Overview of Boeing Planning Tools Alex Heiteraviation.itu.edu.tr/img/aviation/datafiles/Lecture...

Overview of Boeing Planning ToolsAlex Heiter

Network, Fleet and Schedule

Strategic Planning

Module 16: 31 March 2016

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



8



9



10



11

Boeing’s Network and Fleet Planning Suite

TopDownFleet

Planning

NetworkModeling

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

GMASMarket Allocation

SystemBoeing Developed

FOMFleet Optimization

ModuleBoeing Developed

ODSEFlight Schedule

PlannerBoeing 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

FaresNetwork

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 DataAirline Specific Rules (When Available)

Boeing ICAS

Airplane Performance

Boeing APNav

Ownership Rates

(Airline Specific)

Base Traffic/RevenueForecasted 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

MetricDegree 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


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


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


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


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


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


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 4346

5054

59

30


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

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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

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