NAFCOM Probabilistic Cost Analysis Module presented by Sharon Winn NASA Cost Symposium March 10, 200 - PowerPoint PPT Presentation

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NAFCOM Probabilistic Cost Analysis Module presented by Sharon Winn NASA Cost Symposium March 10, 200

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Title: NAFCOM Probabilistic Cost Analysis Module presented by Sharon Winn NASA Cost Symposium March 10, 200


1
NAFCOMProbabilistic Cost Analysis
Modulepresented bySharon WinnNASA Cost
Symposium March 10, 2004
2
NAFCOM Description
  • NAFCOM is a parametric estimating tool for space
    hardware.
  • It is intended to be used in the very early
    phases of a development project.
  • NAFCOM can be used at the subsystem or component
    levels and estimates development and production
    costs.
  • NAFCOM is applicable to various types of missions
    (manned spacecraft, unmanned spacecraft, and
    launch vehicles).
  • There are two versions of the model a government
    version that is restricted and a contractor
    releasable version.

3
NAFCOM Evolution
  • Since 1990, eight versions of NAFCOM have been
    developed and distributed across NASA and other
    government agencies.

NASCOM Books
NASCOM Automated DB
NASCOM Ver. 3.0
NASCOM Ver. 4.0
NAFCOM Ver. 2002
NAFCOM Ver. 2004
NAFCOM96
NAFCOM99
1990
1992
1994
1996
1998
2000
2002
2004
  • NASCOM database in hardcopy
  • Estimators hand-entered data into spreadsheets
  • Database contained 65 data points
  • Allowed online searching copying of data
  • Cost estimates developed in spreadsheets with
    CERs created by individuals
  • Database contained 70 data points
  • Fully functional cost model with user defined WBS
    and data access
  • CERs built within NASCOM using 1st Pound method
  • Database contained 91 data points
  • Combined NASA and Air Force data
  • Enhanced search and filtering of data
  • Standardized WBS elements created
  • Database contained 102 data points
  • First non-weight based CERs for five subsystems
    (Complexity Generators)
  • Gov and contractor versions distributed
  • Database contained 114 data points
  • Total re-write of all NAFCOM program code
  • Complexity Generators for all subsystems
  • Major user interface improvements
  • Database contains 122 data points
  • Complexity Generators reworked
  • Fully integrated version of SOCM
  • Probabilistic Cost Risk Analysis module

4
NAFCOM Probabilistic Cost AnalysisRationale
  • NAFCOM provided a point estimate as opposed to a
    probabilistic range estimate.
  • The addition of a probabilistic cost risk
    analysis module
  • provides the ability to identify, analyze, and
    quantify risk/uncertainty
  • Expands NAFCOM from a static analysis tool to one
    that provides probabilistic analysis of uncertain
    values

5
NAFCOM Probabilistic Cost AnalysisDevelopment
  • SAIC was tasked with developing a Probabilistic
    Cost Analysis capability for NAFCOM.
  • Experts in the risk field, including
    representatives from MCR, Aerospace, NASA, IPAO,
    and Mitre participated in the methodology
    development.
  • A meeting was held at NASA HQ to discuss issues
    including sampling approaches, incorporation of
    correlation and model implementation.
  • Dr. Steve Book has been working directly with
    SAIC and NASA to ensure that the best possible
    approaches are considered for integration into
    NAFCOM and to consult on the methodology
    implementation.

6
NAFCOM Probabilistic Cost AnalysisModel Selection
  • Choice between Analytic approach (FRISK) or Monte
    Carlo-based sampling approach
  • Analytic Approach
  • Pros Simple for summing WBS elements,
    instantaneous results, simple development
  • Cons Difficult to apply risk to algebras beyond
    summation (i.e., inflation, phasing)
  • Monte Carlo Approach
  • Pros Allows probability analysis on different
    algebras
  • Cons Difficult to develop or interface w/COTS,
    time intensive to run (I.e., 5,000 to 10,000
    samples)
  • Decision Use Analytic Approach

7
NAFCOM Probabilistic Cost AnalysisImplementation
At Estimating Level
B
Best-Estimate Cost
0
SEE
Distribution of Estimating Error
L
H
B
Cost Implications of Complexity Drivers
(Technical, Management and New Design)
8
NAFCOM Probabilistic Cost AnalysisImplementation
At Summation Levels
WBS Element 1

Total Cost
  • Approximate by lognormal distribution
  • Compute analytically
  • Mean
  • Mode (Most likely)
  • Confidence percentiles

WBS Element 2

.
.
.
9
NAFCOM Probabilistic Cost AnalysisModel Interface
  • Users can elect to run NAFCOM in Risk On or Risk
    Off mode via toolbar

10
NAFCOM Probabilistic Cost AnalysisModel Interface
  • NAFCOMs Risk On Mode provides capability to
    define triangular distributions for all cost
    driver inputs and for cost thruputs
  • These distributions are combined with CER errors
    to produce distributions for nonrecurring and
    recurring cost for each subsystem

11
NAFCOM Probabilistic Cost AnalysisModel Interface
  • The distributions for each subsystem are then
    aggregated, taking correlation and System
    Integration into account
  • Both intra- and inter-subsystem correlation is
    taken into account
  • Users can elect to modify the default correlation
    matrix
  • The default correlation matrix was developed
    based on Dr. Steve Books recommendation of 0.2
    correlation across all elements (knee of the
    curve)

12
NAFCOM Probabilistic Cost AnalysisModel Interface
  • The final result is uncertainty distributions for
    DDTE, Flight Unit, Production and Total Cost.
  • Result data includes summary statistics,
    probability densities, and cumulative
    distributions for each major estimating element
    (i.e. stage, bus)

13
NAFCOM Probabilistic Cost AnalysisSummary
  • Uses well-documented analytical method to
    calculate risk
  • User can input low, most likely, and high values
    for all NAFCOM complexity generator and
    conventional CER inputs
  • Incorporates both technical and estimating
    uncertainties
  • Incorporates correlation between subsystem costs
  • Results displayed to the user are summary
    statistics, probability densities, and cumulative
    distributions for DDTE, Flight Unit, Production,
    and Total Costs for each major estimating element
    (i.e. stage, bus, etc.)
  • User can select either the Normal distribution or
    the Lognormal distribution to approximate the
    final results

14
NAFCOM Probabilistic Cost AnalysisCurrent Status
  • Model is in final test and debug phase
  • A beta version of the model was delivered to MSFC
    last week
  • Roll out of model to cost estimating community is
    planned for early April
  • NAFCOM can be downloaded from the NAFCOM home
    page at http//nafcom.saic.com
  • winns_at_saic.com
  • 256-971-7275 256-705-8539

15
NAFCOM Probabilistic Cost AnalysisPlanned
Enhancements
  • Initial delivery of NAFCOM probabilistic cost
    analysis module does not address engine CERs, the
    allocation of risk dollars, or an assessment of
    the accuracy of the sampling method used.
  • The the cost risk analysis capability will be
    expanded to include all engine CERs including
    liquid, combined cycle, and air breathing
    estimating algorithms allowing the user to
    perform risk analysis on all current NAFCOM CER
    types.
  • The ability to determine which elements have the
    most cost risk associated with them and to
    allocate risk dollars back to those WBS elements
    will be added.
  • User will be allowed to select a point estimate
    (most likely,mean, median, mode) and a level of
    confidence above the point estimate to determine
    risk dollars.
  • There is no unique optimal way of allocating
    the risk dollars, but NAFCOM will implement a
    technique acceptable to the NASA estimating
    community that takes into account inter-element
    correlations and other features of the estimate.
  • The accuracy of the analytical versus Monte Carlo
    approaches to sampling will be studied to
    determine the most cost effective capability.
  • Determine if there is any value added in using
    the Monte Carlo sampling approach over the
    statistical method.
  • Document findings to support/refute the need to
    use one approach over the other.
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