cost estimating methodology for very small...
TRANSCRIPT
© The Aerospace Corporation 2011
Cost Estimating Methodology for Very
Small Satellites
Dr. Mary Boghosian
&
Dr. Ricardo Valerdi
The Aerospace Corporation Civil & Commercial Program Office, Planetary & Robotic Missions Directorate, Economic & Market Analysis Center COCOMO Forum, USC, November 2, 2011
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Background and Motivation
• Since September 2009 The Aerospace Corporation (Aerospace)
has been engaged in developing a methodology for estimating the
cost of “Very Small Satellites” those with masses <<50kg, primary
focus are satellites with10kg and below- the picosatellites
(picosats), including CubeSats
• Production rates of Very Small Satellites are increasing, yet NO COST
ESTIMATING METHODOLOGY exists
• Current existing small satellites cost models are NOT applicable to
picosats (CubeSats); they require knowledge of physical and technical
parameters, not easy to obtain
• Existing cost models databases contain satellites with 1-2 order of
magnitude large masses, when applied to picosats or CubeSats results
are skewed and cost fidelity not accepted
Dr. Mary Boghosian
AeroCube-2 Explorer-1 RAX-1
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Observations
• The very small satellite cost information not available to the public
• System unit cost is known to the developing team lead only, and is
based on funding availability
• System architecture and requirements are changing throughout the
development period
• Cost of I&T, a major contributor to system’s cost, is not known
• Purchased items used, COTS parts, their costs not always known
• Software cost frequently makes up considerable portion of the system
cost, yet, it is not known
• The development team consists mainly of students in small or large
groups, which are continuously changing
• Aerospace’s SSCM and COBRA cost models are dependent on
systems/ subsystems mass and power as major cost parameters. And
applicable to Large Satellites (>>50Kg)
Dr. Mary Boghosian
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Challenges
• Goal:
• Develop a reliable cost estimation method for Very Small Satellites
Picosatellites (<10 kg) and CubeSats (<1 kg) during development
• Issues:
• What cost model to use? NO EXISTING MODEL
• What parameters to use?
• Are mass and power considered as cost parameters?
• Does cost model depend on I&T, team activities, packaging.. etc?
• How to incorporate cost of COTS, software, … etc?
• How does cost improve project management practices?
• Solution:
A-PICOMO- Aerospace PIcosatellites COst MOdel
Dr. Mary Boghosian
A New Cost Methodology is Needed
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Dr. Mary Boghosian
A new cost methodology to account
for all unique challenges and
requirements of the very small
satellites
A cost model to estimate the cost
during Development ,and Launch
and Operation
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Aerospace PIcosatellite Cost Model A-PICOMO Characteristics
• It is a parametric cost model driven by findings from the small satellite
community.
• Developed in coordination and input from subject matter experts
• Assumes the satellite development follows standards of systems
engineering activities, such as team activities, system integration and
tests, procurements, therefore, can be validated through enquiries and
hypotheses of the cost drivers and their adaptability and management
• Estimates the satellite cost using some exclusively derived size and
cost drivers from measurable systems engineering activities specific to
the very small satellites, such as mission requirements, use of software
and hardware modules, COTS, I&T, team dynamics and the project
lifecycle phase, …etc
• It can be applied at any stage of project lifecycle (so far it is being
applied for “Development” period- before launch)
Dr. Mary Boghosian
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Cost Model Development Methodology A-PICOMO development process follows 8-step COSYSMO methodology
Dr. Mary Boghosian
Determine Model
Needs
Step 1
Analyze existing
literature
Step 2
Perform Behavioral
analyses
Step 3 Define relative
significance, data,
ratings
Step 4
Perform expert-
judgment Delphi
assessment, formulate
a priori model
Step 5
Gather project data
Step 6 Determine
Bayesian A-
Posteriori model
Step 7 Gather more data;
refine model
Step 8
1) Identify capability gap
2) Evaluate existing
methodologies, conduct market
analysis, collect available data
3) Determine key project drivers
4) Quantify impact of project
drivers
5) Perform Delphi Survey
6) Obtain data for calibration and
validation
7) Assess predictive accuracy
8) Review results, iterate for
possible improvements
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Operational Concept
Dr. Mary Boghosian
A-PICOMO (Aerospace PIcosatellite COst MOdel)
A-PICOMO
Size
Drivers
Effort
Multipliers
Cost
Calibration
# Features
# Verification tests
# SLOC
- Multisite coordination
- Team understanding
- TRL
- …
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Description of Size and Cost drivers A-PICOMO- cost Parameters
Dr. Mary Boghosian
• COST DRIVERS
• Factors such as organization, team/personal , and project specific
drivers; I&T, Software use, COTs cost, documentation delivered and
required, project schedule, TRLs … etc
• 5 organization factors, 9 personnel factors, 10 project specific factors
identified
• Factors applicable to the satellite “Bus” and “Payload” were separated
• SIZE DRIVERS
• Measure of System “Complexity” in (hours required to complete the
project). Separated for satellite bus and payload
• 5 size drivers (including software) were identified
• “Number of Software Processor and Module” is treated as size driver,
for satellite bus and payload. The “Relative Software Size” was
measured in line of code (LOC) (1000>LOC>10,000)
• “Number of New and Reused Required Features” were considered
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Description of Size and Cost drivers A-PICOMO- Data Sources and Characteristics
Dr. Mary Boghosian
• SOURCES (examples) – Experts from universities (Cal Poly, Morehead State Univ., Univ. of
Michigan, Univ. of Colorado, Missouri S&T, UC Berkeley, etc.), The Aerospace Corporation, NASA- AMES & GSFC, Air Force, and NRL
• The predicted variable (dependent or Y variable) is the “Actual Cost”. The predictor variables (independent or X variables) are data for all Size Drivers, Cost Drivers, and other project drivers
• Complexity ratings assigned for both Size and Cost drivers
• Size Drivers’ complexity ratings are 3-level, represented as “Easy”, “Nominal,” and “Difficult”.
• Cost Drivers’ complexity ratings are 5 levels, spanned from “Extremely Low” to “Extremely High” translating to either a cost penalty or a cost saving depending on the driver’s effectiveness
• Actual Masses of the satellites range between (0.9 – 35) kg, and Actual Cost of satellites $40K to $40M
• Statistical analysis performed on missions with complete Size and Cost Drivers information
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Characteristics of Data Collected A-PICOMO- Data Type and Distribution
• More than 30 data points (missions) collected so far, only 28 used in model development
• Missions in all project lifecycle phases are considered; 57% Ph C/D, 43% in Phases Pre-A, A, B, E & F
• Earth Observing (EO) and Other types of Functions (i.e. wind measurement, GPS tracking) make 50% of the data
• 78% of the data have no propulsion system
• (60-70)% of the missions use new software processor in the satellite bus and payload
• 87% of the missions use extended team to help the mission’s core team. Extended Team Members involvement in the project with<6 people makes up 81% of the data point. Nominal case is (6-10) people
Dr. Mary Boghosian
0 2 4 6 8
10 12 14
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Characteristics of Data Collected A-PICOMO- Data Type and Distribution
Dr. Mary Boghosian
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Description of Size and Cost Drivers A-PICOMO- Data Statistics- Correlation Data
• Data Correlation • Strong correlation exists between “Actual
Cost” and the “Mass” and the “Cost of Purchased Items”
• Strong Correlation exists between “Actual Cost”, “Mass”, and “Cost of Purchased Items” for those missions in Ph C/D
• Strong correlation exists between Size Drivers and Cost Drivers
• Linear relationship exists between the “Actual Cost” ($M) and both “Mass” and “Actual Cost of Purchased Parts” when the leading variable (15-35)kg is included in the analysis
• Logarithmic relationship exists between the “Actual Cost” ($M) and both “Mass” and “Actual Cost of Purchased Parts” when the leading variable (15-35)Kg is not included
• Correlation decreases when the leading variable (15-35)kg is not included in the analysis
Dr. Mary Boghosian
Actual Mass
(Kg)
Purch Comp Actual Cost
($K)
Actual Cost ($K)
Actual Mass (Kg) 1.000 Purch Comp Actual Cost ($K) 0.973 1.000 Actual Cost ($K) 0.952 0.906 1.000
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Summary Plots of Cost Analysis A-PICOMO- Preliminary Regression Results- Scatter Plots
Dr. Mary Boghosian
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Summary Plots of Cost Residuals A-PICOMO- Regression Results- Residuals
Dr. Mary Boghosian
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Description of some Size and Cost drivers A-PICOMO- Application Thus far
• Application
• Two outliers exist in this data points
• One data point used as a leading variable
• The model tested against a proposed mission in pre-phase A for 8 spacecrafts each of mass 13.6 kg. Each spacecraft has one payload of mass 2.1 kg (making spacecraft and payload total mass as 15.7 kg
• The estimated development cost for the 8 spacecraft is $41M, while A-PICOMO estimated cost is $38.4M.
• Results are shown aside
Dr. Mary Boghosian
Mass Customer
Estimate ($K)
A-PICOMO
Estimate ($K)
S/C (Bus &
Payload) 15.7 kg $ 5,206.75 $ 4,800.00
Total Cost (8 S/C) $ 41,654.00 $ 38,400.00
Cost of Purchased
Parts/ S/C $ 2.29 $ 2.00
Cost of Purchased
Parts (8 S/C) $ 18.30 $ 16.00
• NEXT STEP
• Continue historical data collection
• Refine cost estimating relationship
• Elicit expert input on cost model
structure and parameter used
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Acknowledgement
• Dr. Jared Fortune- Sr. Member of Technical Staff
• Mr. Joseph Pope- Sr. Project Engineer, NASA/CCO/P&RM Dir.
• Aerospace’s “Mechanics Research Dept” and “Micro/Nano
Research Dept”
This work has been made possible by the Support of Aerospace’s
Independent Research and Development Program
Aerospace has been in the business of developing small satellites cost
models for the last twenty years. Its SSCM, COBRA, Activity Based
(Bottom-Up) models and others are widely used by the community.
A-PICOMO (Aerospace Picosatellites COst MOdel) is an added
value, targeted to mainly the Very Small Satellites, including
CubeSats
Dr. Mary Boghosian
© The Aerospace Corporation 2011
Thank you