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Harnessing business intelligence and big data. Is collaboration the key to success?

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John-Paul Clarke, Associate Professor, Georgia Institute of Technology …

John-Paul Clarke, Associate Professor, Georgia Institute of Technology
Air Transport IT Summit 2013 - www.sitasummit.aero - #ATIS2013

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  • 1. Enabling Pro-Active Decisions viaPredictive AnalyticsJohn-Paul ClarkeAssociate Professor, Aerospace (and Industrial and Systems) EngineeringDirector, Air Transportation LaboratoryGeorgia Institute of Technology
  • 2. Agenda• Background and Motivation• Potential for Predictive Analytics in Airport Operations• Using Predictive Analytics to Mitigate Congestion and Delay• SummaryJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 2
  • 3. Enabling Pro-Active Decisions viaPredictive AnalyticsBackground and Motivation
  • 4. What is predictive analytics?• Area of statistical analysis that deals with extractinginformation from data and using it to predict trendsand behavior patterns.– Utilizes a variety of techniques from statistics, modeling,machine learning, and data mining.– Applied to any type of unknown whether it be in the past,present or future.– Accuracy and usability of results depend greatly on thelevel of data analysis and the quality of assumptions.John-Paul Clarke – Georgia Tech 4SITA IT Summit 2013
  • 5. Does predictive analytics really work?“Companies that systematically apply predictiveanalytics to operational decisions, especially thosepertaining to customers, outperform theircompetitors.”- James TaylorCEO, Decision Management SolutionsJohn-Paul Clarke – Georgia Tech 5SITA IT Summit 2013
  • 6. Notable Non-Aviation Successes• How did the grocery cashier know to hand you acoupon for the whole-grain cereal you will need tobuy next week?– Computers crunched terabytes and terabytes of historicalpurchasing data to determine that your favorite whole-grain cereal was missing from your shopping basket.– Further, the computer matches this finding to the ongoingpromotions in the store, and determines that you shouldreceive a coupon.John-Paul Clarke – Georgia Tech SITA IT Summit 2013 6
  • 7. Notable Non-Aviation Successes (cont’d)• Why were the Oakland As so successful in the early2000s, despite a low payroll?– In addition to looking at basic productivity values such asRBIs, home runs, and earned-run averages. Instead, theyanalyzed hundreds of detailed statistics from every playerand every game, attempting to predict future performanceand production.– Team able to sign great players who may have been lesserknown but were equally productive on the field.John-Paul Clarke – Georgia Tech 7SITA IT Summit 2013
  • 8. Notable Aviation Successes• Predicting success of pilot trainees… identifying bestcandidates and reducing training costs.– David Hunter and Eugene Burke, “Predicting Aircraft Pilot-TrainingSuccess: A Meta-Analysis of Published Research,” InternationalJournal of Aviation Psychology 4(4)297-313• Predicting parts failure… anticipating maintenancerequirements.– Clockwork Solutions LLC, “Predictive Analytics in Aerospace andDefense: It’s time Launching Modern Day Modeling and Simulation inthe Defense Industry,” 13 January 2013John-Paul Clarke – Georgia Tech SITA IT Summit 2013 8
  • 9. Notable Aviation Successes (cont’d)• Predicting the number of passengers who won’tshow up for a flight… reducing the number ofoverbooked flights and empty seats… increasingrevenue and customer satisfaction.– Wayne Eckerson, “Predictive Analytics: Extending the Value of YourData Warehousing Investment,” TDWI Best Practices Report, 2007 Q1John-Paul Clarke – Georgia Tech SITA IT Summit 2013 9
  • 10. Notable Aviation Successes (cont’d)• Identifying and catering to high-value customers...increasing customer satisfaction… boosting annualrevenues (on average) by $200 for each “valuable”customer and $800 for each “most profitable”customer– IBM, “Five predictive imperatives for maximizing customer value:Applying predictive analytics to enhance customer relationshipmanagement,” IBM Software Business Analytics Report 2010John-Paul Clarke – Georgia Tech SITA IT Summit 2013 10
  • 11. Enabling Pro-Active Decisions viaPredictive AnalyticsPotential for Predictive Analyticsin Airport Operations
  • 12. Overview of Airport Operations• Airports are the nodes of the air transportationsystem.– Capacity constraints and inefficiencies at airport entrances,parking, curbsides, security, immigration, customs, gates,ramps, runways are the primary drivers of congestion anddelays.• Passengers satisfaction is in large part governed bytheir experience at the airport.John-Paul Clarke – Georgia Tech SITA IT Summit 2013 12
  • 13. Overview of Airport Operations (cont’d)• Control of airport processes and procedures aredistributed or shared between airlines, airportauthorities, government, and third party suppliers.– No single entity able to predict consequences of decisionsand initiate remedies.• Critical need for a mechanism to predict the likelysituation as well as the benefits / consequences ofpotential changes / decisions.John-Paul Clarke – Georgia Tech SITA IT Summit 2013 13
  • 14. How should we approach this problem?“The full value of analytics is only found after askingthe right question.”- Janet WejmanFormer CIO of Continental AirlinesJohn-Paul Clarke – Georgia Tech 14SITA IT Summit 2013
  • 15. What are the right questions?• From the passenger’s viewpoint…– Why are there never enough ground / immigration /customs / check-in / security / gate personnel when I getthere?• From the provider’s viewpoint…– Can we really improve airline and airport operations andenhance the passenger experience by modeling andpredicting the movement of aircraft, baggage, andpassengers and use our predictions to allocating resources(e.g. ground support equipment, staffing levels)?John-Paul Clarke – Georgia Tech 15SITA IT Summit 2013
  • 16. What is the opportunity?• Reality:– Airlines and airports collect a lot of data but mostlyconduct retrospective analyses as opposed to using thedata to predict what could happen and being proactive.• Hypothesis:– Significant operational benefits could be achieved by usingdata to predict where and when congestion and delay willoccur and then using this information to re-allocateresources and mitigate potential congestion and delay.John-Paul Clarke – Georgia Tech 16SITA IT Summit 2013
  • 17. Enabling Pro-Active Decisions viaPredictive AnalyticsUsing Predictive Analytics to Mitigate Congestionand Delay – A Case Study of Sydney Airport
  • 18. Case Study at Sydney Airport• Scenario:– Aircraft en route to Sydney but their estimated touchdown times are off-schedule due to upstream factors.• Questions:– Can we predict the queue sizes and delay in immigrationfor planned staffing levels?– How would these queue sizes and delay change if wechanged the staffing levels?John-Paul Clarke – Georgia Tech SITA IT Summit 2013 18
  • 19. Case Study at Sydney AirportJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 19
  • 20. How should we model this scenario?Flight1 TouchdownFlight1 Gate ArrivalFlight 2 TouchdownFlight2 Gate ArrivalFlight N TouchdownFlightN Gate ArrivalFlight 1 En Route Flight 2 En Route Flight N En RouteFlight 1 Taxiing-In Flight 2 Taxiing-In Flight N Taxiing-InJohn-Paul Clarke – Georgia Tech 20SITA IT Summit 2013Passengers Waiting in Immigration Queue / Being ProcessedFlight 1 PassengersWalking to ImmigrationFlight1 PassengersEnter ImmigrationFlight1 PassengersLeave ImmigrationFlight 2 PassengersWalking to ImmigrationFlight2 PassengersEnter ImmigrationFlight2 PassengersLeave ImmigrationFlight N PassengersWalking to ImmigrationFlightN PassengersEnter ImmigrationFlightN PassengersLeave Immigration
  • 21. Features of the Model• Probability Distributions used to characterize…– Individual movement (including uncertainty) of aircraftand passengers.• Monte Carlo simulation used to capture effects of…– Shared resources;– Flights arriving at different times;– Passengers entering immigration at different times.John-Paul Clarke – Georgia Tech 21SITA IT Summit 2013
  • 22. Calibrating the Model• Out-Off-On-In (OOOI) data used to derivedistributions for…– Taxi-in time for each runway-gate combinationand time period.• Smart-phone data (from SITA Airport iFlow)used to derive distributions for…– Time to walk to immigration for each gate-flightcombination;– Immigration processing rate as a function of time of day(and staffing level).John-Paul Clarke – Georgia Tech 22SITA IT Summit 2013
  • 23. Modeling Taxi-In• Gate 27• 02:00-03:00John-Paul Clarke – Georgia Tech SITA IT Summit 2013 23
  • 24. Modeling Taxi-In Time• Gate 27• 06:00-07:00John-Paul Clarke – Georgia Tech SITA IT Summit 2013 24
  • 25. Modeling Time to Walk to ImmigrationJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 25Walk-Time (min)Frequency
  • 26. Modeling Immigration Service Rate• Processing rate is a function of staffing levelJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 26
  • 27. Simulation ModelFlight1 TouchdownFlight1 Gate ArrivalFlight 2 TouchdownFlight2 Gate ArrivalFlight N TouchdownFlightN Gate ArrivalFlight 1 En Route Flight 2 En Route Flight N En RouteFlight 1 Taxiing-In Flight 2 Taxiing-In Flight N Taxiing-InJohn-Paul Clarke – Georgia Tech 27SITA IT Summit 2013Passengers Waiting in Immigration Queue / Being ProcessedFlight 1 PassengersWalking to ImmigrationFlight1 PassengersEnter ImmigrationFlight1 PassengersLeave ImmigrationFlight 2 PassengersWalking to ImmigrationFlight2 PassengersEnter ImmigrationFlight2 PassengersLeave ImmigrationFlight N PassengersWalking to ImmigrationFlightN PassengersEnter ImmigrationFlightN PassengersLeave Immigration
  • 28. Rate Passengers Leave ImmigrationJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 28
  • 29. No. of Passengers in Immigration QueueJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 29
  • 30. Delay in Immigration QueueJohn-Paul Clarke – Georgia Tech SITA IT Summit 2013 30
  • 31. How do we mitigate delays?John-Paul Clarke – Georgia Tech SITA IT Summit 2013 31Estimate Touch-Down TimesPassenger LoadsFlight NumbersDelaysAcceptable?ResourcesSimulation
  • 32. Enabling Pro-Active Decisions viaPredictive AnalyticsSummary
  • 33. Conclusions• Predictive Analytics has been shown to be animportant tool in pro-active decision-making.• Our studies indicate that predictive analytics canimprove airport operations.– Common prediction of bottlenecks and capability toexplore effectiveness of possible remedies.• Simulation framework can be easily extended.– e.g. Used to predict delay 5 weeks, 1 week, 1 day prior.John-Paul Clarke – Georgia Tech 33SITA IT Summit 2013
  • 34. Next Steps• Develop “industrial strength” IT infrastructure andtools to enable use of predictive analytics in airportoperations.• Investigate use of predictive analytics in variousother aspects of airport operations:– Ground support (under-wing) services;– Curbside, parking, check-in, security, outboundimmigration staffing.John-Paul Clarke – Georgia Tech 34SITA IT Summit 2013
  • 35. DISCLAIMERAny use, republication or redistribution of this content isexpressly prohibited without the prior written consent ofthe Author. Permission to copy and reproduce contentmay be granted by the author, at their discretion, andby request only.Source: presentation of John-Paul Clarke, GeorgiaInstitute of Technology at the 2013 SITA Air TransportIT Summit, Brussels.2013 Air Transport IT Summit