What sets the aviation industry apart is its complex and costly manufacturing process, stringent regulations, and high levels of competition and innovation.<\/p>\n
Aircraft manufacturing requires both machine and human processes that work in tandem. With hundreds of thousands of different components to a plane, manufacturing often takes place at remote manufacturing plants before all the parts are transported to the final assembly line[1]<\/a>.\u00a0 \u00a0While the manufacturing process of a plane ranges from 3-6 months, new product design can take upwards of 8 years[2]<\/a>.<\/p>\n In addition to a complex manufacturing process, aircrafts are also subject to intense degree of scrutiny and regulation, as most countries have national aviation authorities[3]<\/a>. The necessity for high safety requirements means that the margin for error is especially low for Airbus.<\/p>\n Finally, Airbus must also contend with competition from its main competitor, Boeing. The two companies have gone toe-to-toe for decades and often release new aircrafts within the same year[4]<\/a>.\u00a0 At the recent 2017 Paris Air Show, Boeing won more orders than Airbus for the first time since 2012, putting increased pressure in Airbus to innovate quickly[5]<\/a>.<\/p>\n Within this highly competitive and regulated environment, machine learning is one of the few ways that Airbus can improve product design and efficiency quickly while keeping the margin of error low.<\/p>\n Airbus and their Use of AI and Machine Learning<\/em><\/strong><\/p>\n In April of 2017, Airbus CEO Tom Enders said in an interview,<\/em> \u201cI do genuinely believe that we are at a point where those technological changes and breakthroughs in electric propulsion, autonomous flight, artificial intelligence, machine learning, new materials, all come together, plus the data usage, and will be nothing less than a third revolution in aerospace.\u201d [6]<\/a><\/p>\n With this \u201cthird revolution\u201d in mind, Airbus has explored the possibilities of machine learning primarily within two areas: Manufacturing improvements and increased data insights for their airline customers.<\/p>\n In manufacturing an aircraft, numerous production difficulties can occur. In the short term, Airbus has utilized machine learning on the production floor to mitigate this. In the development of their newest A350 model, Airbus wanted to move faster without compromising on quality[7]<\/a>. \u00a0To meet the goal of faster production, Airbus began a data collection process that documented all the issues and actions that took place on the shop floor. The system generated recommendations to suggest the best course of action, should a problem arise. In the longer term, Airbus is hoping to more fully automate the manufacturing process with more machines and less people, further enabling higher production[8]<\/a>.<\/p>\n Airbus is also using their huge amounts of data to help provide insights and predictive analytics to airlines. In 2017, Airbus launched Skywise<\/em>, an open data platform that assists airlines with flight operations analysis, predictive maintenance, and troubleshooting on an ongoing basis[9]<\/a>. Airbus uses onboard sensor data to provide this information (Airbus\u2019 A350 model has 250,000 sensors aboard and can measure 900,000 system parameters)[10]<\/a>. They will continue to sign more airlines to this platform, and in the longer term, make this solution available for helicopters, military aircraft, and other products[11]<\/a>.<\/p>\n Next Steps for Airbus<\/em><\/strong><\/p>\n One area of AI less explored by Airbus is the notion of \u201cself-flying planes.\u201d While a large part of aircraft operations is automated, the pilot remains an indispensable part of the journey. However, it is most likely safer to be in a plane not subject to human error. I recommend that Airbus explore this possibility and invest in making this a reality. Indeed, the use cases for machine learning are infinite, not only in commercial flight, but in the defense and military sectors as well. It is in the company\u2019s best interest to invest in machine learning startups and talent that can help dream up solutions for problems Airbus has not even begun to identify.<\/p>\n Of course, questions remain about how large of a role AI and machine learning can have in aviation. Do you foresee regulatory risk in what Airbus would be able to implement going forward? Are there additional safety concerns if this industry continues to rely more on machines?<\/p>\n (Word Count: 749)<\/p>\n <\/p>\n [1]<\/a> Airbus, \u201cEnhancing Production,\u201d https:\/\/www.airbus.com\/aircraft\/how-is-an-aircraft-built\/production.html<\/a>, accessed November 2018.<\/p>\n [2]<\/a> Leggett, Theo, \u201cA350: The aircraft that Airbus did not want to build,\u201d BBC News, June 14, 2002, https:\/\/www.bbc.com\/news\/business-22803218<\/a>, accessed November 2018.<\/p>\n [3]<\/a> Federal Aviation Administration, \u201cFAA Regulations,\u201d https:\/\/www.faa.gov\/regulations_policies\/faa_regulations\/<\/a>, accessed November 2018.<\/p>\n [4]<\/a> Zhang, Benjamin, \u201cHow Airbus became Boeing\u2019s Greatest Rival,\u201d Business Insider, September 8, 2018, https:\/\/www.businessinsider.com\/airbus-history-boeing-rivalry-2018-4#in-july-airbus-took-full-control-of-the-c-series-program-from-bombardier-and-rebranded-the-innovative-carbon-composite-jet-the-a220-36<\/a>, accessed November 2018.<\/p>\n [5]<\/a> Ostrower, Jon, \u201cBoeing vs. Airbus: A new winner emerges at the Paris Air Show,\u201d CNN Money, June 22 2017, https:\/\/money.cnn.com\/2017\/06\/22\/news\/paris-air-show-boeing-airbus\/index.html<\/a>, accessed November 2018<\/p>\n [6]<\/a> AIAA, \u201cAirbus CEO Enders: Aerospace Industry On Brink Of \u201cThird Revolution,\u201d https:\/\/aviation.aiaa.org\/Notebook.aspx?id=15032387008<\/a>, accessed November 2018<\/p>\n [7]<\/a> Ransbotham, Sam, \u201cAccelerate Access to Data and Analytics With AI,\u201d MIT Sloan Management Review, August 22, 2017, https:\/\/sloanreview.mit.edu\/article\/accelerate-access-to-data-and-analytics-with-ai\/<\/a>, accessed November 2018.<\/p>\n [8]<\/a> Hepher, Tim, \u201cAirbus chief says automation to revamp jet manufacturing, help meet demand,\u201d Reuters, June 3 2018, https:\/\/www.reuters.com\/article\/us-airlines-iata-airbus-interview\/airbus-chief-says-automation-to-revamp-jet-manufacturing-help-meet-demand-idUSKCN1IZ0AZ<\/a>, accessed November 2018.<\/p>\n [9]<\/a> Airbus, 2017 Annual Report, p. 9, https:\/\/www.airbus.com\/investors\/financial-results-and-annual-reports.html#annualreports<\/a>, accessed November 2018.<\/p>\n [10]<\/a> ESMT Knowledge, \u201cTom Enders: Digitalization fuels modern aerospace and aeronautics,\u201d https:\/\/knowledge.esmt.org\/article\/tom-enders-digitalization-fuels-modern-aerospace-and-aeronautics<\/a>, accessed November 2018.<\/p>\n [11]<\/a> Airbus, 2017 Annual Report, p. 9, https:\/\/www.airbus.com\/investors\/financial-results-and-annual-reports.html#annualreports<\/a>, accessed November 2018.<\/p>\n","protected":false},"excerpt":{"rendered":" In an increasingly competitive and innovative landscape, can Airbus use machine learning to improve efficiency while not sacrificing on safety?<\/p>\n","protected":false},"author":11443,"featured_media":36497,"comment_status":"open","ping_status":"closed","template":"","categories":[5075,346,161],"class_list":["post-36449","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artificial-inteligence","category-machine-learning","category-manufacturing","hck-taxonomy-organization-airbus","hck-taxonomy-industry-aerospace","hck-taxonomy-country-netherlands"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/Skywise.png\")
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