it integration in airline business process itab 2007 grach muradyan

IT integrationin airline business process

ITAB 2007Grach Muradyan

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General principle

Information Technologies allow airline

To be aware of… To understand… To describe… In order to control !!!

…but only in case IT is properly integrated in airline business process…

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General principle

Airline industry operates in a dynamic environmentVariety of external and internal factors:

Changes in demandCompetitors actionsFuel cost, etc…

Impossible to predict all the variations IT complex should provide airline

Maximum info for planningMaximum info for ongoing situation / statusMaximum flexibility and best tools to react on the ongoing situationMinimum time for decision delivery to the market

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General principle

IT complex should be organized as a system with feedback (“autopilot scheme”)

Airline commercial department

MARKET

Other factors

Competitors

Regulation

Data collectionData analysis

Objectives & planning

Optimization Implementation Control

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Airline IT modules

Objectives and PlanningRevenues and costs planning Network planningSchedule generation & optimization (max fleet & crew utilization with given restrictions)

Optimization: Revenue ManagementPrognosis, based on the historical dataAvailability and overbooking level recommendationsReporting

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Airline IT modules

ImplementationSchedule publication

Connections (ongoing) optimizationOperation and disruptions management

Inventory controlNesting

Availability Fares

Levels per classRules / limitationsTaxes

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Airline IT modules

ControlRevenue Integrity: increasing load factor and

efficiency of RM utilizationTime limits Fake names (“test”, etc..)Unreal bookings Unproductive bookings

Distribution (in GDSs)Married Segment ControlLong Haul availabilityDynamic Availability Journey Data

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Airline IT modules

Data collection (major airline data streams)

Billing Information (BIDT / ABI): bookings on own airline

Marketing information (MIDT): bookings on own + other airlines

Global Regional Express Custom, etc…

Ticketing Data (ATD)

IATA Reporting / Hand Off Tape format files

ET Flight Coupons (LIFT files)

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Airline IT modules

Major Data analysis tasks Commercial analysis

Demand for the airline services Bookings / cancellations distribution (per markets, flights,

cabin / classes, departure dates, etc…) TAs making passive, test, fake and other unproductive

bookings Revenue / ticketing info

Cashflow / stock control Interlining Source for Revenue Accounting systems

Technological analysis Sales effectiveness (e.g. TK, UN,… levels)

Billing / accounting purposes Understanding billing of IT service providers Supporting airline claims

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Data analysis examples distribution per month of departure

Market and seasonality dependence (RU / Jan)

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Data analysis examplesdistribution per month of departure

Market and seasonality dependence (DE / Jan)

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Data analysis examples distribution per month of departure

Market and seasonality dependence (DE / May)

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Data analysis approach From global to local

Being aware something is going not the way it was expected (e.g. irregularity)…

Understanding what exactly is happening ?(e.g. bookings jumping in low season)

Finding who exactly and why is doing this ?(POS: country->TA office ID)

Deciding how to resolve the issue ?(technological and commercial actions)

Analyzing how to prevent this happening again ?(e.g. changing airline policy)

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Data analysis exampleRegularity in long-term observation

( peaks in Jan and Sep net bookings )

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Data analysis exampleTA activity split by country & office

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Data analysis exampleTA activity split by office

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Distribution per month of departure:Jan bookings made by one TA office (“seats freezing”)

Departure month -> Jan Feb Mar Apr May Jun Jul Aug Sep Oct TotalAdded 60 99 10 846 728 779 824 740 915 921 5922Canceled 14 20 2 10 8 25 10 0 31 43 163Net 46 79 8 836 720 754 814 740 884 878 5759% of Total 0,8% 1,4% 0,1% 14,5% 12,5% 13,1% 14,1% 12,8% 15,3% 15,2% 100,0%Canceled % 30,4% 25,3% 25,0% 1,2% 1,1% 3,3% 1,2% 0,0% 3,5% 4,9% 2,8%Booking period (days) 14 9 3 5 4 (13-16) 4(13-16) 5(13-17) 4(14-17) 4 (14-17) 3(15-17)

0,0%

2,0%

4,0%

6,0%

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10,0%

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18,0%

Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Data analysis example

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Passive bookings made by an office within just one day…

Data analysis example

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Last but not least…

Need for IT policy coordinationon top management level

Internal competition (trend) between departments within airline: IT being a TOOL for commerce Commerce dictates what IT decides how (the best) IT proposes new tools / approaches

Permanent control on IT activity: info processing Regularity Frequency Completeness

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Thank you !