{"id":35977,"date":"2018-11-13T19:36:04","date_gmt":"2018-11-14T00:36:04","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/machine-learning-at-emirates-airlines\/"},"modified":"2018-11-13T19:36:04","modified_gmt":"2018-11-14T00:36:04","slug":"machine-learning-at-emirates-airlines","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/machine-learning-at-emirates-airlines\/","title":{"rendered":"Machine Learning at Emirates Airlines"},"content":{"rendered":"

Machine Learning at Emirates Airlines<\/u><\/strong><\/h1>\n

Why do you think the megatrend you selected is important to your organization\u2019s<\/strong><\/p>\n

management of process improvement and\/or product development?<\/strong><\/p>\n

In today\u2019s competitive market, successful airlines have been able to differentiate themselves by offering excellent customer experiences in a cost-efficient manner. Emirates Airlines is at the forefront of customer service and cost efficiency among its competitors\u00a0(1)\u00a0(2). Innovation and a forward-looking attitude have been fundamental to achieving this, and machine learning can facilitate its next steps towards enhancing customer experience, all the while reducing the costs of doing so.<\/p>\n

Machine learning is suited for predictive tasks such as detecting trends in massive data sets that are correlated to specific effects or events \u2013 something that humans would find almost impossible to do otherwise. A problem all airlines face is that of predicting unconstrained demand\u00a0(3) \u2013 this is because as seats fill up, airlines increase the fare and hence constrain demand. Predicting unconstrained demand is important for planning and optimizing fleet allocation to different routes to maximize revenue per unit of available capacity. Similarly, price optimization can be improved through predicting customers\u2019 willingness to pay and then implementing efficient dynamic pricing models\u00a0(4). Machine learning can also be used predict the effects of weather events\u00a0(5) or perhaps even airport congestion on flight delays. Maintenance records could be analyzed to detect early warning signs that eventually result in unexpected maintenance delays i.e.<\/em> condition-based vs time-based maintenance (6). Customer preferences for on-board entertainment could be predicted and viewing suggestions be made to improve the customer experience (7). Other applications include improving customer service response time through automation and predicting passenger baggage size and weight based on each passenger\u2019s history.<\/p>\n

All the above applications will result in a differentiating competitive advantage, through enhanced customer satisfaction, lower operating costs, improved revenue management and higher capacity utilization.<\/p>\n

What is the organization\u2019s management doing to address this issue in the short term (the<\/strong><\/p>\n

next two years) and the medium term (two to ten years out)?<\/strong><\/p>\n

The management at Emirates have realized that machine learning will be key to unlocking future efficiencies and improving its customers experience. Accordingly, the Oxford-Emirates Data Science Lab was launched, in partnership with Oxford University, to ensure that Emirates remains at the forefront of innovation in this field (8).<\/p>\n

It appears that Emirates noticed that the field of data science had not solved the issues it faces to a satisfactory level and so engaged with world experts to solve these issues directly. This demonstrates Emirates proactive approach to remaining at the top of its industry through sustained innovation. This partnership will yield results in the medium term as academic research takes time; however, initial results have already begun to come through in the areas of demand prediction (9) and network modelling to cope with unexpected events and ensure disruptions do not propagate (10). I expect that significant advancements will result from this partnership in the years to come primarily because the reputation of Oxford University is that of academic excellence.<\/p>\n

In the short term, Emirates announced plans to implement artificial intelligence initiatives such as: autonomous vehicles to make airport operations more efficient (11), airport baggage handling without the need for human intervention (12) and chatbots for increasing customer engagement in advertisements (13).<\/p>\n

What other steps do you recommend the organization\u2019s management take to address this<\/strong><\/p>\n

issue in the short and medium terms?<\/strong><\/p>\n

In the short term, I would recommend that the management increase data collection on all fronts of its business operations. With the pace of advancement in field of machine learning, it\u2019s hard to predict what data will be useful in the future so Emirates should consider collecting new data points across its operations: from maintenance to logistics to customer experience. A larger dataset, across a longer period, will help in the development and training of machine learning algorithms. A larger dataset facilitates the differentiation between signals (real correlations in data) and noise (incidental correlations) through the cross-validation of algorithms against a \u201ctest dataset\u201d i.e. <\/em>data unseen by the algorithm (7). I would also suggest to start testing algorithms early \u2013 this would allow fast feedback loops that make the development cycle quicker.<\/p>\n

In the medium term, as algorithms are applied to its business operations, I would recommend that Emirates puts suitable checks and balances in place to ensure safety of passengers and employees. For example, from a safety point of view, I would not suggest that condition-based maintenance replace time-based maintenance, but rather supplement it to improve uptime. Continuous efforts should also be implemented to detect and eliminate false correlations that are picked up by algorithms. This can be done by ensuring a team of data scientists maintain an intuitive understanding of the statistical patterns detected by the algorithms are indeed applicable to the context at hand (14) and through the practice of regularization, which reduces the complexity of algorithms thereby making them less likely to follow noise in datasets (7).<\/p>\n

In the context of this organization, what are one or two important open questions related\u00a0<\/strong>to this issue that you are unsure about that merit comments from your classmates?<\/strong><\/p>\n

The research by Oxford University will be published \u2013 how can Emirates maintain a competitive advantage if its competitors have open access to this research? Does this mean Emirates considers its real advantage to be executing innovative techniques as opposed to developing them?<\/p>\n

(795 words, excluding title, question text & references)<\/p>\n

 <\/p>\n

References<\/strong><\/span><\/p>\n

    \n
  1. Evaluating Airline efficiency: An application of virtual Frontier Network SBM. <\/em>Li, Ye, Wang, Yan-zhang and Cui, Qiang.<\/strong> 2015, Transportation Research Part E, pp. Volume 81, pp 1-17.<\/li>\n
  2. Trip Advisor.<\/strong> Emirates Reviews. Trip Advisor. <\/em>[Online] [Cited: 11 13, 2018.] https:\/\/www.tripadvisor.com\/Airline_Review-d8729069-Reviews-Emirates.<\/li>\n
  3. Unconstraining Methods in Revenue Management Systems: Research Overview and Prospects. <\/em>Guo, Peng, Xiao, Baichun and Li, Jun.<\/strong> 2012, Advances in Operations Research, pp. Vol. 2012, Article ID 270910, 23 pages.<\/li>\n
  4. A Machine Leaning Framework for Predicting Purchase by online customers based on Dynamic Pricing. <\/em>Gupta, Rajan and Pathak, Chaitanya.<\/strong> 2014, Procedia Computer Science, pp. Volume 36, pp 599-605.<\/li>\n
  5. Prediction of weather-induced airline delays based on machine learning. <\/em>Choi, Sub, et al.<\/strong> Sacramento, CA\u00a0: IEEE, 2015. 2016 IEEE\/AIAA 35th Digital Avionics Systems Conference (DASC). pp. pp 1-6.<\/li>\n
  6. Current status of machine prognostics in condition-based maintenance: a review. <\/em>Peng, Ying, Dong, Ming and Jian Zuo, Ming.<\/strong> 2010, The International Journal of Advanced Manufacturing Technology, pp. Volume 50, Issue 1-4, pp 297-313.<\/li>\n
  7. Yeomans, Mike.<\/strong> What every manager should know about machine learning. 性视界 Business Review. <\/em>2015, July.<\/li>\n
  8. Oxford University.<\/strong> Oxford-Emirates Data Science Lab will streamline air travel. [Online] 2015. [Cited: 11 13, 2018.] http:\/\/www.ox.ac.uk\/news\/2015-10-29-oxford-emirates-data-science-lab-will-streamline-air-travel.<\/li>\n
  9. Price, Ilan, Fowkes, Jaroslav and Hopman, Daniel.<\/strong> Cornell University Library. [Online] 11 29, 2017. [Cited: 11 13, 2018.] https:\/\/arxiv.org\/pdf\/1711.10910.pdf.<\/li>\n
  10. Beguerisse, Mariano and Tolvanen, Antti.<\/strong> Dissertation: Spatiotemporal Analysis of Air-Travel Networks. [Online] 12 2016. [Cited: 11 13, 2018.] https:\/\/www.maths.ox.ac.uk\/system\/files\/attachments\/projects_2016-17.pdf.<\/li>\n
  11. Khaleej Times.<\/strong> Emirates takes off to new world of artificial intelligence. [Online] 2017. [Cited: 11 13, 2018.] https:\/\/www.khaleejtimes.com\/business\/aviation\/emirates-takes-off-to-new-world-of-artificial-intelligence-.<\/li>\n
  12. Logistics Middle East.<\/strong> Emirates wants artificial intelligence to handle airport baggage. [Online] 2018. [Cited: 11 13, 2018.] https:\/\/www.logisticsmiddleeast.com\/business\/30871-emirates-wants-artificial-intelligence-to-handle-airport-baggage.<\/li>\n
  13. Liffeing, Ilyse.<\/strong> Emirates Vacations brings a chatbot to banner ads. [Online] 2018. [Cited: 11 13, 2018.] https:\/\/digiday.com\/marketing\/emirates-brings-chatbot-banner-ads\/.<\/li>\n
  14. Fedyk, Anastassia.<\/strong> How to tell if machine learning can solve your business problem. 性视界 Business Review. <\/em>11 25, 2016.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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