How Sikosky Adapted to Meet US Army FARA Program Timeline
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The U.S. Army developed an aggressive timeline to design, build, assemble and fly a clean sheet aircraft for the FARA (Future Attack and Reconnaissance Aircraft) program. FARA is one of the U.S. Army's Future Vertical Lift programs to revolutionize their rotorcraft fleet. The FARA program's timeline provided for approximately 40 months to bring a clean sheet aircraft to first flight. To achieve this timeline, Sikorsky, a Lockheed Martin Company, developed a solution set to reduce lead time developed specifically for the FARA program. This solution set contained Product Enablers, Process Enablers and Manufacturing Enablers to reduce lead time across the entire enterprise. A lead time metric was developed that calculates the duration from when a final design is released until the first unit completes manufacturing. This metric was applied to a recent program for similar parts to create a baseline for comparison. In this paper we take a critical path FARA component and explain how the solution set was applied. The results show a greater than 50% reduction in lead time for this component.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it