Khalid Usman, Vice President, Oliver Wyman
Transcript of Khalid Usman, Vice President, Oliver Wyman
© 2012 OLIVER WYMAN
AVIATION, AEROSPACE & DEFENCE
Khalid Usman Jan 28, 2014
Fundamentals of QSI
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Agenda
• QSI overview
• Uses of QSI
• QSI methodology
• Framework of QSI forecasting
• Summary and Discussion
QSI Overview Section 1
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QSI Overview
• QSI stands for Quality of Service Index
– History dates back to 1970’s where it was developed by the Civil Aeronautics Board (CAB)
• Based on the product attributes, quantifies the relative attractiveness of consumer choices
• QSI is a quantitative score that is assigned to each travel choice available to the traveler
– Higher QSI score would mean that the itinerary is preferable
– Lower QSI score would mean less attractiveness
• QSI scores then help determine market share that each itinerary will capture
– Market share : Itinerary QSI / Market QSI
– Where Market QSI is the total QSI in the market
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QSI Overview
• QSI Models are calibrated based on historical data for passenger travel
– Revealed preference (ticketing/booking) vs. stated preference (survey)
– Ticketing/booking data is used for calibration
• QSI methodology originated in deregulated era where fares were not a determinant of choice
• Currently the landscape has changed, with fares being a very important determinant of choice
– QSI methodology still proves to be robust
– Very important to properly calibrate and maintain the models
• Alternate methodologies to QSI are available – Logit modeling
– An advanced technique that has been used in transportation modeling and planning
Uses of QSI Section 2
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Uses of QSI
• QSI is used to forecast passenger demand on flights, and associated revenue and profit/loss
• QSI is generally an integral part of Network planning model (or called Network-simulation
model/Schedule profitability forecasting models)
• Mainly used to answers questions such as:
– Market share that a new flight will capture
– Load factors and passengers carried on a flight
– Local and connecting passenger mix, and what O&D’s are participating in the flow traffic
– Diversion of passengers from competitors with new flight introduction
– Revenue associated with the flight (local revenue and connecting revenue)
– Network contribution and P&L (achieved through cost modeling)
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Uses of QSI
• Different entities in the aviation industry use QSI for answering important business questions
Airlines
• Forecast route load factors and passengers
• Passenger mix: local/connecting
• Network flow
• Revenue forecast
• P & L
• Fleet decisions
• Mergers and acquisition analysis
Airports
• New route opportunities for airports
• Presentation of business cases with same methodology that airlines use
Manufacturers
• Fleet changes and more economic flying/matching of supply and demand with fleet types
Government/ Regulatory authorities
• Competition studies, merger analysis
QSI Methodology Section 3
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Factors affecting QSI
• Typically QSI models will include the following factors:
– Level of service
- Non-stop
- Direct
- Single connection
- Double connection
– Codeshare and Interline factor
– Aircraft size preference, penalty for turbo-props
– Departure time preference
– Elapsed time preference
– Longer connection time penalty
– Airline preference factors
• Additionally, other factors can be included
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QSI and market share calculations Market share models are based on itinerary scoring mechanism
Example: Albuquerque – New York
Hypothetical market with 4 itineraries/day between ABQ and JFK
Itinerary
Index
score
Expected
share
Non-stop 7.0 96.0%
1-stop via
ORD
0.27 3.7%
2-stop via
DFW, RDU
0.021 0.3%
Total market 7.29 100.0%
Illustrative
ABQ
JFK
ORD
DFW
RDU
MCO
Non-stop itinerary
Score: 7.0
One-stop itinerary
Score: 0.27
Two-stop itinerary
Score: 0.021
Invalid itinerary
Circuity > 1.35
*Any alternate airport effect such as LGA or EWR are not included for simplicity
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QSI and demand allocation Total marketsize is split using predicted market share
Example: Albuquerque – New York
Hypothetical market with 3 valid itineraries/day between ABQ and JFK
Illustrative
ABQ
*Any alternate airport effect such as LGA or EWR are not included for simplicity
JFK
ORD
RDU DFW
Non-stop
Single-connection
Double-connection
QSI Sore: 7.0
100 PDEW QSI Sore: 0.27
QSI Sore: 0.021
Market QSI: 7.29
Marketsize/
Size of Pie
QSI Model/
Share of Pie
Non-stop
share: 96%
Single
Connection
share: 3.7%
Double
Connection
share: 0.3%
Forecasted
Passengers
Non-stop: 96
Single
connect: 3.7
Double
connect: 0.3
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Determinants of QSI score
• Non-stop flights get highest QSI relative to connections
– In long-haul markets, connections get higher relative preference compared to short-haul
markets
– Double connections get very low preference compared to non-stops and single connections
• Codeshare flights are becoming more and more important
– Bi-lateral JVs typically get higher preference than regular codeshares
– Differentiation between local codeshare and connection codeshare, local codeshares get less
preference
• Interline flights get lower preference than codeshares
• Flights having higher elapsed time get lower preference, similarly flights having longer ground
time get lower preference
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Determinants of QSI score
• Aircraft size matters (regional jets and turbo-prop preference)
– Relation to distance
– Some aircraft types get higher preference, more in long-haul
• Time of day preference
– Important for business markets
• S-curve effects
– Can be built in QSI models, tendency is to treat any adjustments outside of model
• Airline preference factors and share gaps
– Some airlines perform better than their fair share
– Low cost carriers with lower fares than market averages get higher share
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Non-stop flights get highest preference In US domestic markets with non-stop service, 90% of traffic is captured on non-stop flights (based on Q2, 2013 Db1b data)
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ATLLGA BOSDCA FLLLGA JFKLAX JFKSFO LASLAX LASSFO LAXORD LAXSFO LGAORD
Non-stop Single Connect Double Connect
Top 10 US Mainland markets
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Market distance is an important determinant of relative preference In short-haul markets non-stop flights get much higher preference as compared to connections
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LASLAX LAXSFO BOSDCA LASSFO LGAORD ATLLGA FLLLGA LAXORD JFKLAX JFKSFO
Non-stop Single Connect Double Connect Non-stop Miles
Top 10 US Mainland markets
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How QSI parameters are calculated Historical data is used for calibration of parameters
• Historical data is used to calculate QSI parameters
– Typically data used is DOT Db1b data, MIDT and PaxIS data
• Co-efficients are based on large amounts of passenger booking or ticketing data
• Different models are calibrated:
– Level of service
– Elapsed time
– Codeshare/interline preference
– Ground time penalty
– Aircraft size
• Typically, regression modeling and statistical curve fitting techniques are used to calibrate
parameters
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QSI can be calculated for limited set of markets or the whole world In an airline network, each flight gets flow traffic from large number of O&D markets • A world-wide itinerary QSI file has several million itineraries
– Jun 2013: 7 million+ itineraries
– File size over 800 Mb
• Example of a QSI data file is given below
Framework of QSI forecasting Section 4
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QSI Forecasting Process The QSI model takes schedules, airport details, and alliance information into consideration as it builds itineraries and QSI scores
• QSI market share models are only one component of the overall forecasting process
• As airport planners and air service development practitioners, it important to understand the main
components of the forecasting process
• QSI calculations for particular O&D markets can be done manually, however due to complexity of
forecasting flights that involve network flow it becomes impractical
• First step is to take the input data and build all possible itineraries (choices) based on the
business rules/constraints
• Spill & recapture is another component of the modeling that should be used as capacity constraint
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QSI Scoring Assignment
Market share calculation and
demand allocation
Connection generator
Input
QSI Forecasting Process Forecasting process goes through a series of steps to produce final output
• Schedule: SSIM, OAG/Innovata, Codeshare • Airport Specific Details: Minimum connection time (MCT), airport reference list • Demand and fare databases: DOT data, MIDT, PaxIS, Vendor calibrated
• Build all possible connection itineraries: Online, codeshare, interline • Connections are subject to: MCT, circuitry rules, Maximum connection time limits • Cabotage, traffic rights are handled through traffic suppression codes
• Service levels (non-stop, single connection, double connection, through flight) • Aircraft size (Mainline, RJ, turbo-prop) and other preferences • Codeshare and interline preference • Elapsed time and ground time preference • Share gaps and airline preference
• Market share for each itinerary is calculated based on the QSI score generated • Itinerary demand is calculated based on the itinerary markets share
Segment allocation &
Spill and recapture
Final Output
• Itinerary demand is allocated to flight segments
• Available demand is constrained by the capacity of the aircraft
• Boeing spill model is used
• Forecast is obtained at the flight/market level
• Details of network flow available • Misconnection analysis, revenue diversion analysis • Alliance valuation and codeshare analysis
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Different Levels of route forecasts While flight/route centric forecasts are important network contribution should not be ignored which provides better system view
Passenger/Load Factor
Revenue
P &L Forecasts
• Provides the basic guidance whether operating flight in
a new market, or adding frequency makes sense or not
• Differentiates markets based on the average fares paid
• Cargo and ancillary revenue are important contributors. Markets
operated by wide-body aircraft could have significant cargo revenue
• While passenger and resulting revenue are important metric, final
evaluation criteria is flight profitability
• Includes costs also: P& L = Revenue - Cost
Route profitability
Route profitability with aircraft ownership cost
Fully allocated costs (including ownership)
Short-term decision horizon
Long-term decision horizon
Basic
Comprehensive
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QSI forecasting software Software packages utilize input data to produce market forecasts
• While QSI forecasting software provides a lot of advantages, however it should be used with
proper caution
• Calibration of the model is very important
– Comparing historical actuals with historical forecast
– Applying share gaps and any other required adjustments
• Input data is most critical, bad data feed will provide bad decisions
• New markets require stimulation study to stimulate demand – generally done outside the model,
although automated features can be built within model
• Proper maintenance of the model is required for getting good results and output. Includes
parameter calibration, marketsizes, schedule and MCT data updates
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QSI forecasting software QSI Software allows modeling of network flows. Example UA ORD-SFO flight
43%
44 %
13%
Local O&D
Single
Connections
Double
Connections
Flight Composition
ORD SFO
Illustrative
• Network flow over flights means that each flight will have passengers
from several to several hundred markets
• ORD-SFO flight serves a lot of connecting markets – both domestic
and international
• Since QSIs and shares need to be calculated for several hundred
markets for a flight forecast, it is not practical via spreadsheets
• Powerful QSI software area ideally suited for forecasting complex
airline networks
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QSI forecasting schematic Important to understand the various components of forecasting
Forecasted Marketsize
Boeing/Airbus Market Outlook, IMF GDP
Forecasts
QSI Forecasting Model
Worldwide Connection Generator
Market Share Model (QSI)
Spill and Recapture Model
Industry Schedule Data, Minimum connection time
(MCT data)
Schedule alternatives for Evaluation
Model Calibration Process (historical forecast vs. actual)
Segment based Load factor information (Internal)
True O&D passenger and fare information (Internal)
Seat Information (Internal)
P&L Forecast Cost Information (Internal)
Estimation of Fares for new routes
Final Output: By Route,Market forecast
Flight forecast Diversion, share analysis
Marketsize Stimulation Model
Base Marketsize (historical)
Legend:
Internal data
Summary and discussion Section 5
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Summary
• QSI modeling helps build the business case for new services
– However, it should be accompanied by complete business case
– Important to utilize credible and similar data sources as airlines are using
• Market size is one of the most critical inputs to QSI models
– For US domestic markets, US DOT is the standard
– International market sizes are MIDT, PaxIS based or vendor calibrated
– Calibration of model is very important, modeling bias would distort the results
– Running the model against historical month and checking against actual data
• New markets or additional service may require marketsize stimulation
– Based on historical data
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Interesting facts Top 15 QSI producers in the world
• Overall, QSI gets correlated
with number of departures
• Airlines with higher network
connectivity get higher overall
QSI values since they
generate more connecting
service
• LCCs tend to get capture
higher share than just
predicted by schedule
preference due to fare effect -
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DL UA AA US WN LH CZ AF AC MU FR TK BA CA U2
QSI (Weekly) Departures (Weekly)
QS
I
De
pa
rtu
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About Oliver Wyman With offices in 50+ cities across 25 countries, Oliver Wyman is a leading global management consulting firm that combines deep industry knowledge with specialized expertise in strategy, operations, risk management, organizational transformation, and leadership development. The firm’s 3,000 professionals help clients optimize their businesses, improve their operations and risk profile, and accelerate their organizational performance to seize the most attractive opportunities. Oliver Wyman is part of Marsh & McLennan Companies [NYSE: MMC]. For more information, visit www.oliverwyman.com Oliver Wyman’s global Aviation, Aerospace & Defense practice helps passenger and cargo carriers, OEM and parts manufacturers, aerospace/ defense companies, airports, and MRO and other service providers develop value growth strategies, improve operations, and maximize organizational effectiveness. Our deep industry expertise and our specialized capabilities make us a leader in serving the needs of the industry. Also, Oliver Wyman offers a powerful suite of industry data and analytical tools to drive key business insights through www.planestats.com. For more information, please contact: Khalid Usman 202.331.3691 [email protected]