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<p>This thesis develops statistical analysis in support of Readiness-Based Sparing (RBS) for U.S. Navy aviation weapon systems. RBS seeks to determine the least-cost allowance list to meet pre-specified operational availability of specifically identified systems. The research shows how RBS products such as Navy Aviation RBS Model (NAVARM) can be used by leadership and builders to anticipate changes in RBS cost as a function of changes in key inputs. We develop NAVARM Experimental Designs (NED), a computational tool created by applying a state-of-the-art experimental design to the NAVARM model. Statistical analysis of the resulting data identifies the most influential cost factors. Those are, in order of importance availability goal, unit price, wartime flying hours, maintenance rate to failure, high priority order and ship time, number of aircraft, wholesale delay time, rotable pool factor, intermediate maintenance activity repair time, and mean time to repair. Seventy-five percent of NED predictions are within a 3% or less error of actual values for changes within 10% to baseline values, and all predictions are within 7%.
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<br>Subjects: readiness-based sparing; design of experiments; meta-modeling; sensitivity analysis; aviation supply and inventory; stepwise regression; Navy aviation; RBS model</div>
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Pfaff, Jason J.</div>
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