Quantifying the Impact of Aircraft Cannibalization Task MM0206

1. Quantifying the Impact of Aircraft Cannibalization Task MM0206 Principal Investigator C. Richard Cassady, Ph.d., P.E. Co-Principal Investigators Scott J. Mason, Ph.D., P.E. Justin R. Chimka, Ph.D. Graduate Research Assistants Kellie Schneider Stephen Ormon Undergraduate Research Assistants Chase Rainwater Mauricio Carrasco Jason Honeycutt ASC PA 03-2420 9/15/03

2. Project Motivation • extensive use of cannibalization in fleet maintenance • existing mathematical models of cannibalization do not address USAF issues

3. Project Objectives and Activities • project objectives • to develop a mathematical modeling methodology for assessing the impact of cannibalization on fleet performance • to identify policies for making cost-effective, dynamic cannibalization decisions • to study the impact of these policies on management of the spare parts supply chain • project activities • generic scenario definition • generic simulation modeling • application • future work

4. Generic Scenario Definition • set of n independent and identical aircraft • each aircraft • two parts connected in series • part i has a constant failure rate λi • aircraft operation • continuous until failure • aircraft failure – caused by failure of part j • immediately routed to base of operations for Mx • sj part j “spares” in the system i=1 i=2 λ1 λ2

5. Maintenance Logic failure – remove part j (time Rj) – send removed part off for repair (time Lj) spare part j available? Y N install part j (time Ij) cannibalization allowed? N Y cannibalization possible? N Y cannibalize (time Cj) – cannibalized aircraft now needs both parts aircraft departs aircraft waits

6. Maintenance Logic (cont.) repaired part j returned to base failed aircraft waiting for only part j? Y N install part j on failed aircraft failed aircraft waiting for both parts? Y N spare of other part available? N Y aircraft departs restore aircraft add part to inventory

7. Generic Simulation Modeling • simulation model of defined scenario constructed in Arena • performance measures estimated from model include: • average readiness (R) • Mx-man-hours per flying hour (MMH/FH) • average “experience” of a failed aircraft

8. Performance Analysis – Example (min, mode, max) in hr

9. Performance Analysis (cont.) • suppose target average readiness is 80% • suppose no cannibalization is permitted • R = 78.8% • MMH/FH = 0.0035 • suppose cannibalization is permitted • R = 81.9% • MMH/FH = 0.0042 • cannibalization satisfied the readiness issue but increased requirements for Mx resources • what are some other options? • 1. add a spare for part 2 • 2. reduce the repair delay for part 2 by two days • option 1: R = 84.2%, MMH/FH = 0.0036 • option 2: R = 81.2%, MMH/FH = 0.0036

10. Performance Analysis (cont.) • suppose all three options are implemented • R = 86.6% • MMH/FH = 0.0039 • average “experience” of a failed aircraft • 23.5% receive a spare immediately • 11.9% are restored via cannibalization • 64.6% must wait • average time waiting for a single part = 70 hr • 11.9% become cannibalized • average waiting time after cannibalization = 174 hr

11. Application Outline • Hill AFB visit • motivating questions • should cannibalization be consolidated? • how many technicians should be assigned to the CANN-dock? • how many aircraft should be designated as CANN-birds? • how long should aircraft remain in CANN-bird status? • simulation modeling

12. Simulation Modeling – Application • based on the cannibalization activities that take place at Hill AFB • modeling concepts • one wing, three squadrons • cannibalization, slaving • AMU-level or consolidated cannibalization • fixed number of CANN-birds • rebuilds after specified number of days • rework and testing after rebuilds • limited number of maintenance technicians • various technician skill sets • technicians pulled from AMU to CANN-dock