business intelligence and airline operational improvement
DESCRIPTION
From SITA IT Conference in Brussels, 19 June 2013. I review how big data analytics can fundamentally improve visibility into operational challenges and change cross-departmental goals. I give specific examples of how business intelligence can change both operational performance and efficiency.TRANSCRIPT
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SITA IT Summit 2013
Operational visibility through deep analytics How big data methods improve aviation profitability
Joshua Marks, CEO +1 703 994 0000 Mobile [email protected]
W W W . M A S F L I G H T . C O M
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SITA 2013 IT Summit
Big data methods unlock new profitability gains
$13.5
$22.6
$32.5 $36.1
$40.1
2009 2010 2011 2012 2013e
Unbundling Revenue (USD Billions) Global aviation profitability has
depended on ancillary revenue. But those gains are slowing. Aviation must use productivity to sustain growth – and invest in IT platforms that merge and link data
Source: Amadeus/IdeaWorks
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SITA 2013 IT Summit
Today: Critical data trapped in IT silos, crippling big data
Flight Schedule and Fleet Data
Revenue and Passengers
Airport and Operations
Finance & Accounting
Different Vendors & Silos Different Users Manual Integration
Revenue
Flt Ops
IT/Web
Finance
FEED
Collect Data, Merge Tables Build Databases
Obtain data from the web or internal PCs,
integrate by hand
FEED
FEED
FEED
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SITA 2013 IT Summit
Operational visibility through deep analytics
Validated information and task-specific applications are critical for aviation planning and management.
Forecasting Partner analysis Post-ops review Benchmarking
Schedule design Hub connectivity Maintenance planning Airport operations
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SITA 2013 IT Summit
Foundation of Big Data: Integrated, Managed Information
Schedule Sources
FLIFO Sources
Weather Sources
Radar & Flt Plan
Airport & Gate Info
Fleet & Tail Info
Other Sources
FL
EE
T
AIR
LIN
E
SY
ST
EM
FL
IGH
T
FILED & FINAL SCHEDULES
GATES AND AIRPORT INFO
TAIL NUMBER & FLEET INFO
GATE DEPARTURE & TAKEOFF
LANDING & GATE ARRIVAL
ORIGIN & DEST WEATHER
FLIGHT PLAN FILED & FLOWN
ENROUTE WEATHER
MARKETING CARRIER OPERATING CARRIER
R E A L T I M E D A T A S O U R C E S
C L O U D D A T A W A R E H O U S E
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SITA 2013 IT Summit
Example: Improving Schedule Accuracy
Block planning is an art based on review of: Taxi and flight history One-time factors
Big data enables a more scientific approach with: Departure and arrival gates Intra-seasonal weather Tail number differences
0
50
100
150
200
250
5 15
25
35
45
55
65
75
85
95
105
115
125
135
145
155
165
175
185
195
205
215
225
235
Cou
nt o
f Flig
hts
Minutes After Gate Departure
Gate Out Landing Time Gate In
Modal Taxi Out 23 min
Modal Gate Arrival 2h 28m
Delta: All 2012 New York LGA to Atlanta Distribution of Taxi and Flight Times
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SITA 2013 IT Summit
Example: Identifying Airport Operational Improvements
West International (Odd gates 91-99)
23.5 min taxi-out
East International (Even gates 90-100)
21.3 min taxi-out
East Base Domestic (Gates 68-71)
18.1 min taxi-out
Outer Domestic Pier (Gates 76-77 and 80, 82, 84, 88)
18.6 min taxi-out Inner Domestic Pier
(Gates 81, 83, 85, 87, 89)
20.7 min taxi-out
Data from 2012 All UA SFO Operations
West Base Domestic (Gates 72-75)
21.0 min taxi-out
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SITA 2013 IT Summit
Example: Operational Disruption for High-Yield Passengers
Delta Air Lines 2012 New York to Los Angeles
13% 11%
10% 8% 8%
9%
ATL DTW MSP
Misconnect % Pax > $500
15%
8% 8% 8% 7% 6%
15% 18%
DTW MSP ATL SLC
Misconnect % Pax > $500
14% 12% 12%
9%
14%
7%
11% 11%
ATL MSP SLC DTW
Misconnect % Pax > $500
Blue: Flights A+30
and Cancelled
Red: % of NY-LA
O&D > $500
Compare connect points and O&D
traffic
From JFK via: From LGA via: From EWR via:
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SITA 2013 IT Summit
Cloud + Big Data: Visibility without legacy constraints
Management
Linked data Full archives
Powerful retrieval
Aggregation AUTOMATED DATA
COLLECTION & LINKING
Visibility
Lower IT investment, more flexibility and new insight
SCALABLE STORAGE ARCHITECTURE
FEED ANALYTICS AND DASHBOARD SYSTEMS
Multi-source feeds Auto correction Linked tables
Ops & Revenue Real-time monitor
Predictive Analytics
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• Profitability depends on finding new efficiencies in operations and revenue
• Linked, cloud-hosted data combines low acquisition cost with flexibility and power
• Big data analytics fundamentally changes how planning can reduce variability
• Dashboard and monitoring systems also change day-of and predictive management
Investment Case & ROI
Organizational Insight & Value
SITA 2013 IT Summit
Conclusions for Cloud-Based Big Data
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SITA 2013 IT Summit
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