khalid usman, vice president, oliver wyman

© 2012 OLIVER WYMAN

AVIATION, AEROSPACE & DEFENCE

Khalid Usman Jan 28, 2014

Fundamentals of QSI

1 © Oliver Wyman 1

Agenda

• QSI overview

• Uses of QSI

• QSI methodology

• Framework of QSI forecasting

• Summary and Discussion

QSI Overview Section 1

3 © Oliver Wyman 3

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

4 © Oliver Wyman 4

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

6 © Oliver Wyman 6

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)

7 © Oliver Wyman 7

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

10 10 © Oliver Wyman

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


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


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


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


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


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


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


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


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


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


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


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


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


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


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

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pa

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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]

khalid usman, vice president, oliver wyman

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