2003 © UTD
Seminar: Air Cargo Supply Chain Management and Challenges
Raja KasilingamVice President Air Cargo SolutionsSabre Inc.
transporting air cargointo the future
Center for Intelligent Supply Networks (C4ISN)
2003 © UTD Slide 2 Center for Intelligent Supply NetworksC4ISN
Agenda
Sabre, Inc. OverviewAir Cargo IndustryAir Cargo LandscapeCargo Revenue Management ConceptsCargo Revenue Management Solution
2003 © UTD Slide 3 Center for Intelligent Supply NetworksC4ISN
Revenues of about $2 Billion
NYSE symbol: TSG
Approximately 6,000 employees in 45 countries worldwide
Headquarters in Dallas/Fort Worth, Texas
A S&P 500 company
Sabre at a Glance
2003 © UTD Slide 4 Center for Intelligent Supply NetworksC4ISN
Sabre Businesses
TravelMarketing and
Distribution
AirlineSolutions
2003 © UTD Slide 5 Center for Intelligent Supply NetworksC4ISN
160 products & services clients
70 software products
70 reservations clients
42 consulting clients
Diversified client list
Airline Solutions
2003 © UTD Slide 6 Center for Intelligent Supply NetworksC4ISN
CZEKMHPRTGNHKEQFOZBR
The World of Sabre
Central AmericaAMMXCMLRTAVHJRRG
USCOHPNWUAAADLNJUPZXDAWNHQ
EuropeSKAZIBAFSRSNMKDIOATK
Asia / PacificGFCXNZCAMUTCSQVTCIKAJL
AfricaSAATETUY
ACFLDHOHQXASHASYTZJIZWEDAW
South AmericaAVARKKLBLVPLRGTR
North AmericaSUYWTPOSIWLHSOAY7UTU
BYBAEWLOBDKLCYLTK8
Middle East
KUPKTK9WAIMS
YVF9YXPAQ25JXYOOJZNK9N9X2S
ZVEVEDZKAEBW7GXJNJWOEOVX
QKFH7FLIEDQK2KYX9X2YJBYJ
2003 © UTD Slide 7 Center for Intelligent Supply NetworksC4ISN
Pricing /Yield Management
Planning /SchedulingAirFliteOasisARMFAMTAMCRSSimDPMAPMSlot ManagerPC AirFlite
Flight OperationsFOSAirOpsAirPathSteadyStateFliteTracEagle
Crew ManagementAirCrewsBLGOCPOSCrewPlanCrewQualCrewTrac
Sales & MarketingTraverseCRSViewProVisionLiteVisionWiseVision
M & EMaxi-MerlinRAPS
Finance Systems
QuasarTravelCard Pro
AirportFliteSourceGate PlanGSE PlanStaffAdminStaffManagerStaffPlanGateManager
CargoCargoMaxCargoRev
Cargo ClaimsCargoNet
SabreCargoCargoPlan
Customer ServiceQik-Access
Dining & Cabin Services
AirMaxAirPriceAirPrice Contract
ComposerAvailability ProcessorGroup Management
System
AirServ
Airline Products and Services
2003 © UTD
Air Cargo Industry
transporting air cargointo the future
2003 © UTD Slide 9 Center for Intelligent Supply NetworksC4ISN
Air Cargo Industry
The international air cargo market is $40 billion a year and it is still the combination carriers that dominate. Growth in air cargo anticipated to exceed passenger traffic growth in every major regional marketForecasts anticipate the addition of more than 2,600 freighter airplanes by 2019, as nearly 1,100 current freighters retireCustomers around the globe are demanding better and faster productsThe air cargo industry has realized that information technology is key to meet their challenges
2003 © UTD Slide 10 Center for Intelligent Supply NetworksC4ISN
Growth is projected at 6.4%
World airborne cargo will grow at 6.4% per year during the next 20 years.
2003 © UTD Slide 11 Center for Intelligent Supply NetworksC4ISN
Asian cargo markets lead…
2003 © UTD Slide 12 Center for Intelligent Supply NetworksC4ISN
Major Traffic Flows
A snap shot of international freight flows as a percentage of IATA international scheduled freight
2003 © UTD Slide 13 Center for Intelligent Supply NetworksC4ISN
Top 25 Freight Airlines
2003 © UTD
Air Cargo Landscape
transporting air cargointo the future
2003 © UTD Slide 15 Center for Intelligent Supply NetworksC4ISN
At a very high level…
Shipper
Customers
Customers
Interline
Forwarder
Customers
Airport/Airline OperationsShipper/Forwarder Consignee
Terminal Terminal
Aircraft
Interline
2003 © UTD Slide 16 Center for Intelligent Supply NetworksC4ISN
A bit more detailed…
N
S
EW
Shipper
IntegratorsForwarder
Packaging
Warehousing
Distribution
Receiving
Ground Handling
Airline Trucking/Off-Airport Trucking
Inventory
Terminal Operations
Airlines
Airport
OwnTermnial
Operations
OwnGround
Handling
Own Aircraft
RegionalHub
Customs
2003 © UTD Slide 17 Center for Intelligent Supply NetworksC4ISN
The key players are...
Shipper Need a commodity sent anywhere in the world for the lowest cost and meet the required service level
Forwarder Act as the “middle man” between the shipper and the airline.
Integrator Operate in business-to-business markets and specialize in door-to-door transport solutions
Airline Operations Receive, store, transfer, track, load and unload cargo; assign and manage capacity; bill customers.
Consignee Recipient of the shipment.
2003 © UTD Slide 18 Center for Intelligent Supply NetworksC4ISN
Business challenges
Shipper
Make bookings
Negotiate best rates
Preparation of Documents - Customs, Insurance
Track shipments
Accept billings and make payments
Place claims and repair charges
Speed (time sensitive)
Reliability
Forwarder
Make bookings
Negotiate best rates
Preparation of Documents - Customs, Insurance
Track shipments
Accept billings and make payments
Place claims and repair charges
Speed (time sensitive)
Reliability
Booking acceptance
Bid for space –allotments
Distribution
Warehousing
Invoice shipper
Interact with multi-modal carriers
Messaging and Transaction Ability
Consolidation of Shipments
2003 © UTD Slide 19 Center for Intelligent Supply NetworksC4ISN
Business challenges
Airlines / Operations
Schedule cargo flights
Plan cargo routes
Initialize and open flights for booking
Negotiate rates
Publish prices/rates
Provide distribution channels
Forecast cargo capacity
Segment and forecast cargo demand
Plan for no-shows, cancellations and Overbook
Set-up Bid Prices
Accept /Reject shipments
Maximize Revenue
Improve load factors
Track shipments
Accept Bids from customers
Allocate Block Space -Allotments
Resource Management of Terminal Staff
Accept Shipments Tendered
Dangerous Goods Control
Package Validation
Shipment prioritization
Shipment re-accommodation
Plan Loading of cargo - build, containerize etc.
Unload Cargo
Load balancing
Warehousing
Obtain/Send Flight Manifest
ULD Management - Track, Inventory, Repairs etc.
Service Reliability
Plan for CAD
Track and re-route Refusals
2003 © UTD Slide 20 Center for Intelligent Supply NetworksC4ISN
Business challenges
Airlines / Operations
Offer Product Services - Express, Next Day
Track shipments, containers
Invoicing/Billing
Prorating
Interline billing
Revenue Accounting
Sales Accounting
Claims Management
Receive/Send updates on arrival
Receive/Send updates on delivery
Message Interactions
Airports / Operations
Warehousing -Storage
Customs
Security Clearance
Dangerous Goods Control
Package Validation
Notify Captain
Facilitate smooth cargo operations
Consignee
Track shipments
Accept billings and make payments
Place claims and repair charges
2003 © UTD Slide 21 Center for Intelligent Supply NetworksC4ISN
Mapping of Business Challenges
Reliability
Negotiate Best Rates
Bid for Space – Allotments
Schedule cargo flights
Plan cargo routes
Initialize and open flights for booking
Negotiate rates
Publish prices/rates
Forecast cargo capacity
Segment and forecast cargo demand
Plan for no-shows, cancellations and Overbook
Set-up Bid Prices
Accept /Reject shipments
Revenue Management
Maximize Revenue
Improve load factors
Accept Bids from customers
Allocate Block Space -Allotments
2003 © UTD Slide 22 Center for Intelligent Supply NetworksC4ISN
Mapping of Business Challenges
Invoicing/Billing
Prorating
Interline billing
Revenue Accounting
Sales Accounting
Accept billings and make payments
Revenue Accounting
Make bookings
Message Interactions
Shipment Tracking
Warehousing
ULD Management - Track, Inventory, Repairs etc.
Reservations and inventory control
2003 © UTD Slide 23 Center for Intelligent Supply NetworksC4ISN
Mapping of Business Challenges
Resource Management of Terminal Staff
Load / Unload Cargo
Staff Schedules
Union Contracts
Shipment Prioritization
Receive Shipments
Terminal Operations
ULD Tracking
ULD Control
Distribution & Warehousing
Container Management
2003 © UTD Slide 24 Center for Intelligent Supply NetworksC4ISN
Mapping of Business Challenges
Filing claims and repair charges
Claims processing
Claims tracking
Claims Management
Security Clearance
Embargo / Quarantine
Dangerous Goods control
Paperwork
Customs
2003 © UTD Slide 25 Center for Intelligent Supply NetworksC4ISN
Sabre Cargo Portfolio
Airline Business Area Sabre ProductRevenue Management Y CargoMaxRevenue Accounting Y CargoRev
Reservations & Inventory Control Y Sabre Cargo, QIK CargoClaims Management Y Claims ManagementTerminal Operations Y CargoPlan, CargoNet
Customs XContainer Management X
Warehousing XOther Potential Business Areas
Solutions for Forwarders XSolutions for multi-modal carriers X
Auctions XSolutions for Airline alliances (like WOW) XSolutions for Airline Forwarder alliances X
Solutions for Integrators X
2003 © UTD
Cargo Revenue Management Concepts
transporting air cargointo the future
2003 © UTD Slide 27 Center for Intelligent Supply NetworksC4ISN
Revenue management is ..
Integrated management of pricing and inventoryTo maximize total revenue
By selling the right product (right price) to the right customer at the right time
Key components areReduction of spoilage by selling more than available capacityMatch demand and supply (capacity) at network level
Considers routing possibilitiesLong-haul displacing short-haul
Key RequirementsProduct/price differentiation existsDemand exceeds supply
2003 © UTD Slide 28 Center for Intelligent Supply NetworksC4ISN
Revenue without and with price differentiation
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0 25 50 75 100 125 150 175 200
Demand (tons)
Rat
e ($
/kg)
Price-Demand Function
80 tons @ $1.20
Revenue - $96,000
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
2.00
0 25 50 75 100 125 150 175 200
Demand (tons)
Rat
e ($
/kg)
Price-Demand Function
45 tons @ $1.20
35 tons @ $1.65
75 tons @ $0.40Revenue - $141,750
2003 © UTD Slide 29 Center for Intelligent Supply NetworksC4ISN
Passenger vs. Cargo Revenue Management
Capacity is not known and not fixedCapacity is 2-dimensionalCargo can be over or under-tenderedLimited number of customers Relationship based businessRouting in most cases is flexibleDemand is lumpySparse data and poor quality of data
Less sophisticated usersReluctance to new methods
2003 © UTD Slide 30 Center for Intelligent Supply NetworksC4ISN
Revenue Management – Value proposition
Improves revenue and profits (generally 2 to 7%)Accuracy in capacity forecasts (consistent under forecasting) -translates to more space to carry cargoBetter overbooking decisions - more cargo booked to offset no-shows and cancellationsOptimal station/customer allotments - low value customers do not block valuable spaceBetter rate and density mix - maximize the use of all dimensions of capacity and protect space for high value cargo
Reduces offloads and service failuresAccuracy in capacity forecasts (consistent over forecasting) -translates to less refusalsBetter overbooking decisions - improved service reliability when showup is highEnsuring operational feasibility through router module
2003 © UTD Slide 31 Center for Intelligent Supply NetworksC4ISN
Critical success factors
Process fit - current processes and process gapsOrganizational alignment – departments aligned to processes Technology fit - integrating revenue management system with existing systemsData availability - the major data requirements include post departure data, air waybill data, passenger bookings, and flight scheduleUser acceptance - acceptance and willingness from the user community
2003 © UTD
Cargo Revenue Management Solution
transporting air cargointo the future
2003 © UTD Slide 33 Center for Intelligent Supply NetworksC4ISN
Cargo revenue management - Functional Overview
Medium to long-term allotment managementOptimal assignment of space to station/customers
Short-term capacity and demand managementCapacity forecastingShowup rate forecastingOverbookingDemand forecasting Route generatorBid pricing
Reporting and proactive flight management toolsManagement reportsFlight workbench (critical flights analyzer)Performance monitoringCustomer valuation
2003 © UTD Slide 34 Center for Intelligent Supply NetworksC4ISN
Interaction among revenue management components
Overbooking
Show-upRate
Forecasting
CapacityForecasting
Bid Pricing DemandForecasting
RouteGenerator
ValueDetermination
FlightManager
Common Functionality
AllotmentManagement
Cargo CapacitiesBid Prices
Space AllocationsValues
Management Reports
PerformanceMonitoring
2003 © UTD Slide 35 Center for Intelligent Supply NetworksC4ISN
Modeling and Functionality
Focuses on profitability as opposed to load factorManages network capacity instead of leg capacityFocuses on freight mix based on density and revenue Controls are based on commercial feasibility and operational feasibilityCapacity segmentation is in terms of time horizon and productsAllows the capability use ‘bonus’ or ‘value’ based on O&D and customer.
2003 © UTD Slide 36 Center for Intelligent Supply NetworksC4ISN
System Setup
Control values are set several days before departure and then updated nightly until departureControl values are in terms of weight, volume, and positionsUsers have the ability to set default values and overrides Monitors performance of forecasts and user overrides
2003 © UTD
Approach to Forecasting
transporting air cargointo the future
2003 © UTD Slide 38 Center for Intelligent Supply NetworksC4ISN
Approach to Forecasting
Capacity, showup rate, and demand forecasts are key inputsForecasting is based on historical and current dataForecasts are monitored on a regular basis
2003 © UTD Slide 39 Center for Intelligent Supply NetworksC4ISN
Cargo Capacity
Psgr Weight
PAX Bag Weight
Other Weight
Extra Fuel Weight
PAX Weight
Mail Weight
Allotments
FreesaleTotal Cargo
Capacity
Allotments
Freesale
PAX Bag Volume
Mail Volume
Other VolumeBelly Volume
Total Cargo Capacity
Payload
2003 © UTD Slide 40 Center for Intelligent Supply NetworksC4ISN
Capacity Forecasting
A tool for forecasting the capacity available for cargo by flight leg and departure dateForecasts flight capacities in terms of weight, volume, and positionsInterfaces with Passenger Revenue Management system to obtain expected passengers on board
2003 © UTD Slide 41 Center for Intelligent Supply NetworksC4ISN
Capacity Forecasting
CapacityForecasting
Model
PassengerForecast
Fuel Weight
MailForecast
StackingLoss
BagStandards
Positions PayloadForecast
BellyVolume
Capacity forecasts
by flight leg and
departure date
2003 © UTD Slide 42 Center for Intelligent Supply NetworksC4ISN
Showup Rate Forecasting
A tool for forecasting the booking behavior of flights by flight leg and departure dateConsiders no-shows, cancellations, and over/under tendering.Forecasts showup rates in terms of weight, volume, and positions
2003 © UTD Slide 43 Center for Intelligent Supply NetworksC4ISN
Showup Rate Forecasting
ShowupForecasting
ModelHistoricalBooking
Historical Tender
Showupforecasts
by flight leg and
departure date
2003 © UTD Slide 44 Center for Intelligent Supply NetworksC4ISN
Demand Forecasting
To properly manage cargo profitability, forecasting needs to be done by demand groups or ‘rate classes’A rate class should have shipments that are very similar in density and revenue - to address density mix and rate mix Key data requirements include historical bookings and current bookings
2003 © UTD Slide 45 Center for Intelligent Supply NetworksC4ISN
Rate/Density Clustering (Rate Class)
0
0.5
1
1.5
2
2.5
0 2 4 6 8 10 12 14 16 18 20
Density
Reve
nue
Revenue Class
2003 © UTD Slide 46 Center for Intelligent Supply NetworksC4ISN
Rate/Density Clustering (Rate Class)
Remaining demand by Rate-Density class
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0.50
1.00
1.50
2.00
0 20 40 60 80 100 120 140 160 180 200 220 240
Density (kg/Cu.M)
Rat
e($/
Kg)
10 11 12
7 8 9
4 5 6
1 2 3
5 tons
10 tons
10 tons
5 tons
5 tons
10 tons
15 tons
4 tons
0 tons
4 tons
3 tons
2 tons
2003 © UTD Slide 47 Center for Intelligent Supply NetworksC4ISN
Demand Forecasting
DemandGroup
Definition
DemandGrouping
DemandForecasting
Demand Forecasts
CurrentBooking
HistoricalBooking
ForecastingModel
Parameters
2003 © UTD
Methodology for Optimization
transporting air cargointo the future
2003 © UTD Slide 49 Center for Intelligent Supply NetworksC4ISN
Methodology for Optimization
Capacity segmentation by time - allotment optimization for medium term customer/station allotmentsOverbooking of remaining space to account for booking behaviorGenerate routing options to carry cargoMatch O&D demand to leg capacities considering routing options and rate/density classes
Compute bid prices Determine capacity allocations
2003 © UTD Slide 50 Center for Intelligent Supply NetworksC4ISN
Allotment Management
A tool for managing medium term station or customer agreementsContains a network based optimization model that evaluates competing requests based on:
Promised revenueHistorical utilizationFlight scheduleAvailable capacity for allotmentsA pre-defined set of business rules
The model generates a business-practical solution that maximizes the revenue for the airline
2003 © UTD Slide 51 Center for Intelligent Supply NetworksC4ISN
Allotment Management
AllotmentManagement
Model
AllotmentUsageModel
Allotment Requests
RecommendedAllotments
AvailableCapacity
ContainerData
AllocationRules
Schedule
2003 © UTD Slide 52 Center for Intelligent Supply NetworksC4ISN
Overbooking
Overbooking offsets the effect of cancellations, no-shows, and over/under-tenderingBased on booking behavior, economic aspects (maximize net revenue or minimize sum of offload and spoilage costs), and acceptable level of service failureUsers can set the maximum acceptable service failures at flight, entity, or system levelUsers have the ability to override the authorized capacities for specific flight departures
2003 © UTD Slide 53 Center for Intelligent Supply NetworksC4ISN
Spoilage and Offload Costs
0 20 40 60 80 100 120 140 160
Overbooking Level
Cost
Spoilage Costs
Offload Costs
Overbooking Level
Total Cost
2003 © UTD Slide 54 Center for Intelligent Supply NetworksC4ISN
Overbooking
AuthorizedCapacity
OverbookingModel
Show-up RateModel
FlownAir waybills
BookingRecords
CapacityForecasts
Costs andOverrides
AcceptableServiceFailure
2003 © UTD Slide 55 Center for Intelligent Supply NetworksC4ISN
Routing
Generates routes for the bid price model within the Revenue Management ModuleDetermines feasible routes at the time of processing a booking request
Takes into account operational requirementsAccounts for shipment characteristicsEnsures that the selected routes meet the service levelConsiders cost, time, and distanceHandles the complexity of multi-hub network
2003 © UTD Slide 56 Center for Intelligent Supply NetworksC4ISN
Routing
Shortest PathModel
ServiceLevel
ConnectTimes
Via/Not Via
Schedule
ShipmentCharacteristics
Route Costsand Times
Routes
2003 © UTD Slide 57 Center for Intelligent Supply NetworksC4ISN
Bid Pricing / Allocation
Is an O/D based network optimization model that maximizes network profitabilityMatches demand to capacity over multiple routes by redirecting cargo from high to low-load factor flightsLeg bid price for a flight leg is (wt BP + VolBP/density)O and D bid price is the sum of the bid prices of flight legs along the route of the shipment Bid price gradients enable bid prices to be adjusted to reflect capacity changes
2003 © UTD Slide 58 Center for Intelligent Supply NetworksC4ISN
Bid Price Example
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1.00
1.50
2.00
2.50
0 5 10 15 20 25 30 35 40
Demand (tons)
Rate
($/k
g)
Capacity = 15 tonsBid Price = $1.00/kg
6 tons @ $1.50
3 tons
@ $2.00
10 tons @ $1.00
20 tons @ $0.50
Capacity = 25 tonsBid Price = $0.50/kg
2003 © UTD Slide 59 Center for Intelligent Supply NetworksC4ISN
Bid Price
Network BasedBid Price Model
ConnectionMatrix
DemandForecasts
AuthorizedCapacity
Schedule
Revenueand Cost
Space allocation
to Demand Group
BidPrices
2003 © UTD Slide 60 Center for Intelligent Supply NetworksC4ISN
Customer Valuation
A tool for assigning relative importance to major customersCustomer value is used to scale up or scale down the offered rate when making booking acceptance decisionsCustomer value may vary by segment and seasonCustomer value can be based on several variables: total revenue, space utilization, commodity shipped, claims history, etc.
2003 © UTD Slide 61 Center for Intelligent Supply NetworksC4ISN
Customer Valuation
Value Determination
Model
User Preferences
Customer values
Operations
RevenueManagement
Finance
Sales
2003 © UTD Slide 62 Center for Intelligent Supply NetworksC4ISN
Major categories of data
Flight ScheduleBooked and tendered cargo information
Advance and live air waybillsPre and post departure flight information
Mail, bags, passengers, cargo, payload, fuel, etc.Expected passengers on board Reference data
Aircraft, container, station, customer, commodity, products, etc.
Historical data
2003 © UTD Slide 63 Center for Intelligent Supply NetworksC4ISN
Key interfaces
Cargo RMS
Passenger Booking
Post-departure data
Cargo ReservationSystem
Booked Air Waybills
Bid Price/AllocationsBooking Requests and
other updates
CargoRevenue
Accounting
Flight Operations/
Departure Control
Passenger Revenue
Management
Flown Air Waybills
Schedule
DB Tables/User Inputs
Reference Data
Legend:RealReal--time linktime link
Batch ProcessBatch Process
Batch/realBatch/real--timetime
2003 © UTD Slide 64 Center for Intelligent Supply NetworksC4ISN
Further research is needed …
Overbooking modelShow up rate distribution2-dimensionalityShipment level overbooking
Demand forecastingStability/accuracy of forecasts - Lumpy demand, multiple rate classes, fewer booking requestsPredicting when to use and when not to use forecastsEstimating true demand: demand reduction/un-truncation
Integrated overbooking and bid pricing
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