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Overview of Boeing Planning Tools Alex Heiter
Network, Fleet and Schedule
Strategic Planning
Module 16: 2 April 2015
Istanbul Technical University
Air Transportation Management
M.Sc. Program
2
Lecture Outline
Introduction to Boeing Network & Fleet Planning
Overview of Boeing SOARS tools
Examples of “real-life” airline studies
Potential areas of joint airline-Boeing collaboration
3
Network & Fleet Planning Decisions
Include a Wide Range of Factors
Market &
Route
Economics
Route
Level
Network
Level
Economics
Point-to-Point
Fleet
Considerations
Airline Capacity/
Supply
Passenger
Demand
Competition
Government/
Airport/
Regulatory
Origin –
Destination
Airline Fleet
4
Network & Fleet Planning Goals
COST
PROFITABILITY
PRODUCTIVITY / EFFICIENCY
REVENUE
5
Areas of Investigation
Area Description / Area of Investigation
Traffic
Forecasting
Airline traffic forecasts for passenger demand by fare class Sizing & scoping of OD demand Fare & yield analysis
Network
Planning
Network scenario profitability modeling Identification & evaluation of potential future markets Evaluation of different airplane configurations & passenger mix Market feasibility studies / network profitability analysis
Scheduling
Schedule development and evaluation / hub evaluation Schedule design, best practices, connection analysis, etc. Schedule-based fleet plans Airplane & route optimization
Fleet
Planning
Traffic based fleet plans & economics Fleet mix analysis & optimization (spill, revenue, profitability) Boeing & competitor fleet plans / fleet optimization Fleet phasing plans (additions & retirements)
Shaping the
Market
Sharing network & fleet planning best practices both internally and externally Apply airline perspective & industry expertise to product development
Revenue
Management
Industry awareness of revenue management systems Airline recommendations on value derived from revenue management systems
6
What Kind of Work Does Boeing NFP Do?
NETWORK & FLEET PLANNING FOCUS AREAS:
7
What Kind of Work Does Boeing NFP Do?
NETWORK & FLEET PLANNING FOCUS AREAS:
8
What Kind of Work Does Boeing NFP Do?
NETWORK & FLEET PLANNING FOCUS AREAS:
9
What Kind of Work Does Boeing NFP Do?
NETWORK & FLEET PLANNING FOCUS AREAS:
10
What Kind of Work Does Boeing NFP Do?
NETWORK & FLEET PLANNING FOCUS AREAS:
11
Boeing’s Network and Fleet Planning Suite
Top
Down Fleet
Planning
Network Modeling
SOARS
TOP DOWN (Macro)
Utilizes airline RPK data, regional
schedules & growth rates, and airplane
characteristics to evaluate profitability
models.
SOARS (Advanced)
Comprehensive market & schedule
data used to evaluate optimized fleet
planning scenarios and
competitively derived market
demand.
Analysis
Sophistication
Network Profitability Model
(Intermediate)
Itinerary-based tool involving markets,
frequencies & equipment, fares and
costs.
Schedule Optimization & Airline Revenue System
12
Schedule Optimization & Airline
Revenue System (SOARS)
A Boeing-developed tool-kit to create & validate network
analysis
Assess comparable airplane products and their impact on a
network
Solve for the most profitable fleet mix and network utilization
improvements
Identify frequency and capacity opportunities for existing and
new markets
Identify hub optimization opportunities and maximize
connectivity
Achieve ‘operationally feasible’ schedules with airline specific
constraints
13
Schedule Optimization & Airline
Revenue System (SOARS)
PROFITABLE
SOLUTION
FLEET
SCENARIO
GMAS Market Allocation
System Boeing Developed
FOM Fleet Optimization
Module Boeing Developed
ODSE Flight Schedule
Planner Boeing Developed
• O&D paths by passenger preference
• Host airline vs. global airline networks
• Service level probabilities
• Operationally feasible solutions
• Implementable recommendations
• Optimized markets & frequency
Optimal network aircraft
Based on demand, revenue & costs
Spill, re-capture, performance, constraints
• Schedule changes / editing
• Comprehensive airline statistics
• Hub Connectivity Analysis
14
Global Market Allocation System
(GMAS)
How passengers use airline networks to reach their destinations
Network
Exploration
Passenger
Choice Logit
Model
Competitor
Analysis
Constrained
Allocation
Fleet
Optimization
Module
Flight
Schedule
Planner
Path
Generation
Unconstrained
Allocation
Global
Airline
Schedule
Global
O&D
and
Fares Network
Scenario
Schedule
Initiatives
15
GMAS - The Passenger Choice Model:
Example Dublin-Boston (GMAS)
GMAS forecasts the
probability of passenger
choice for all flight
paths between an OD
city-pair
Probabilities are derived
from competitiveness of
flight paths based on
flight duration and
number of stops
DUB
BOS
SNN
JFK
34% Probability
One-stop
8:05 Hrs
3% Probability
One-stop
8:55 Hrs
5% Probability
One-stop
8:55 Hrs
14% Probability
One-stop
8:05 Hrs
71% Probability
Non-stop
6:35 Hrs
16
Fleet Optimization Module (FOM)
Placing the right airplanes on the right routes
Demand & Fares
Spill Model
Cost Structure
Airplane Specs
Airport Limitations
Maintenance
Cargo
Optimized-
for-Profit
Solution
Network
Scenario
CPLEX
Min Turn
Sensitivities
Fuel Price
Sensitivities
Revenue
Sensitivities
Pax
Choice
Model
Schedule
Editor
17
Fleet Optimization Analytical
Methodology
18
Origin Destination Schedule Editor
(ODSE)
Schedule editing and
development with visibility
of rotations to improve
utilization and airplane
efficiencies
Imports SSIM or OAG data
files for creating and
editing schedules
Uses airplane definitions,
configurations and
operating data
Operational constraints are
applied to schedule
19
Wide array of data needed for optimal
analysis and decision-making
Boeing procures data from both internal (cost, performance) and external
(traffic, yields, schedules, macro) sources
Data Stream Description Sources
Schedule
Worldwide airline schedules, updated weekly.
Innovata, OAG
Passenger/Traffic
Origin & Destination, on-board loads, fares/revenue.
Sabre, PaxIS, Diio, Travelport
Profitability/Performance
Actual profitability of flights/markets with detailed passenger breakdown.
Internal
On-time Performance
Actual on-time performance with detail by flight.
Internal
Economic
GDP, historical traffic growth rates, disposable income, fuel prices, etc.
Various sources (usually governmental)
Aircraft
Payload/Range, economics, maintenance, fuel burn, etc.
Aircraft Manufacturer
Cost
Detailed cost breakdowns by category.
Internal/Aircraft Manufacturer
20
SOARS Network Modeling Process
Excel-based modeling is used in conjunction with SOARS tools to estimate
the financial (profitability) impact of various fleet and network scenarios
Existing Network / Future Network (SOARS) Baseline and Future Network Profitability
Cost Data Airline Specific Rules (When Available)
Boeing ICAS
Airplane Performance
Boeing APNav
Ownership Rates
(Airline Specific)
Base Traffic/Revenue Forecasted Growth Rates (Boeing CMO or Airline provided)
Airline Published
Data
Validation & Verification
Pax Yield & Demand Data
Third-party or Airline provided
Cargo Yield & Demand Data
Third-party or Airline provided
21
Key Performance Indicators (KPIs) are
important for measuring success
Metric Degree of Difficulty
in Calculating
Load Factor Low
On Time Performance Low
Asset Utilization Low
Passenger Misconnection Data Medium
Market Share Medium
Revenue per ASK Medium
Cost per ASK High
Profit and Loss High
22
KPIs: Successful airlines focus on maximizing
revenue per unit of capacity (RASK)
Yield =
Total passenger revenue
Revenue passenger kilometers
RASK =
Total passenger revenue
Available seat kilometers
Revenue per passenger-kilometer
Load Factor =
Total passengers flown
Total available seats
Revenue per available seat-kilometer
23
“Real World” Airline Study Examples
Example airline hub schedule restructure utilizing ODSE and GMAS
Adjusted hub schedules and modeled impact of connectivity
Potential LOT Warsaw Structure
0
50
100
150
200
250
600-700 730-900 920-
1050
1100-
1220
1220-
1410
1420-
1520
1530-
1600
1600-
1710
1715-
1900
2000-
2200
2215-
2245
Weekly
Fre
qu
en
cy
Arrivals
Departures
EXAMPLE EUROPEAN HUB:
POTENTIAL RE-STRUCTURE
24
“Real World” Airline Study Examples
Positive impact of schedule redesign: Incremental transit traffic due to
greater connectivity which improving local demand preference
Projected Impact of Alternate WAW Schedule
9.8%
5.6%
31.6%
13.5%
0%
5%
10%
15%
20%
25%
30%
35%
Capacity Local Demand Transit Demand Total Demand
Ch
an
ge v
s.
Au
gu
st
09
Modeled impact of re-structured schedule
25
“Real World” Airline Study Examples
Utilizing ODSE to schedule potential new flights, a financial forecast
can be created following a GMAS estimation of demands/fares
$(10)
$(8)
$(6)
$(4)
$(2)
$-
$2
$4
$6
$8
An
nu
al G
ross
Pro
fit
(exc
l. f
ixe
d c
ost
s) -
20
10
US$
M
Forecast Longhaul Route ProfitabilityAnnualized 2015, 4x Weekly Service
26
“Real World” Airline Study Examples
Utilizing SOARS tools and methods, alterative network scenarios can
be evaluated at a flight, route or system level
737-800 Opportunity Atlanta-Los Angeles
$0.6
$14.3
$2.5
$4.4
$1.0
$1.8
$0.6
$1.0
$1.6
$3.2
$0.6
$0.1
$0.0$0.2$0.1
$0.1
$0.4
$0.5
$0.2
$0.2
$0.3
$0
$2
$4
$6
$8
$10
$12
$14
$16
$18
Revenue Traff ic Yield Fuel Maintenance Flight Crew Cabin Crew Landing Other Revenue-
Related
Ow nership Contribution
An
nu
al C
os
t / R
ev
en
ue
($
M)
Projected current 73G performance
737-800 superior revenue/profit
737-700 lower cost
27
“Real World” Airline Study Examples
Detailed financial modeling is required for any number of network
and/or fleet analyses
Statistics 737-700 737-800 E190 Total 737-700 737-800 E190 Total 737-700 737-800 E190 Total
Stage Length 1,104 1,566 639 1,289 1,218 1,486 744 1,326 1,093 1,646 906 1,390
Frequency 606 520 138 1,264 588 654 152 1,394 660 774 140 1,574
Annual Trips 31,039 26,634 7,068 64,742 30,132 33,484 7,785 71,401 33,798 39,651 7,171 80,620
ASM 4,248 6,464 425 11,137 4,551 7,711 544 12,806 4,580 10,118 611 15,309
RPM 3,097 5,181 269 8,546 3,392 6,160 369 9,921 3,445 8,092 436 11,973
L.F. 72.9% 80.2% 63.4% 76.7% 74.5% 79.9% 67.8% 77.5% 75.2% 80.0% 71.4% 78.2%
Seats 3,848,876 4,128,332 664,426 8,641,634 3,736,331 5,189,948 731,831 9,658,111 4,190,956 6,145,975 674,055 11,010,986
Psgrs 2,758,896 3,313,876 434,452 6,507,224 2,737,688 4,116,986 496,175 7,350,848 3,120,575 4,915,678 473,472 8,509,725
Yield 15.14 12.52 19.86 13.70 14.67 12.66 18.54 13.57 14.80 12.51 17.35 13.35
Fare 169.91$ 195.75$ 122.94$ 179.93$ 181.83$ 189.47$ 137.96$ 183.15$ 163.41$ 205.92$ 159.92$ 187.77$
Passenger Rev. $468.8 $648.7 $53.4 $1,170.9 $497.8 $780.0 $68.5 $1,346.3 $509.9 $1,012.3 $75.7 $1,597.9
Other Rev. $37.5 $51.9 $4.3 $93.7 $39.8 $62.4 $5.5 $107.7 $40.8 $81.0 $6.1 $127.8
Total Revenue $506.3 $700.6 $57.7 $1,264.5 $537.6 $842.4 $73.9 $1,454.0 $550.7 $1,093.2 $81.8 $1,725.7
Expenses
Flight Crew $39.0 $44.4 $5.7 $89.0 $41.0 $53.3 $7.1 $101.3 $41.9 $69.1 $7.6 $118.7
Cabin Crew $8.8 $12.8 $0.9 $22.5 $9.2 $15.4 $1.1 $25.8 $9.4 $20.0 $1.2 $30.7
Fuel $235.2 $305.3 $30.0 $570.5 $248.7 $365.6 $37.2 $651.6 $252.6 $478.4 $40.4 $771.4
Maintenance $27.2 $34.0 $3.9 $65.0 $28.1 $41.1 $4.6 $73.8 $29.3 $52.9 $4.8 $87.0
Landing Fee $9.0 $8.8 $1.4 $19.2 $8.8 $11.0 $1.5 $21.3 $9.8 $13.0 $1.4 $24.3
Control & Comm $14.9 $12.8 $3.4 $31.1 $14.5 $16.1 $3.7 $34.3 $16.2 $19.0 $3.4 $38.7
Ground Handling $13.9 $14.2 $2.0 $30.1 $14.3 $17.3 $2.3 $33.9 $14.9 $22.7 $2.5 $40.1
Other $3.6 $3.2 $0.8 $7.5 $3.5 $4.0 $0.8 $8.3 $3.9 $4.7 $0.8 $9.4
Passenger-Related $37.5 $51.9 $4.3 $93.7 $39.8 $62.4 $5.5 $107.7 $40.8 $81.0 $6.1 $127.8
Total DOC $389.0 $487.3 $52.2 $928.5 $407.7 $586.1 $64.0 $1,057.9 $418.9 $760.9 $68.2 $1,247.9
Ownership $73.7 $97.2 $12.4 $183.3 $73.7 $117.5 $12.4 $203.5 $73.7 $145.8 $12.4 $231.9
Total Expense $462.6 $584.5 $64.7 $1,111.8 $481.4 $703.6 $76.4 $1,261.4 $492.5 $906.7 $80.6 $1,479.8
Profit $43.6 $116.0 -$7.0 $152.7 $56.2 $138.8 -$2.5 $192.6 $58.2 $186.5 $1.2 $246.0
Aircraft 22 24 5 51 22 29 5 56 22 36 5 63
20122010 2011
28
“Real World” Airline Study Examples
Future network & fleet scenarios can be evaluated against the “status
quo” to help fully understand the impact of proposed scenarios
Revenue and Profit Growth With Potential Ideal Fleet Mix:
Conservative Growth Scenario
2012 vs. 2008
$57
$306
$34
$51
$39
$84
$120
$690
$0
$100
$200
$300
$400
$500
$600
$700
$800
Revenue Crew Fuel Maintenance Pax-Related Other Ownership Value
2020 vs. 2014
29
“Real World” Airline Study Examples
Optimized fleet phasing accounts for most profitable replacement of
older types while allowing for growth
Potential Ideal Fleet Mix By Year
15 12 11 9 8 8
20
16 17 19 21 22
815 18
2225
29
0
10
20
30
40
50
60
70
2008A 2008I 2009I 2010I 2011I 2012I
B737-800
B737-700
E190
2015 2016 2017 2018 2019 2020
43 43 46
50 54
59
30
“Real World” Airline Study Examples
Network & Fleet solutions must be evaluated based on their net value
over time
0
20
40
60
80
100
120
2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026
Mil
lio
ns
NPV $245M
NPV $343M
NPV $379M
NPV $315M
• NPV Discount Rate: 12%
Op
era
tin
g M
arg
in (
U.S
. D
oll
ars
)
Present Values (PV) Per Network/Fleet Plan Year
31
Typical types of network/fleet studies
Fleet plan optimization analysis
Model ideal fleet mix given marginal traffic & yield
assumptions, new markets, and growth expectations
Hub schedule analysis
Evaluate connectivity of existing schedules
Propose changes to schedule to improve connections/local traffic demand
Market profitability sensitivity
Model with various yield / demand / seasonality sensitivities
Compare fleet types at market / frequency level
New market development
Identify potential markets to evaluate
Evaluate what airplane type is best to open market
What fare tradeoffs
What new markets could be considered / modeled?
Network, fleet value comparison over time
Value alternative fleet scenarios over time with varying spill,
demand, revenue, cost and growth parameters