Overview of Boeing Planning Tools Alex Heiter

Transcription

1 Overview of Boeing Planning Tools Alex Heiter Istanbul Technical University Air Transportation Management M.Sc. Program Network, Fleet and Schedule Strategic Planning Module 16: 31 March 2016

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 2

3 Network & Fleet Planning Decisions Include a Wide Range of Factors Airline Fleet Airline Capacity/ Supply Market & Route Economics Origin Destination Passenger Demand Competition Network Level Point-to-Point Fleet Considerations Economics Route Level Government/ Airport/ Regulatory 3

4 Network & Fleet Planning Goals REVENUE COST PRODUCTIVITY / EFFICIENCY PROFITABILITY 4

5 Areas of Investigation Area Traffic Forecasting Network Planning Scheduling Fleet Planning Shaping the Market Revenue Management Description / Area of Investigation Airline traffic forecasts for passenger demand by fare class Sizing & scoping of OD demand Fare & yield analysis Network scenario profitability modeling Identification & evaluation of potential future markets Evaluation of different airplane configurations & passenger mix Market feasibility studies / network profitability analysis Schedule development and evaluation / hub evaluation Schedule design, best practices, connection analysis, etc. Schedule-based fleet plans Airplane & route optimization Traffic based fleet plans & economics Fleet mix analysis & optimization (spill, revenue, profitability) Boeing & competitor fleet plans / fleet optimization Fleet phasing plans (additions & retirements) Sharing network & fleet planning best practices both internally and externally Apply airline perspective & industry expertise to product development Industry awareness of revenue management systems Airline recommendations on value derived from revenue management systems 5

6 What Kind of Work Does Boeing NFP Do? NETWORK & FLEET PLANNING FOCUS AREAS: 6

7 What Kind of Work Does Boeing NFP Do? NETWORK & FLEET PLANNING FOCUS AREAS: 7

8 What Kind of Work Does Boeing NFP Do? NETWORK & FLEET PLANNING FOCUS AREAS: 8

9 What Kind of Work Does Boeing NFP Do? NETWORK & FLEET PLANNING FOCUS AREAS: 9

10 What Kind of Work Does Boeing NFP Do? NETWORK & FLEET PLANNING FOCUS AREAS: 10

11 Boeing s Network and Fleet Planning Suite TOP DOWN (Macro) Utilizes airline RPK data, regional schedules & growth rates, and airplane characteristics to evaluate profitability models. Top Down Fleet Planning Analysis Sophistication Network Profitability Model (Intermediate) Itinerary-based tool involving markets, frequencies & equipment, fares and costs. SOARS (Advanced) Comprehensive market & schedule data used to evaluate optimized fleet planning scenarios and competitively derived market demand. Network Modeling Schedule Optimization & Airline Revenue System SOARS 11

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 12

13 Schedule Optimization & Airline Revenue System (SOARS) O&D paths by passenger preference Host airline vs. global airline networks Service level probabilities FLEET SCENARIO GMAS Market Allocation System Boeing Developed ODSE Flight Schedule Planner Boeing Developed FOM Fleet Optimization Module Boeing Developed Operationally feasible solutions Implementable recommendations Optimized markets & frequency Schedule changes / editing Comprehensive airline statistics Hub Connectivity Analysis Optimal network aircraft Based on demand, revenue & costs Spill, re-capture, performance, constraints PROFITABLE SOLUTION 13

14 Global Market Allocation System (GMAS) How passengers use airline networks to reach their destinations Network Scenario Global O&D and Fares Constrained Allocation Flight Schedule Planner Passenger Choice Logit Model Path Generation Unconstrained Allocation Network Exploration Schedule Initiatives Global Airline Schedule Competitor Analysis Fleet Optimization Module 14

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 BOS JFK 34% Probability One-stop 8:05 Hrs 14% Probability One-stop 8:05 Hrs 71% Probability Non-stop 6:35 Hrs 3% Probability One-stop 8:55 Hrs 5% Probability One-stop 8:55 Hrs SNN DUB 15

16 Fleet Optimization Module (FOM) Placing the right airplanes on the right routes CPLEX Revenue Sensitivities Cargo Demand & Fares Schedule Editor Network Scenario Maintenance Spill Model Optimizedfor-Profit Solution Airport Limitations Cost Structure Airplane Specs Pax Choice Model Fuel Price Sensitivities Min Turn Sensitivities 16

17 Fleet Optimization Analytical Methodology 17

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 18

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 Passenger/Traffic Profitability/Performance On-time Performance Economic Aircraft Cost Worldwide airline schedules, updated weekly. Origin & Destination, on-board loads, fares/revenue. Actual profitability of flights/markets with detailed passenger breakdown. Actual on-time performance with detail by flight. GDP, historical traffic growth rates, disposable income, fuel prices, etc. Payload/Range, economics, maintenance, fuel burn, etc. Detailed cost breakdowns by category. Innovata, OAG Sabre, PaxIS, Diio, Travelport Internal Internal Various sources (usually governmental) Aircraft Manufacturer Internal/Aircraft Manufacturer 19

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 Airplane Performance Boeing APNav Ownership Rates (Airline Specific) Airline Published Data Validation & Verification Pax Yield & Demand Data Third-party or Airline provided Cargo Yield & Demand Data Third-party or Airline provided Cost Data Airline Specific Rules (When Available) Boeing ICAS Base Traffic/Revenue Forecasted Growth Rates (Boeing CMO or Airline provided) Existing Network / Future Network (SOARS) Baseline and Future Network Profitability 20

21 Key Performance Indicators (KPIs) are important for measuring success Metric Load Factor On Time Performance Asset Utilization Passenger Misconnection Data Market Share Revenue per ASK Cost per ASK Profit and Loss Degree of Difficulty in Calculating Low Low Low Medium Medium Medium High High 21

22 KPIs: Successful airlines focus on maximizing revenue per unit of capacity (RASK) Load Factor = Total passengers flown Total available seats RASK = Total passenger revenue Available seat kilometers Revenue per available seat-kilometer Yield = Total passenger revenue Revenue passenger kilometers Revenue per passenger-kilometer 22

23 Weekly Frequency Real World Airline Study Examples Example airline hub schedule restructure utilizing ODSE and GMAS Adjusted hub schedules and modeled impact of connectivity EXAMPLE EUROPEAN HUB: POTENTIAL RE-STRUCTURE Potential LOT Warsaw Structure Arrivals Departures

24 Change vs. August 09 Real World Airline Study Examples Positive impact of schedule redesign: Incremental transit traffic due to greater connectivity which improving local demand preference 35% Projected Impact of Alternate WAW Schedule Modeled impact of re-structured schedule 31.6% 30% 25% 20% 15% 13.5% 10% 9.8% 5% 5.6% 0% Capacity Local Demand Transit Demand Total Demand 24

25 Annual Gross Profit (excl. fixed costs) US$M 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 $8 Forecast Longhaul Route Profitability Annualized 2015, 4x Weekly Service $6 $4 $2 $- $(2) $(4) $(6) $(8) $(10) 25

26 Annual Cost / Revenue ($M) Real World Airline Study Examples Utilizing SOARS tools and methods, alterative network scenarios can be evaluated at a flight, route or system level Opportunity Atlanta-Los Angeles $18 $16 $2.5 $0.3 Projected current 73G performance $ superior revenue/profit $12 $ lower cost $10 $8 $6 $14.3 $0.4 $1.0 $0.1 $1.8 $0.1 $0.6 $0.2 $0.2 $0.0 $1.0 $4 $0.1 $1.6 $2 $0.2 $3.2 $0 Revenue Traffic Yield Fuel Maintenance Flight Crew Cabin Crew Landing Other Revenue- Related $0.5 Ow nership $0.6 $0.6 Contribution 26

27 Real World Airline Study Examples Detailed financial modeling is required for any number of network and/or fleet analyses Statistics E190 Total E190 Total E190 Total Stage Length 1,104 1, ,289 1,218 1, ,326 1,093 1, ,390 Frequency , , ,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, ,137 4,551 7, ,806 4,580 10, ,309 RPM 3,097 5, ,546 3,392 6, ,921 3,445 8, ,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, ,426 8,641,634 3,736,331 5,189, ,831 9,658,111 4,190,956 6,145, ,055 11,010,986 Psgrs 2,758,896 3,313, ,452 6,507,224 2,737,688 4,116, ,175 7,350,848 3,120,575 4,915, ,472 8,509,725 Yield Fare $ $ $ $ $ $ $ $ $ $ $ $ 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 $ $7.0 $152.7 $56.2 $ $2.5 $192.6 $58.2 $186.5 $1.2 $246.0 Aircraft

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 $800 Revenue and Profit Growth With Potential Ideal Fleet Mix: Conservative Growth Scenario vs $700 $57 $600 $500 $306 $400 $300 $200 $690 $34 $51 $39 $84 $100 $0 Revenue Crew Fuel Maintenance Pax-Related Other Ownership Value $120 28

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 B B E A I I 2010I I I

30 Operating Margin (U.S. Dollars) Millions Real World Airline Study Examples Network & Fleet solutions must be evaluated based on their net value over time 120 NPV $379M 100 NPV $315M 80 NPV $343M 60 NPV $245M NPV Discount Rate: 12% Present Values (PV) Per Network/Fleet Plan Year 30

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 31