ATLAST Deployment &Push Pack Spares Optimizer

1. ATLASTDeployment &Push Pack Spares Optimizer Presented by Dr. Naaman Gurvitz

2. Log Functions System Configurations/Status Aircraft/Spares Assembly Location/Age/Status Time-based Forecasts Availability Model Input Data Legend Achieved Flying Hours Spares Unavailability Operations Failures and Life Limit Events Flying Program Stock Levels Spares Locations Engineering ARDEC Assembly Ages And Repair History Spare Pre-positioning Deployment PM/Readiness Base Maintenance Logistics/Supply IMMC/Item Mgr Supply Maintenance Depot & Field Repair Locations, Rules/DOF, Capacity, Cycle Time Order Lead Times Repair Turn Around Shipment Times Tasks Performed Awaiting Parts Occurrences Depot Maintenance Items Condemned Awaiting Maintenance Occurrences Arrivals to Depot ATLAST Model Logic

3. ATLAST Strengths & Applications • Analysis to be Supported with Model • Maintenance Concept Modifications (e.g. Recapitalization) • Aircraft Induction Strategies • Operations Surge • Parts Purging • Sparing Strategies • Deployment Planning • Real-World Complexities (State-based Forecasting) • Supports Initialization Down to Serial Number • Initialized “State” of Components • Phased Aircraft Inductions by Tail Number • Aging Analysis • Recap, Zero Aging Analysis • Distribution Swapping based on Repair Count • Reconfigurable Indenture Structures • Life Limit Event Management • Op Temp Variations by Location • Interchangeability/Substitutability • Capacity Constraints • Time-Dependent Outputs

4. ATLAST Outputs • Readiness • Time on Wing • Availability • Achieved Flying Hours • Cumulative Age • Supply • % of Time Awaiting Parts • Avg. Time Awaiting Parts • Spares Stock Levels • Spares Unavailability • Maintenance • Removals (Causal, Timed, Opportunistic) • Modules to Depot • Condemnations • Tasks Performed • % of Time Awaiting Maintenance • Avg. Time Awaiting Maintenance • Cost • O&S • O&S Per Flight Hour By Location and Time Dependent

5. ATLAST Optimizer • Deployment & Push Pack Spares Optimization Module: • Creates a full deployment scenario on any number of aircraft at a single base • User defines logistics delay and operational profiles • Generates availability vs. cost graph • Provides analyst necessary information to determine optimal spares pack

6. Scenario Setup Process

7. Scenario From Master Database Select Master Database Choose Master Choose DeploymentScenario name OR Type Location Select Base Select aircraft tail numbers

8. Scenario From Master Database Define Deployment Period Assign OperationalProfile

9. Scenario From Master Database Set Logistic Delays

10. Scenario From Master Database Set Number of Histories Type Descriptive Information

11. Simulation & Optimization Run Simulation Verifications Select Ranking Method Ranked Spares List associated with Cost and Estimated Performance Select Sparing Strategy and Run Simulation Verification

12. Simulation & Optimization Selected Sparing Strategy Blue Points Simulated Values Red Line Estimated Values Selected Point “co-ordinates”

13. Validation Identical Assumptions in Simulation and Analytical Models 5 Aircraft in 8 quarters a) No Life Limits b) Single exponential failure distributions c) New aircraft

14. Numerical Results: Case A 10 Aircraft in 8 quarters a) Life Limits b) Multiple Weibull failure distributions c) Initial ages

15. Numerical Results: Case B 5 Aircraft in 4 quarters a) Life Limits b) Multiple Weibull failure distributions c) Initial ages

16. Numerical Results: Case C 1 Aircraft in 1 quarter a) Life Limits b) Multiple Weibull failure distributions c) Initial ages

17. Conclusions • AT LAST Deployment Push Pack Spares Optimization Module is both accurate and efficient • Further developments will include: • An automatic adjustment mechanism • Improved handling of “coinciding” LRU removals occurrences • Importing optimization results to the master database

18. Thank You

19. Backup Slides

20. Mathematical Formulation Markov Process States Legend n=number of systems m=number of LRU’s per system s= number of spares = effective failure rate = repair turnaround rate Reducing failure rates (function of operational units) Same failure rates (enough spares to ‘fill up’ for failed units) Repair rates function of number of failed units AT-LAST – Finite Markov State Process Constant failure rates (independent of number of failures) Repair rates function of number of failed units Traditional – Infinite Markov State Process

21. Steady State Markov Equations State 0 State s State s+1 State s+nm Boundary Condition State 0 State s State s+nm Boundary Condition

22. Steady State Probabilities Poisson Distribution

23. Little Theorem Where: Where:

24. Unavailability Estimates ATLAST Expression: Applicable for serial systems in which LRU’s do not fail while the system is down Where: calculated from outputs