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nMachine Learning and AI at Delta Air Lines - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-and-ai-at-delta-air-lines\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning and AI at Delta Air Lines - 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