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SpyGlass Estimation of Development and Manufacturing Costs and Risks for Satellite Electro-Optical Sensors

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SpyGlass Estimation of Development and Manufacturing Costs and Risks for Satellite Electro-Optical Sensors

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    1. Ó Galorath Incorporated 2004 SpyGlass Estimation of Development and Manufacturing Costs and Risks for Satellite Electro-Optical Sensors Karen McRitchie Galorath Incorporated Evin Stump Galorath Incorporated Dan Ferens Air Force Research Lab SCEA 2004 – Manhattan Beach, California

    2. Ó Galorath Incorporated 2004 Presentation Outline Introduction and Background Estimation Objectives SpyGlass Model Development SpyGlass Product Development SpyGlass – Current Status Conclusion

    3. Ó Galorath Incorporated 2004 Introduction and Background Phase I Small Business Innovative Research (SBIR ) contract awarded to Galorath in April, 2001 (9 Months, $100,000) Develop improved cost estimating relationships for estimating manufacturing costs of electro-optical sensors for space Algorithms built using the database using data from the “Passive Sensor Cost Model”

    4. Ó Galorath Incorporated 2004 Phase II - Objectives Algorithm Refinement Create (or refine) and validate algorithms to estimate costs of space-based, airborne and ground-based electro-optical (E-O) sensors Supplement Phase I with new data and methods Develop algorithms for key E-O elements, such as: Focal plane array assemblies & solid state devices common to electro-optical systems Optical telescope assemblies Cryogenic coolers Integration, assembly, and test Examine, in depth, the nature of electro-optical technologies and cost implications Collection of algorithms, interrelationships, and cost relationships are referred to as the “SpyGlass” model

    5. Ó Galorath Incorporated 2004 SpyGlass Model Development Goal: Develop a generalized engineering trades (Cost As Independent Variable, or CAIV) cost model for space-based electro-optical sensors Include all known missions (missile tracking, astronomy, etc.) Include all usual system elements (optical telescope assembly, focal plane array, etc.) Include all current technologies for each element Model Development Steps Data Collection Domain Definition Key Technical / Performance Parameter (KTPP) Identification Configuration/ Cost Baseline Development Building of Influence Functions

    6. Ó Galorath Incorporated 2004 SpyGlass Model Development 1. Data Collection Problem: Historical cost data is sparse and generally not enough for a statistically based model (a common problem for cost modelers!) Solution: A data collection approach which has several stages: Use existing high-level data from PSCM and other existing and available database Collect detailed data from sources such as NASA, MDA, and other Government and industry sources Use outside consultants, or subject matter experts (SMEs) to elicit detailed technical and cost data in a structured manner (SMEs were used in other development steps also !)

    7. Ó Galorath Incorporated 2004 SpyGlass Model Development 2. Domain Definition Ensure SpyGlass adequately addresses the E-O domain in terms of missions and specific technologies for missions SMEs identified 17 airborne and space missions which use passive E-O sensors as primary data collection means From agencies using U.S . launch facilities from 1996 – 2004 Missions provide framework for identifying technologies SMEs then identified technologies categories for these E-O sensor component categories: - Optical telescope assembly - Focal plane array - Calibrator - Mechanism - Integration and test - Cooler Technologies defined (e.g.,“Refracting” for telescope assy)

    8. Ó Galorath Incorporated 2004 SpyGlass Model Development 3. Key Technology or Performance Parameter (KTPP) Identification KTPP = Key Technology or Performance Parameter KTPPs are key cost drivers that describe technical and performance attributes which influence cost A four-part approach taken to identify KTPPs (with help from SMEs) Brainstorming – for initial identification Multi-voting – Ordering KTPPs in an ordinal sense Screening – Reviewing KTPP candidates to insure they meet criteria for SpyGlass and SEER-H Analytical Hierarchy Process: Ordering KTPPs in a ratio sense For selected KTPPs, ranges for values were determined

    9. Ó Galorath Incorporated 2004 Multi-Voting Forces a consensus (ranking opinions can differ) Requires at least three participants (usually SMEs) Illustration of process: SpyGlass Model Development 3. Key Technology or Performance Parameter (KTPP) Identification

    10. Ó Galorath Incorporated 2004 SpyGlass Model Development 3. Key Technology or Performance Parameter (KTPP) Identification Analytical Hierarchy Process (for KTPPs left after screening) Pair-wise comparisons and relative importance ratings Refines rankings, shows relative KTPP strength for cost Example of process: List KTPPs in order of importance (A to E) then enter numbers from 1 to 9 as follows: If A equal to B (as a cost driver), enter 1 If A slightly more than B, (or B slightly more than C, etc.) enter 3 If A strongly more than B, enter 5 If A very strongly more than B, enter 7 If A absolutely stronger than B, enter 9 If indecisive between two ratings, use Even number between ratings (e.g., 2 between 1 and 3)

    11. Ó Galorath Incorporated 2004 SpyGlass Model Development 4. Develop Configuration / Cost Baselines Baseline Configurations have technologies and KTPP values For Each E-O Sensor category (e.g., refracting telescopes), SME selects one project exemplar that is best (in SME opinion—most representative actual costs) Similar to mission default technology Availability of detailed cost estimation Availability of detailed technical information For selected technologies, SME assigns (in-range) values for all KTPPS Both technical and cost data needed Use real data when available; otherwise, use hypothetical values based on expert opinion SME must provide thorough rationale for selection (technical and cost information, sources of information, project name, etc.) SMEs also identify strong trade areas to supplement baseline configurations (e.g., using a more powerful telescope)

    12. Ó Galorath Incorporated 2004 Results from SME Analyses

    13. Ó Galorath Incorporated 2004 Build weighted “influence functions” for Development Labor (DL), Development Material (DM), Production Labor (PL) and Production Material (PM) A set of influence functions is constructed for each technology

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    16. Ó Galorath Incorporated 2004 What is a SEER Plug-In?

    17. Ó Galorath Incorporated 2004 SpyGlass Product Estimation Work Breakdown SEER-H Work Elements Electronics Mechanical/Structural Site (for O&S) Rollup SpyGlass Work Elements Optical Telescope Assembly Focal Plane Array Mechanisms Cooling Calibrator Integration and Test Software Elements May be linked in from SEER-SEM

    18. Ó Galorath Incorporated 2004 Key Inputs – Focal Plane Array Focal Plane Array. A device placed perpendicular to the axis of a lens or mirror that transforms light energy into electrical signals. Technologies Include Large Linear or Area Silicon CCD Linear Silicon Detector Custom Linear Gallium Nitride Multi-anode Micro Channel (MAMA) Linear HgCdTe Area HgCdTe Linear or Area InSb Ge:Ga Photoconductor

    19. Ó Galorath Incorporated 2004 Key Inputs – Optical Telescope Assembly Optical Telescope Assembly. An instrument used for enlarging and viewing the images of distant objects by means of refraction of incident light rays through lenses or reflection from concave mirrors. Technologies Include

    20. Ó Galorath Incorporated 2004 Key Inputs – Cooler Cooler. A refrigerating device designed to lower the temperature of a focal plane array and sometimes other parts of an electro-optical sensor to make it more sensitive to low levels of light. Technologies Include Single Stage Thermoelectric Two Stage Thermoelectric Single Stage Stirling or Pulse Tube Two Stage Stirling or Pulse Tube Multistage Sorption Single Stage Reverse Brayton Two Stage Reverse Brayton

    21. Ó Galorath Incorporated 2004 Key Inputs – Mechanisms Mechanisms. Specialized mechanical devices intended to support the operation of an electro-optical sensor system. Technologies Include Mirror Scan Drive Gimbals Fast Steering Mirror Selectable Optical Filter Assembly Alignment Assembly

    22. Ó Galorath Incorporated 2004 Key Inputs – Calibrator Calibrator. Specialized devices included in an electro-optical sensor system for the purpose of correcting errors that occur gradually due to ageing or other causes. Technologies Include: Visible/NIR Integrating Sphere Optical Cavity Blackbody Geometrically Enhanced Blackbody Collimated Blackbody Source

    23. Ó Galorath Incorporated 2004 Key Inputs – Integration and Test Integration and Test. The set of activities required to assure that an electro-optical system has been assembled according to design intent and that it is functioning within specified limits. Technologies (Activities) Include: Integration/Alignment OTA to Mechanisms & Calibrators Integration/Alignment OTA to FPA Calibration & Test of Integrated Assembly Acceptance Testing

    24. Ó Galorath Incorporated 2004 Progress and Lessons Learned Progress Project nearing end of 2 year development phase Beta release this summer (with the new SEER-H 6.0) Lessons learned EOS is a complex field and SME participation is essential Technologies required for estimation will evolve over time Make the model and software adaptable to changes such as new technologies

    25. Ó Galorath Incorporated 2004 Conclusion SpyGlass will enable: Better understanding of EO Sensor cost drivers Better performance and technical trades Increased insight into risks Please stop by the Galorath booth for more information

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