Xebia Middle East

1. SOFTWARE DEVELOPMENT DONE RIGHT LEVERAGING DATA TO IMPROVE FLYING EXPERIENCE WHO WHAT Machine Learning & Optimization Mechanism International Airline

2. BACKGROUND Our client is one of the largest international airline operating over 2,000 flights per week to nearly 120 cities in 75 countries across six continents. Our client’s host country airport is one of world's busiest airport in terms of international passenger trafc. Daily hundreds of flights take-of and land at this airport. The main challenge faced by the client was in managing flight delays ranging from few minutes to several hours during take-of and landing at the time of disruptions. The main cause of disruption could be weather, crew logistics, operational issues, technical problems etc. The client was looking for an automated solution which could help them in prioritizing their flights for take-of and landing in case of such disruptions. In order to overcome this challenge, the client approached Xebia to implement an optimization technique that can help the client in making decisions during flight landing and take-ofs. Xebia worked in collaboration with the client and implemented machine learning and optimization mechanism to compute a score for each flight based on certain parameters. This helped the business in taking decisions and prioritizing inbound and outbound flights based on the assigned score instead of relying on personal experience or historical data. BUSINESS CHALLENGE The main challenge faced by the client was in prioritising flights in diferent scenarios enumerated below: • If a flight A is scheduled to depart on time, but flights B and C of connecting passengers are delayed, should the flight A wait? If yes, how long should it wait; should it wait only for A or only for B or for both? • In case of weather disruption, the capacity of the runway reduces to as low as 20% of its normal capacity. Hence departing flights have to be put on hold on the ground and arriving flights in the air. In such situation, which flights should be put on hold and by how much time? • In case of non-weather disruption, due to reason such as runway closure, mechanical defects, logistics, security etc. multiple flights have to be delayed. The challenge was to decide which flights should be delayed and by how much time? In order to overcome these challenges, the client was looking to: -- Replace manual flight prioritisation with automated data driven decision system. -- Automate the solution which could continuously monitor the flight network and give a real time priority order of flights. SOLUTION Xebia’s team of Machine Learning experts were deployed at the client site to gain insights from passenger data sources. The team devised a mechanism to compute the value score of each flight based on various passenger parameters such as age, gender, loyalty tier (if any), number of infants, number of medical case/wheelchairs, number of first/business/economy passengers, connection flight timing details of connecting passengers etc. Clustering algorithm were used to segment flights into five profiles and each profile was validated with business users to arrive at the priority order. This was followed up with scoring of flights within each cluster/profile by computing the optimal weight for each parameter. This ensured that all individual flights in a high priority cluster would have a higher score and hence higher priority than those in a lower priority cluster.

3. The following processes were followed to derive flight scores: • Feature Engineering – The original raw data was feature engineered to create several new derived variables such as passengers travelling to or away from their home country, number of passengers with tight connections (60 mins, 90 mins etc), and percentage of loyalty members in the load. Also, each of above features was further drilled down to sub categories such as age was sub categorised into young age, middle age, old age etc. • Computing the optimal number of clusters – Optimal number of clusters were identified for inbound and outbound flights using the Ward/elbow method. • Creating Clusters - Using K - Means algorithms, clusters were created, and their stability was tested. • Business validation and prioritization – The cluster averages were presented to the client and the priority order was discussed and finalized. • Feature importance – Ranking was based on dominant features that were used for prioritizing flight profiles. Ranks of inbound and outbound flights were combined to come up with a consolidated ranking of features. • Flight scoring - Weights were assigned to each of the ranked features so that higher ranked features were given more weights than the lower ranked ones. The final score of the flight was computed separately for all passengers joining from connecting flights and new passengers joining the existing flight. Key Benefits • Improved and accurate decision making • Better customer experience • Improved trafc handling capability • Reduced airline operating cost due to real-time decision making

4. SOFTWARE DEVELOPMENT DONE RIGHT Get in touch with Xebia TEC, Ofces 3, One Central, DWTC, Dubai, UAE +97145264752 infome@xebia.com www.xebiamiddleeast.com Netherlands | USA | India | UAE I UK

5. Disruption Disruption could be - weather, crew logistics, operational issues, technical problems etc Passenger Data Passenger profile details such as age, gender, loyalty tier, number of infants, number of medical case / wheelchairs, number of first / business / economy passengers etc. Clustering algorithm - Create profiles of flight so that priority can be assigned to the clusters/profiles Feature Medical Loyalty Infants Elderly connectors Rank 1 2 3 4 Based on the ranking, highest ranked flight is given the preference for landing /take-off