Efficient Supply Chain Management at the U.S. Coast Guard Using Part-Age Dependent Supply Replenishment Policies
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 United States Coast Guard (USCG), now part of the Department of Homeland Security, has the mission to secure the U.S. coastline using a combination of air and sea capabilities. This paper focuses on an application of operations research techniques at the USCG to improve the performance of its aircraft service parts supply chain. We focused on evaluating the supply chain benefits from linking the aircraft maintenance database with the aircraft parts inventory database. This required us to (a) develop an approach to link the databases and (b) use aircraft maintenance information to improve the inventory management of service parts at the USCG. We first used mathematical programming tools to merge the maintenance database with the demand database. We then developed state-dependent supply replenishment policies that use part-age information to manage the service parts supply chain. We show that one of the proposed policies permits analytic estimation of the benefits of linking the data sets. The impact of these inventory policies was evaluated using empirical demand data for 41 critical parts over a five-year period. Computational results suggest that our proposed policies can lead to significant reductions in inventory cost over the current system, as high as 70% for some parts. Based on the insights from this study, the USCG is currently contracting with commercial vendors to develop an operational database and decision-support implementation across all parts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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