Airline planing and marketing
Transcript of Airline planing and marketing
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Optimization inAirline Planning and
Marketing
Institute for Mathematics and ItsApplications
November 2002
Barry C. Smith
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Overview
Airline Planning and Marketing
Landscape
Applications of Optimization Modeling
Planning and Marketing Integration
Unsolved and Under-solved Problems
Future Outlook
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Airlines Make Money Only When TheyMatch Supply and Demand
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Major US domestic carriers: Operate 5000 flights per day Serve over 10,000 markets Offer over 4,000,000 fares
Schedules change twice each week
On a typical day, a major carrier will change100,000 fares
Airlines offer their products for sale more than one year inadvance
The total number of products requiring definition and control isapproximately 500,000,000
This number is increasing due to the proliferation of distributionchannels and customer-specific controls
The Problem is Large and Dynamic
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Enterprise Planning Product Planning
Tactics and
Operations
Effective Planning and Marketing is aContinuous Process
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DecisionsDecisions
There Should be Continuity
Route StructureRoute Structure FleetFleet MaintenanceMaintenance
BasesBases
Crew BasesCrew Bases FacilitiesFacilities
ScheduleSchedule Fleet AssignmentFleet Assignment Pricing PoliciesPricing Policies
PricePrice RestrictionsRestrictions AvailabilityAvailability
TimeTimeHorizonHorizon
18 Months +18 Months + 18 Months 18 Months 1 Months1 Months
3 months 3 months DepartureDeparture
ObjectiveObjective Maximize NPVMaximize NPVof Future Profitsof Future Profits
Maximize NPVMaximize NPVof Future Profitsof Future Profits
Maximize NPVMaximize NPVof Future Profitsof Future Profits
ConstraintsConstraints FinancialFinancialResourcesResources
RegulationRegulation
Route StructureRoute Structure FleetFleet MaintenanceMaintenance Crew BasesCrew Bases FacilitiesFacilities
ScheduleSchedule Pricing PoliciesPricing Policies
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Significant Optimization Applications
Tactics and OperationsYield Management
Product Planning
Fleet Assignment
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Yield Management Objectives
Sell the right seat
To the right passenger
At the right price
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YM is Essential to Airline Profitability
Annual benefit of Yield Management to amajor airlines is 3% 6% of total revenue
A major airlines revenue benefits from yieldmanagement exceed $500,000,000 per year
Applying this rate to the industry ($300billion/year) yields potential benefits of $15billion per year
The possibilities for even the most
sophisticated carriers go well beyond what isachieved today
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YM Controls
Overbooking
Revenue Mix
Discount allocation
Traffic flow
Groups
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Yield Management Evolution
High Value Low Value
1970s:ClassCode
Rev +4%3 MM
1960s:
Overbooking
Revenue+2%,300k
Full Fare
DeepDiscount
Value of Last Seat
Class
Code
Origin-Destination Market
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Revenue Mix Problem Flight Leg
Stop selling Current (low-value) products when:
Profit (Current) < Profit (high-value) * P (Sell out)
Sell to Current Customer
Hold for
Higher-ValueCustomer
Sell outHigh-Value Profit
Unsold Product
Current Profit
$0
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Yield Management Evolution
High Value Low Value
1970s:ClassCode
Rev +4%3 MM
1980s:ODRev +5%
30 MM
1960s:
Overbooking
Revenue+2%,300k
Full Fare
DeepDiscount
Value of Last Seat
Class
Code
Origin-Destination Market
1990s: BidPrice
Rev +6%
1 MM
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Revenue Mix Problem Network
0
),(
:Subject to
Re*Max
=
sAllocation
CapacityPax
DemandAllocationfPax
vPax
ODF
FlightODF
ODFODFODF
ODF
ODFODF
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Passengers = f (Allocation, Demand)
= 0
> 0
Mean Demand
Pass
enger
sCarried
Allocation
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Revenue Mix Approaches
Deterministic Leg Allocations (wrong)
Stochastic LegAllocations (BA, MIT)
Deterministic Network Allocations (wrong)
Stochastic Network Bid Price (AA)
Deterministic Network
EMSR
VN Allocations (MIT)
Stochastic NetworkADP on Leg Bid price(Columbia)
ADP on Network Real-time evaluation (GIT)
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2000s:Mult.
Channels
CRM
Yield Management Evolution
High Value Low Value
1970s:ClassCode
Rev +4%3 MM
1980s:ODRev +5%
30 MM
1960s:
Overbooking
Revenue+2%,300k
Full Fare
DeepDiscount
Value of Last Seat
Class
Code
Origin-Destination Market
1990s: BidPrice
Rev +6%
1 MM
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Fleet Assignment FAM
Fleet Assignment Models (FAM) assign aircraft types to anairline timetable in order to maximize profit
FAM is widely used in the airline industry AA and DL have reported 1% profit margin improvements
from FAM
Given a flight schedule and available fleet of aircraft,FAM maximizes operating profit subject to the followingphysical and operational constraints: Cover: Each flight in the schedule must be assigned
exactly one aircraft type
Plane Count: The total number of aircraft assignedcannot exceed the number available in the fleet Balance: Aircraft cannot appear or disappear from
the network
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Basic FAM Formulation
0}1,0{
)(,,
0
1
)(
,,,
, ,
,,,,,1,
,0,
,
,,,
=+
=
staaf
s
tsArrivalf
tsDeparturef
staafafsta
Stationssasa
Aircrafta
af
afAircrafta Flightsf
afaf
yx
circularTimestStationssAircrafta
yxxy
AircraftaPCy
Flightsfx
toSubject
CRxMax
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FAM Extensions
Time windows (US, MIT) Integration
Routing (UPF, MIT, GIT)
Crew (Gerad) Yield Management (MIT, LIS, Sabre, GIT)
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Leg Revenue Modeling Approaches
Average passenger fare: Inconsistent with yieldmanagement practices. As capacity is added,incremental passengers have lower average revenue.
Leg revenue: Modeling passenger revenue on a flightas a function only of capacity on this flight assumes
that there is no upline or downline spill These assumptions create inconsistencies with
subsequent airline marketing processes, in particularO&D yield management, and tend to bias FAMsolutions to over-use of large aircraft
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Improving Revenue Modeling in FAM
Allocations For each flight leg allocate space to each passenger path
Piecewise linear approximation for traffic/revenue oneach path
Solve the OD YM model inside of FAM
Model size explodes -- There are 150,000-500,000passenger paths in a typical problem for a major carrier
Decomposition
Solve yield management model outside of FAM
Incorporate model results into FAM
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Integration of FAM and YM
FAM
Capacity
YM
CapacityBid Price
Revenue Function Approximation:
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Revenue Function Approximation:One Leg, One Cut
Revenue
($US)
Leg Capacity (No. of Seats)
Bidprice, ($/seat)
CAPj
R0
R0 f fC A Pf R C A Pf
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OD FAM Master
CutsODi
:andBalance,Count,PlaneCover,
,0
,,
+
Flights f Aircraft aTotalafa
if
i
afAircrafta Flightsf
afTotal
RxCapR
toSubject
CxRMax
Revenue Function Approximation:
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Revenue Function Approximation:One Leg, Multiple Cuts
Revenue
($US)
Leg Capacity (No. of Seats)CAPj
R0
)(*0 fffi
fi
CAPRCAPR +
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Planning and Marketing Integration
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Enterprise PlanningEnterprise Planning Product PlanningProduct Planning
Tactics andTactics and
OperationsOperations
Ideal Planning
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Planning & Scheduling
Pricing
Yield Management
Distribution
Customers
Planning Reality
Sales
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Airline Pricing
Simple Concepts Relatively fixed seat
capacity
High fixed costs
Combination of elastic andinelastic market segments
Complex Reality Oligopoly market behavior
Multi-period repeated trial
Strategy is generallydominated by mechanics(tactics)
The pricing process is oftenunclear to airline executives
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On Tariff -- GDS
AL.com
Distressed Inventory
TA.com
Res Office
FFP Burn/Earn
CorporateTour/Cruise/Cons
Partners
Sales and Distribution:Multi-channel
Airline
Capacity
Forecast of
Demand and
Free Market
Value
Customers
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Planning & Scheduling
Pricing
Yield Management
Distribution
Customers
Bid Prices Support Integration
Sales
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Unsolved and Under-solved Problems
Opportunities Enterprise Planning
Facilities Manpower Fleet
Longitudinal Planning Alliance Optimization
Customer RelationshipManagement
Robust Planning Demand Operations Competition
Support for Labor Negotiations
Supporting Models Customer Behavior
Modeling
Simulation
Airline
Alliance
Industry
Scenario Analysis
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The Evolving Environment
Distant Past: Airlines initiated development of optimization-based systems
Recent Past: Following deregulation of the US domestic industry, airlinessupported technology development
Technical leadership shifted from airlines to academics, consultants andsoftware providers
Current: The current market conditions have reduced the ability of major UScarriers to support significant new development
Future: The marketplace for new optimization applications will bedominated by the requirements of the emerging carriers low-cost,alternative business models
Simple
Flexible
Developed outside of the carrier
Operated outside of the carrier
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Optimization inAirline Planning and
Marketing
Institute for Mathematics and ItsApplications
November 2002Barry C. Smith