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RAMSES: Rule-Based Asset Management for Space Exploration Systems: Automatic IMS Self-Reporting

RAMSES: Rule-Based Asset Management for Space Exploration Systems: Automatic IMS Self-Reporting. Prof. Olivier de Weck deweck@mit.edu MIT Department of Aeronautics and Astronautics (RAMSES Principal Investigator) Joe C. Parrish

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RAMSES: Rule-Based Asset Management for Space Exploration Systems: Automatic IMS Self-Reporting

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  1. RAMSES: Rule-Based Asset Management for Space Exploration Systems:Automatic IMS Self-Reporting Prof. Olivier de Weck deweck@mit.eduMIT Department of Aeronautics and Astronautics(RAMSES Principal Investigator) Joe C. Parrish jparrish@aurora.aeroAurora Flight Sciences Inc.(RAMSES Project Manager) Abe Grindle grindle@mit.eduMIT Department of Aeronautics and Astronautics(Graduate Research Assistant) End of NASA STTR NNC07AB25C Phase 2 System Demonstration NASA Johnson Space CenterAugust 14, 2009

  2. The Team • Massachusetts Institute of Technology • Olivier de Weck, Ph.D., Associate Professor (RAMSES PI) • Abe Grindle, Graduate Student, AA and TPP • Sydney Do, Graduate Student AA • Howard Yue, Graduate Student AA • Aurora Flight Sciences Inc. • Joe Parrish, VP (RAMSES PM) • James Francis, Software Engineer • Joe Zapetis, Software Engineer • Joanne Vining, Senior Technician • NASA • Nathan Sovik, NASA SSC Stennis, COTR (Phase 1) • Ray Bryant , NASA SSC Stennis , COTR (Phase 2) • Sarah Shull, NASA JSC DO5

  3. Agenda • Motivation for Real-Time Automated Asset Management • Overview of RAMSES STTR Phase 1/2 Project • Project Heritage • High-Level System Architecture • Smart Container (CTB) • Location-based Asset Tracking Software (RAILS v2) • Microgravity Testing Results • Cost-Benefit Analysis • RAMSES Demo (in Lunar Habitat Mockup) • Discussion and Suggestions for Phase 3

  4. Motivation for Real-Time Automated Asset Management

  5. Pocket Container Carrier Module Segment Compartment Element Pallet Assembly Facility* Node Vehicle Supply Items M02 Bags MPLM Racks MPLM Cargo Integration MPLM In Shuttle Nested Complexity Evans W., de Weck O., Laufer D., Shull S., “Logistics Lessons Learned in NASA Space Flight”, NASA/TP-2006-214203, May 2006 • Item • Drawer • Kit • Locker • Unit • Rack • Lab • Platform • MPLM • Payload Bay • Fairing • Component • Subsystem • System • SRU • LRU • ORU • CTB • M-01 • M-02 • M-03 *In-Space Facility (e.g., the European Technology Exposure Facility (EuTEF) Need to track items across dynamic parent-child relationships

  6. Current ISS Inventory Architecture • Barcodes • CTBs (Cargo Transfer Bags) and other bags/kits • 1/2, Standard, Double, Triple • Concentration of Inventory Transactions • IMS (Inventory Management System) • Copies in Houston, Moscow, Baikanour, and ISS • Delta files • ISO (Integration Stowage Officer) • Mission Control; assist crew with IMS • Write stowage notes for all procedures

  7. Inventory Tracking on ISS Manual bar-code based system Relatively accurate system (~ 3% lost) RSA/NASA Inventory Management System (IMS) Requires substantial manual labor (>20min/day/astronaut)

  8. ISS Lessons Learned International Space Station Multilateral Coordination Board Consolidated Lessons Learned For Exploration, report issued July 22, 2009 10-Lesson: Micromanage Consumables Resupply, logistics and onboard stowage have proven to be critical issues for the ISS. Out of necessity, the program carefully re-evaluated the usage rates for critical consumables and found innovative ways to reduce resupply requirements. Micromanagement of consumables was found to be essential to ensure adequate supply inventories. Reliability and maintenance strategies are critical. Application to Exploration: Consumables will be even more critical for extended lunar or Mars expeditions because of the more limited resupply opportunities. Micromanagement of consumables and inventory will be critical and should be thoroughly addressed during the systems design phase.

  9. ISS Lessons Learned International Space Station Multilateral Coordination Board Consolidated Lessons Learned For Exploration, report issued July 22, 2009 NASA ISS Lessons Learned – Logistics, Resupply, and StowageBased on the ISS experience, careful management of consumables and inventory will be critical and should be thoroughly addressed during the systems design phase. The Exploration Programs should utilize technologies that were not readily available at the beginning of the ISS Program to help minimize resupply requirements and track inventory. For example, Radio-Frequency Identification Devices (RFID) might help to simplify inventory tracking.

  10. Functions of a State-of-the-Art IMS • Automated inventory tracking and management • Automated mass and C.G. calculations for vehicle management before launch and during flight operations • Automatic reports of % full levels (by mass or volume) by module/vehicle/node for precise stowage planning • Alerts when critical consumables are about to run low (can establish dynamic warning thresholds) • Alerts when incompatible/hazardous items are stored together or in the wrong place • Save temperature/pressure history with the item • Real time assistance in searching for items • …

  11. Implications of Automated ISS Inventory Process @ ISS Assembly Complete: • 600 Cargo Transfer Bags (CTBs) on-orbit • 730 Crew Hours / Year spent updating IMS ~ 4 ½ person-months (40 hrs/wk)

  12. RAMSES Project Heritage

  13. MIT Space Logistics Planning & Analysis MIT and Aurora Flight Sciences (formerly Payload Systems Inc.) have been collaborating on a series of projects relating to space logistics and automated inventory tracking/management • Interplanetary Supply Chain Management & Logistics Analysis (ISCM&LA) • Funded through NASA Exploration Systems technology BAA 2005-2007, $4M over 2 years • Multi-faceted project, resulting in SpaceNet software for LEO/Lunar/Mars supply chain modeling and analysis • Haughton-Mars Research Station Expedition 2005 • Field campaign, applying principles from SpaceNet • RFID-based portals enabled tracking of vehicular traffic in/out of base camp; personnel and equipment in/out of habitat and lab areas • Rule-Based Analytic Asset Management for Space Exploration Systems (RAMSES) • STTR Phase 1 and 2, from NASA Stennis Space Center 2006-2009 • Focus on hardware-agnostic architecture for tracking diverse assets on ground and in space • Several generations of smart containers Haughton-Mars Research Station Smart CTB Prototype

  14. Introduction to SpaceNet • SpaceNet is an interplanetary supply chain modeling and simulation tool • Goal: Support short and long-term architecture and operational decisions such as: • What effect will vehicle (element) design decisions have on future NASA operations and lifecycle costs? • Are in-space refueling and ISRU helpful in improving performance? • Is it better to have cargo vehicles that carry small re-supply loads or a few large pre-deploy or resupply flights? • Diverse user base • Mission/system architects • Mission planners and logisticians • Operations personnel • Etc… Staging Location In-Space Refueling

  15. Interplanetary Supply Chain Management and Logistics Architectures SpaceNet – Network View SpaceNet 1.3

  16. SpaceNet – Manifest View

  17. RFID at the Haughton-Mars Project Research Station

  18. HMP Expedition 2005: Objectives • Inventory classes of supply on base • Analyze analogy to lunar/Mars base • Model HMP supply chain • Quantitative transportation network model • Test and evaluate RFID technology • Field experiments during normal HMP operations • Test autonomous tracking of supplies, vehicles, people • Study EVA logistics requirements • Short traverses and overnight stays

  19. Inventoried 2300 items (20,717 kg) Developed inventory procedures Validated supply classes Maintained inventory over time (for use next season) Goals: Understand, Categorize Supplies on Base - Classification of inventory - Quantify inventory (total imported mass) - Compare with prediction for a lunar base - What would it take to ‘create’ an HMP-like base? Total Mass Inventoried [kg] HMP: Inventory

  20. Personnel Profile 4. M Cargo Mass Flow 6. F 0.D 0.D 6. F 7. I 7. C 3. R 5. H 1.O 6. F 0. Dep. Point for Each Team 1. Ottawa 2. Edmonton 3. Resolute 4. Moffet USMC St. 5. HMP Base 6. HMP Field 7. Cambridge Bay Iqaluit Yellowknife 7. Y 0. D 2. E Normal Trans. Emergency Trans. HMP: Transportation Analysis Transportation Network Analysis for HMP • Mass inflow per season ~ 20 mt • Analysis highlights room for improvement: • Plan for reverse logistics • Reduce asymmetric flight usage • Smooth personnel profile • “Robustness” more important than optimality • due to weather, emergencies, aircraft availability

  21. Formal Experiments Asset Flow ATV Tracking HMP: Agent & Asset Tracking (RFID) Goal: “Smart Base” for Micro-Logistics • Technology demonstrations • Observation/Insight for further implementation Selected Conclusions • RFID has potential for remote bases • dramatically improve asset management • reduce crew time spent in inventory • increase ground knowledge of base requirements • Technical hurdles • reliability, interference, packaging • STTR to further investigate

  22. Overview of RAMSES Phase 1/2 STTR Project

  23. RAMSES Project Overview • NASA STTR Phase 2 (Research Institution partner: MIT) • Contract number NNS07AB25C (NASA Stennis Space Center) • Objective: Provide asset tracking and management for all of NASA’s assets • Document in office at NASA center…supply item on International Space Station…pressurized rover on surface of Moon/Mars • Hierarchical to accommodate diverse styles of assets • Room level…outdoors…orbits and planetary surfaces • Device-agnostic to accommodate diverse styles on locating/tracking systems • RFID…WiFi…Cellular…GPS • Emphasis on open source software • E.g., Google Maps API • Strong potential for terrestrial applications • Military theater operations • Humanitarian aid • Entertainment industry • Consumer products

  24. RFID RFID RFID NASA Applications LN Local Node Applications R RFID Reader RFID RFID Tag Real-Time Data Capture Platform Integrate real-time RFID; Barcode; GPS; Interplanetary Network Connection Internet TDRSS Planetary Surface Earth Ground In-Space LN LN LN Launch vehicle Spaceport R R R R R R ISS Lunar CEV Mars Ground Processing Base RFID RFID RFID

  25. RAMSES Architecture Informational Architecture Physical Architecture Rule-Based Analytics events Outdoor Tracking transactions Indoor Tracking system state Container Tracking raw data (e.g. triggers) Messaging System Relational Database Relational Database Google Maps 802.11 interrogate Tracked Items Other Devices Web Browser (RAILS) external information User Email, SMS

  26. RAILS • RDF-based Asset Information and Location Software • Web-based real-time interface Facility-level Tracking Item Locator Container Inventory Supported Web Browsers: Internet Explorer, Firefox

  27. passive tag item x wireless radio 802.11 wireless radio 802.11 item x 8:41am Smart Container Concept www interface wireless router 8:41a.m. 802.11 PC/laptop switch MySQL Relational database RFID Antennas (1-4) database RF opaque “liner” …. container 1 Prototype: Instrumented CTB 5V DC Battery (30Ah) RFIDReader (915 MHz)

  28. Smart Container Evolution Generation 1 Cooler Proof-of-concept for RF-insulated container and automated/wireless inventory function Generation 2 Hard Container Hard-case with integrated display, modular electronics Generation 3 Soft Bag CTB proxy with RF-shielding insert, integrated electronics and antennae Generation 4 CTB Retrofit Kit CTB-specific prototype, ready to transition to flight implementation

  29. Testing Results (2007 MIT Undergraduate Design Project) • 6 Test Subjects • 3 male, 3 female • 24 Experiments each • Time Savings: RFID versus Bar-coding can be > factor of 2 time savings • Benefit increases as more items have to be managed in the system • Accuracy: Above 95% is feasible if: • use 3 RFID antennas • ~20 items • 2 tags per item helps Source: Teresa Pontillo, Alice Fan, 16.622 Final Report, MIT

  30. Microgravity Testing of Smart CTB August 11-12, 2009 Play Movie Clip X48p test condition

  31. Motivation for Microgravity Testing • Hypothesis that microgravity environment could actually improve RFID tag read accuracy • Tags in free-float will move around in container and present themselves in randomized orientations to antennae • Vice laying on top of each other in bottom of container • MIT and Aurora proposed parabolic flight experiment to NASA FAST program, and were approved for two sorties • Sorties took place earlier this week, using Zero-G Corp. B-727 from Ellington Field • Collected data during 68 parabolas, with emphasis on measuring read rates for different numbers and types of tagged materials and different tags

  32. W=Water Bottles T=Tissues X=miXed Items M=Metal Cans Parabola 15 X36 ___ v M30 ___ T30 ___ W30 ___ X30 ___ M30 ___ T30 ___ W30 ___ X30 ___ W24 ___ M24 ___ T24 ___ X24 ___ W24 ___ M24 ___ T24 ___ X24 ___ X18 ___ M18 ___ T18 ___ W18 ___ X12 ___ T12 ___ M12 ___ W12 ___ T6 ___ X6 ___ W6 ___ M6 ___ v X60 ___ Parabolas 16-22 Parabolas 1-7 Parabolas 8-14 X54 ___ RAMSES System 0-g Test Flights TEST PLAN – FAST Program August 10-14, 2009 X48 ___ 0g 1.8g 0g 1.8g X42 ___ MIT-Aurora Flight Sciences Parabolas 23-34

  33. Flight Day One Results with Alien Tags

  34. Results of Microgravity Testing • Flight data collected for three materials (water, metal, paper) and two types of tags (Alien, Omni-D) • Baseline data collected in 1-G for comparison • For all materials and tags, microgravity read rates were equal or better than those from 1-G • From a performance standpoint, we believe that there are no fundamental reasons why RFID in 0-G would be inferior to 1-G • Caveats: • Small statistical samples for 0-G cases • Tag read rates are still not perfect – but we generally saw 90-100% read rates during 20 seconds of reader integration

  35. Cost-Benefit Analysis

  36. Net Present Value Analysis • Are the benefits of this RFID application worth the costs? How likely is this system to result in net present value? • Key Equation: B = Benefits C = Costs r = Discount Rate (Set to 7%, per OMB guidelines [1]) N = Number of Years of Study (FY 2009 – FY 2016, N=8)

  37. Two Implementation Strategies Modeled • “Phase-In” Implementation • Existing CTBs currently on Station are gradually replaced by new, “wired” CTBs according to the existing launch schedule • Contents transferred to new bags by Crew; most-used bags first • CTB launch rate perhaps too low, especially post-Shuttle Retirement • Modification Kits Implementation • Instead of launching new CTBs, just launch RAMSES hardware in mod-kits that the Crew can install on-orbit to retrofit existing CTBs • Assumes all mod-kits launched & installed in FY 2009

  38. Costs Considered • NASA Engineer Time for: • Flight Certification & Approval • Operational Support & Maintenance • Cost for Vendor to Modify CTBs or Cost to Build Mod-Kits • Cost of RFID Hardware • “Opportunity Cost” of: • Launching the System Mass • Launching the System Volume • Crew Time to Transfer Items to Wired Bags or Install Mod Kits

  39. Benefits Considered • Value of Crew Time Saved on: • Bi-annual Inventory Audits • Missing Item Searches • Daily Inventory Management System Updates • Reduced workload for JSC Inventory Stowage Officers (ISOs) • Less need to assist Crew with Inventory updates/searches • Only Partial Savings realized, per “System Effectiveness” (β) parameter: β = (% of Inventory Transactions ‘Automate-able’) x (System Accuracy)

  40. Quantifying Value (“Opportunity Cost”) of Cargo Launch Volume & Mass • Value of Cargo Launch Volume = [Annual Net Variable Recurring Cost (all Cargo Missions)] [Annual Net Dry Cargo Launch Volume Available (habitable)] = ~ $20.3 million / m^3 (‘09-’10), ~ $31.6 million / m^3 (‘10-’16) • Value of Cargo Launch Mass = [Annual Net Variable Recurring Cost (all Cargo Missions)] [Annual Net Cargo Launch Mass Available] = ~ $25,500 / lb (‘09-’10), ~ $35,700 / lb (‘10-’16)

  41. Quantifying Value of On-Orbit Crew Time • Value of 1 Hour of On-Orbit Crew Time = [Average Annual ISS Ops Budget (Common Systems Operations Cost)] [# Crew] x [# “Active” Hours per day / Crew Member] x [365 days/yr] = ~ $185K / hr (’09) # Crew = 3, Each active 16 hrs/day = ~ $ 100K / hr (’10-’16) # Crew = 6, Each active 16 hrs/day • Notes: • Common Systems Operations (CSO) Cost is defined as “the cost to operate the ISS”, including “the cost to transport crew and common supplies” and “ground operations costs” [9] • International Partners’ negotiated shares of CSO Costs [10]: NASA = 76.6%; JAXA = 12.8%; ESA = 8.3%; CSA = 2.3% || RSA = Russian Segment & Crew Ops Costs

  42. Key Variables • 7 “High-Impact”, Uncertain Variables identified via Sensitivity Analysis of Discrete Calculation results (“best-available” input values): • Average ISS Ops Budget • # of “Active” Crew Hours • % of IMS Transactions that could be Automated • System Accuracy • Volume Required for 1 RAMSES Unit • “Opportunity Cost” of Cargo Launch Volume • # of CTBs that are to be “Wired” • All but “# of CTBs” are randomly varied within reasonable ranges for probabilistic Monte Carlo simulations; “# of CTBs” is varied between Monte Carlo simulations Value of Crew Time “System Effectiveness” “Cost” of System Volume

  43. Results • NPV = +$14.8 Million for Discrete Calculation, Mod-Kit Scenario • NPV = -$ 63.0 Million for Discrete Calculation, Phase-In Scenario • Monte Carlo general results: • Mod-Kit Scenario performs better than gradual Phase-In • Simulations w/ Normally-Distributed Variables perform slightly better than those w/ Uniformly-Distributed Variables • Both scenarios less than 50% likely to result in NPV > 0 if inventory transactions are evenly distributed among all CTBs • If transactions are somewhat concentrated in subset of CTBs, and RAMSES installation can be targeted to those CTBs, both scenarios are likely (to very likely) to result in NPV > 0. Magnitude and Likelihood of NPV vary with degree of transaction concentration.

  44. Results • If the transactions are evenly distributedthroughout all CTBs and we wire 100% of the total CTBs: • 43% probability of NPV > 0 • Mean NPV = $(13.2) Million • NPV Std. Dev. = $77.4 Million • If 50% of all transactions occur in 25% of the total CTBs: • 95% probability of NPV > 0 • Mean NPV = $49.4 Million • NPV Std. Dev. = $30.6 Million • If 75%of all transactions occur in 50% of the total CTBs: • 84% probability of NPV > 0 • Mean NPV = $46.8 Million • NPV Std. Dev. = $48.7 Million • Modification Kits Scenario: Normally-Distributed Variables Aurora Flight Sciences / Payload Systems Division

  45. Conclusions • If inventory transactions are concentrated in some subset of CTBs, and part or all of that subset can be targeted for RAMSES installation, this application of RAMSES is quite likely to result in positive Net Present Value. • Such concentration has been reported by JSC ISOs, but not quantified. Intuitively, it makes sense - some desk drawers get almost all the use. • Cost drivers:System Volume, Mass, & Crew Time required to install. • Key Benefit: Saving part of 20 min/day each Crew Member spends updating IMS (total = 730 hours/yr) . System Effectiveness (β) parameter is critical. • As with any Cost/Benefit Analysis, results are limited – can provide guidance, but not absolute truth. Assumptions and unknowns are important.

  46. RAMSES Demo

  47. Demo Flow • Login to RAILS with web browser • Smart Container Inventory (what is in it?) • Inventory database • Real-time updating • Supply Item Hierarchical Tracking (where is it moving (item)?) • Removal of item • Return item • Supply Item Addition • Item Search (where can I find …?) • Rule-based Analytics • Low Inventory Warning • Mass Properties, Shelf Life • Supply Class Incompatibility Rule • Automatic Messaging (email) • Logging out Login Information http://projects.payload.com/RailsV2 user: ramses password: rfid1

  48. DiscussionSuggestions for Phase 3

  49. Interest/Contact Points at NASA • NASA Stennis Space Center • T9.02 Integrated Life-Cycle Asset Mapping, Management, and Tracking Lead Center: SSC • NASA Wireless & RFID Working Group • Lead Center: JSC • Asset Management on ISS • Lunar Surface Micro-Logistics (e.g. in Habitat) • NASA Glenn (and NASA JSC CHeCS) • Crew Medical Supply Inventory • NASA Astronaut Office • Greg Chamitoff • ISS Operations Branch

  50. Recommendations for Phase 3 • Establish “Permanent” Test Implementation at JSC • Bldg. 9 ISS Mockup • Bldg. 14 Lunar Habitat Mockup • On-Orbit DTO Demonstration in 2010-2011 timeframe with “a few” retrofitted CTBs • How many? What items? • Integration with IMS • Medical supply tracking • STS-134 and E25/E26 are potential targets of opportunity • Continue/Evolve Database and Rule-Base Development • Critical Inventory Levels with Crew Size 6 • Extended ISS Operations (2016-2020) • Possible recommendation by Augustine Commission today

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