agent-based economic modeling of commercial … · assumptions and description of case studies...

SpaceWorks Engineering, Inc. (SEI)www.sei.aero

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AGENT-BASED ECONOMIC MODELING OF COMMERCIAL TRANSPORTATION SERVICES TO THE INTERNATIONAL SPACE STATION

AIAA Space 2006 Conference21 September 2006San Jose, CA

Mr. Dominic DePasqualeSystems EngineerSpaceWorks Engineering, Inc. (SEI)[email protected]+770.379.8009

Mr. A.C. CharaniaSenior FuturistSpaceWorks Engineering, Inc. (SEI)[email protected]+770.379.8006

Contents

IntroductionNodal Economic Space Commerce (NESC) ModelCommercial Transportation Services to the International

Space Station (ISS) Case StudiesResults of Case Studies


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3

Introduction


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About SpaceWorks Engineering, Inc. (SEI)

Overview:- Engineering services firm based in Atlanta (small business concern)- Founded in 2000 as a spin-off from the Georgia Institute of Technology- Averaged 130% growth in revenue each year since 2001 - 85% of SEI staff members hold degrees in engineering or science

Core Competencies:- Advanced Concept Synthesis for launch and in-space transportation systems- Financial engineering analysis for next-generation aerospace applications and markets- Technology impact analysis and quantitative technology portfolio optimization


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Practice AreasSpace Systems Analysis | What is the System?Conceptual Level Engineering AnalysisConceptual Level Engineering DesignLife Cycle AssessmentCost EngineeringAdvanced / Robust Design Processes

Technology Prioritization | What are the Implications? Technology AnticipationTechnology Benefit AssessmentsTechnology Prioritization

Financial Engineering | Is the Project Viable?Business DesignFuture Venture Due DiligenceReal Options Analysis

Future Market Assessment | What is Next?Scenario PlanningMarket ForecastingMarket Analysis

Policy and Media Consultation | How to Express the Vision?Government InitiativesPolicy ConsultationTelevision, Film, Radio, Internet Presence


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Sample Performance Analyses

Note:Data generated through SENTRY model and exported to data visualization program for eventual display

Sample Thermal Analysis:Maximum RLV Orbiter Entry Surface Temperature (via SENTRY)

Sample Thermal Analysis:Maximum RLV Booster Entry Surface Temperature and TPS Tile Thickness (via SENTRY)

TOP VIEW UNDERSIDE VIEW

Sample Trajectory Analysis (via POST):

0.00.51.01.52.02.53.03.54.04.5

0 100 200 300 400 500 600Time (s)

Mlb

Thrust

Weight

Thrust and Weight vs. Flight Time

050

100150200250300350400450

0 100 200 300 400 500 600Time (s)

Altit

ude (

thou

sand

s of f

t)

Altitude vs. Flight Time

Optimized transitionto SSME-only

Relative Velocity and Mach vs. Flight Time

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ive V

elocit

y (ft/

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ber

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Relative Velocity

Mach

“Net” ISP (SSME and RD-180) vs. Flight Time

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0 100 200 300 400 500 600

Time (s)

ISP

(s)

SSME

RD-180“Net” Value


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Sample Economic Analyses

Human Exploration Cost Estimates Scenarios of Reusable Launch Vehicle (RLV) Price Sensitivity

500

1,500

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4,500

25% 50% 75%Turn-Around-Time Reduction

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e Pe

r Pou

nd P

aylo

ad [$

/lb]

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ht R

ate

[Flig

hts

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Yea

r]

Price Per Flight [$/lb]

Flight Rate [Flights/Year]

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4,500


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/lb]

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10,000


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nd P

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/lb]

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Oper

atio

ns C

ost R

educ

tion

DDT&E AND TFU COST REDUCTION25% 75%

25%

75%

Components of LCC (FY06)

Other (Robotic/ISS/Shuttle)

CEV/CM

CLV

LSAM

CaLV-HLLV

EDS + CEV/SM

Technology Maturation Surface Systems

Facilities, Operations, and Flight Tests

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Year

$M

$111.3 B (2006-2018) $53.4 B (2019-2025)$164.7 B

NASA FY06 Exploration-Related Budget

See: http://www.sei.aero/library/technical.html for more information and technical papers on above analyses

Space Tourism Economic Modeling International Space Station (ISS) Support Market

-100M

-50M

0M

50M

100M

0 2 4 6 8 10 12

Disc

ount

ed C

umul

ative

Ca

sh F

low

(US

$)

Project Year

Effect of Competition

Higher-End Operator

In Competition with Higher-End

Lower-End Operator

Effect of Market Entry Date

0 2 4 6 8 10 12Project Year

-40M-20M

0M20M40M60M80M

-60M-80M 2 Year Market Delay

4 Year Market Delay

Higher-End Operator

Lower-End Operator

5 Commercial Competitors + min. 2 CEV/Yr + Russian Competition


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Nodal Economic Space Commerce (NESC) Model


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Nodal Economic Space Commerce (NESC) Model

The Nodal Economic Space Commerce (NESC) model is a dynamic, agent-based space market simulation and financial engineering tool

Agent-Based Modeling (ABM): allows heterogeneous agents with varied and dynamic behavior

- Simulation can represent plants and animals in ecosystems, vehicles in traffic, people in crowds, or autonomous characters in animation and games

- Qualities: emergent phenomena, natural description of system, flexible

Modeling space capitalism in NESC- Competition between firms, includes current and future competitors- Entrance of new competitors within existing and new markets - Explore variations in customer preferences- Current markets: sub-orbital space tourism and ISS support- Use available data for demand and to estimate supply

Companies compete for customers with the goal of maximizing revenues

- Each company autonomously decides its pricing strategy given its unique capacity, costs, and vehicle characteristics

- Model outputs financial health of each company- Capable of capturing differences in products/services

Built Upon “REcursive Porous Agent Simulation Toolkit”RePast Sources: http://www.duncanrobertson.com/research/simulation.htm, http://sourceforge.net/projects/repast/, http://complexityworkshop.com/cw/tutorial/RePast/index.html


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Users of Agent-Based Modeling (ABM)

P&G to understand the impact of alternative shipment and payment terms on retail in stock positions and company inventory

Macy’s to generate new store layout options for maximizing customer satisfaction and spending

Hewlett Packard to understand the effect on organizational performance by a change in hiring practices

NASDAQ to understand what happen when the “tick size” changes from 1/8 of a dollar to 1/100 of a dollar

U.S. Department of Defense to conduct war games predicting battlefield outcomes in a networked information environment

Pricewaterhouse Coopers to predict CD sales in the Japanese pop (J-Pop) market using 75,000 agents (correlation coefficients of actual and predicted sales between 0.8 and 0.9)

Electronic Arts (EA) and their “The SIMS” game

Sources: “Agent-based Modeling: A valuable new weapon for Chief Marketing Officers in the fight of their lives,” EMM Group, Inc., 2004, http://www.red3d.com/cwr/ibm.html


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Commercial Transportation Services to International Space Station (ISS) Case Studies


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NASA Commercial Orbital Transportation Services (COTS) Program

CapabilitiesA: External cargo delivery and disposalB: Internal cargo delivery and disposal

Phase I (2006 – 2010)Development and demonstration of International Space Station supply services by private industry in coordination with NASAFixed-price, pay-on-demonstration milestones

Phase II (Post 2010)Potential competitive procurement of orbital transportation servicesFlexible services contracts for delivery orders

C: Internal cargo delivery and returnD: Crew transportation

Award: $278 MillionAward: $207 MillionSource: NASA, http://www.nasa.gov/mission_pages/exploration/news/COTS_selection.html

Management: NASA Commercial Crew and Cargo Program Office

Contracting Mechanism: Space Act Agreements

Award Date: Aug 18, 2006


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Nodal Economic Space Commerce (NESC) Model: ISS Support Market

Crew and cargo demands Contributions

Revised crew and cargo demands Contract type

Deliveries

Awards & Payments

Space Station International Partners

Commercial Companies

Price Setting

Bids

Deliveries

Government

Bids

Can companies be financially successful in this market?

Two companies (Firm A and Firm B) compete to deliver crew and cargo to the ISS from 2011 to 2017.

NESC Model ISS Services Market Implementation in the Desktop PC Environment

Flow of Major Events in NESC Model ISS Services Market Implementation


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Assumptions and Description of Case Studies

Modeling AssumptionsISS Configuration after 15 additional STS flights6 Crew rotated every 6 monthsMarket period from 2011 to 2017

Crew Exploration Vehicle (Orion) crew and pressurized cargo variants are available after 2012

Case StudiesCase Study 1: Deterministic, No Vehicle FailuresCase Study 2: Probabilistic, With Vehicle Failures

ISS Picture Source: NASA, http://www.nasa.gov/mission_pages/station/structure/ISS_Assembly_Artwork.html

ATV Picture Source: ESA, http://esamultimedia.esa.int/images/ISS/ATV_to_ISS.jpg

Soyuz Picture Source: http://astro.ysc.go.jp/soyuz-tma-1_2.jpg

Russian Soyuz and Progress are available but do not compete with COTS companies

ESA and JAXA provide one ATV and one HTV flight per year “free” to the US Government / NASA in accord with international agreements


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Vehicles Operated by Firms in NESC

COTS FIRM A COTS FIRM B Characteristic Crew

Vehicle Unpress’d

Cargo Vehicle Press’d

Cargo Vehicle Crew

Vehicle Unpress’d

Cargo Vehicle Press’d

Cargo Vehicle DDT&E Start Year 2007 2007 2007 2007 2007 2007 IOC Year 2011 2011 2011 2011 2011 2011 Crew Capacity 7 0 0 6 0 0 Reusable (1=Yes, 0=No) 1 0 1 1 1 1

Spec

s Time b/t Flights (months) 2 2 2 2 2 2 Pressurized (kg) 300 0 4000 50 0 1500 Unpressurized (kg) 0 7000 0 0 2000 0 Water (kg) 0 400 300 0 200 100 Gas (kg) 0 100 50 0 50 20 Propellant (kg) 0 0 0 0 0 0 Total Cargo (kg) 300 7000 4000 50 2000 1500

Capa

city

Return Mass (kg) 300 0 2000 50 1500 700 Development Cost ($M) 200 145 25 250 50 90 Production Cost ($M) 75 20 65 75 8 10 Launch Vehicle Cost ($M) 51 51 51 20 20 20 Certification Cost ($M) 1 1 1 1 1 1 ISS Configuration Cost ($M) 5 1 1 5 1 1 Maintenance Cost ($M) 5 1 5 8 6 8 Failure Cost ($M) 8 6 8 8 6 8 Learning Curve Percent (%) 90% 90% 90% 85% 85% 85%

Vehi

cle C

osts

Cost Skewing Profile 5 5 5 5 5 5 Fixed Operating Cost ($M) 5 5 3 8 3 3 Cost Per Flight ($M) 6 4 4 8 4 5 Launch Site Fee ($M) 2 2 2 2 2 2 Op

s Cos

t

Insurance ($M) 2 2 2 4 4 4 Historical Flights 2 2 2 2 2 2 Maximum Flights (if reusable) 5 0 5 30 30 30 Design Reliability 0.97 0.97 0.97 0.95 0.95 0.95 Re

liabi

lity

Down Time if Failure (months) 12 6 6 12 6 6 NOTE: Public data has been used as a reference in defining firms that are reasonable proxies of those that may

participate in this market, but the shown values were not provided to SEI by any specific companies.


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Modeling Assumptions for Vehicle Cost and Capability Inputs

Vehicles are available for purchase by the government agent in the year of their Initial Operating Capability (IOC)The government agent within NESC schedules all flights and ensures that the time between flights for each vehicle is not violatedCargo capacities shown represent the mass of cargo that each vehicle can deliver to ISS orbit (400 km altitude, 51.6 degree inclination) after taking into account structure of the vehicle, but not including any necessary tare (packaging) mass

- Average tare mass factors per cargo category are applied within the NESC model

Development costs are incurred for all vehicles, regardless of whether they are subsequently selected by the government agent or notThe production cost of each vehicle is incurred when selected and awarded a contract by the government agent

- Expendable vehicles are produced each time they are flown- Reusable vehicles are produced and can be flown until a failure occurs or they

have flown the input maximum number of flightsLaunch vehicle costs are independent of the crew and cargo vehicle costs and are incurred at every flightFailure cost is incurred by the company agent when a vehicle failure occurs and represents all costs to investigate the failure and make engineering changes


17SpaceWorks Engineering, Inc. (SEI)

www.sei.aero

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Results of Commercial Transportation Services to ISS Case Studies


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-200

-150

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-50

0

50

100

150

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Year

Pres

ent V

alue

($M

, FY0

6)A

t 22.

6% D

isco

unt R

ate

COTS Firm A

COTS Firm B

Case Study 1: Deterministic with No Failures (2011-2017)

Model Conditions: No vehicle failures (w/ CEV, w/ ESA + JAXA IP, w/o Russian Competing), 15 STS flights ISS end-state, with $485M of NASA COTS funding

11 flights / yrAverage Flights Per Year65 flightsTotal Commercial Flights

0 failures / yrOverall Failure Rate$16.72 MAvg. Price per Passenger$1,403 MTotal Crew Revenue

84 passengersTotal Crew Delivered$33,947 /kgAvg. Price per Kg

$4,913 MTotal Cargo Revenue144,721 kgTotal Cargo Delivered

14 flightsTotal IP Flights

No Failures Case Study

Both Firm A and Firm B are financially successful if no vehicle failures occur

Firm A NPV: $118 Million

Firm B NPV: $28 Million


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Case Study 1: Deterministic with No Failures

ATV and HTV together deliver 38% of total upmassBoth Firm A and Firm B deliver some percentage in each cargo category, however, Firm A delivers a greater percentage than Firm B

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Pressurized Unpressurized Total Upmass Return

Perc

ent o

f Car

go D

eliv

ered

IPFirm BFirm ACEV


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0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

10 20 30 40 50 60 70

Price per Kilogram to ISS ($K)

Prob

abili

ty D

ensi

ty

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1

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ulat

ive

Prob

abili

ty

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12 14 16 18 20 22

Price per Passenger to ISS ($M)

Prob

abili

ty D

ensi

ty

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1

Cum

ulat

ive

Prob

abili

ty

Case Study 2: Probabilistic with Failures (2011-2017)

Model Conditions: With vehicle failures (w/ CEV, w/ ESA + JAXA IP, w/o Russian Competing), 15 STS flights ISS end-state, 1000 Monte Carlo simulations, with $485M of NASA COTS funding distributed for development offsets

Mean Price: $37.1 M / passenger

0.42 failures / yrAverage COTS Failures0.24 failures / yrAverage IP Failures

11.2 flights / yrAverage Flights Per Year69.6 flightsTotal Commercial Flights

0.68 failures / yrOverall Failure Rate

$17.35 MAvg. Price per Passenger$1,435 MTotal Crew Revenue

82.6 passengersTotal Crew Delivered$40,681 /kgAvg. Price per Kg

$5,758 MTotal Cargo Revenue142,450 kgTotal Cargo Delivered11.6 flightsTotal IP Flights

With Failures Case Study (Mean Values)

Prices and total revenues increase when failures are considered


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0

0.001

0.002

0.003

0.004

0.005

0.006

-50 50 150 250 350Net Present Value of Firm A (FY06 $M)

Prob

abili

ty D

ensi

ty

0

0.2

0.4

0.6

0.8

1

Cum

ulat

ive

Prob

abili

ty

Case Study 2: Probabilistic with Failures (2011-2017)

Firm B Mean NPV: - $6.8 MFirm A Mean NPV: $146 M

0

0.002

0.004

0.006

0.008

0.01

0.012

-110 -60 -10 40 90 140Net Present Value of Firm B (FY06 $M)

Prob

abili

ty D

ensi

ty

0

0.2

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1

Cum

ulat

ive

Prob

abili

ty

Only a single firm has mean NPV greater than zero when failures are considered

Model Conditions: With vehicle failures (w/ CEV, w/ ESA + JAXA IP, w/o Russian Competing), 15 STS flights ISS end-state, 1000 Monte Carlo simulations, with $485M of NASA COTS funding distributed for development offsets


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Total Cost to Support ISS Using Commercial Transportation (2011-2017)

$3,945 M

$2,784 M

$428 M

$29 M

$7,185 M

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

Firm A

Firm B

CEV

IP

Total

Revenues / Cost to Government (FY06 $M)

Model Conditions: With vehicle failures (w/ CEV, w/ ESA + JAXA IP, w/o Russian Competing), 15 STS flights ISS end-state, 1000 Monte Carlo simulations, with $485M of NASA COTS funding distributed for development offsets, error bars represent one standard deviation


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Potential for Savings to Government

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Baseline: CEV + IP,Deterministic (No Failures)

Case 1: 2 COTS + IP, Deterministic (No Failures)

Case 2: 2 COTS + IP,Probabilistic (Mean and ±

1 Std Dev)Suppliers

Cos

t to

Supp

ly fr

om 2

011-

2017

($M

, FY0

6)

Use of COTS providers can potentially save the Government more than $4.5 Billion


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ISS Commercial Transportation Services Market Conclusions

Agent-based modeling using NESC allows for a more realistic and dynamic simulation of traditional and emerging space markets

The two firms modeled in this effort can both be financially successful in the ISS crew and cargo services when vehicle failures do not occur

When vehicle failures are considered probabilistically, only a single firm of the two considered is financially successful in terms of mean NPV

- There is still some probability that the second company achieves a positive NPV- COTS companies should anticipate failure over the life of the program given the new products being

offered and flight rates required

The occurrence of failures increases the price charged to the Government and results in higher revenues for the participating companies

Use of commercial providers for resupply is potential win-win scenario for US Govt. and emerging commercial companies

- NASA savings might approach several billion dollars over seven year period (compared to using government CEV), commercial providers gain an entry market at an attractive price (possibly multi-billion dollar market is enabled)

- There is high reliance on ATV and Russian support for propellant re-supply, current study did not include any commercial company capable of providing such a service (could be a needed capability not addressed in the specific market segments requested by the COTS solicitation)


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www.sei.aero

Business Address:SpaceWorks Engineering, Inc. (SEI)1200 Ashwood ParkwaySuite 506Atlanta, GA 30338 U.S.A.

Phone: 770-379-8000Fax: 770-379-8001

Internet:WWW: www.sei.aeroE-mail: [email protected]

agent-based economic modeling of commercial … · assumptions and description of case studies...

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