Optimal inventory cycle in a two-stage supply chain incorporating imperfect items from suppliers
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
In this paper, a two-level supply chain situation is considered. A manufacturer/vendor is assumed to receive raw material parts from a number of suppliers for assembling a single product. The parts obtained from the supplier are not of a perfect quality. That is, each supplier may have an approximately fixed percentage of defectives in the lots supplied. An inspection process is carried out at the vendor’s end to take out these imperfect parts while manufacturing the product. A different percentage of defectives from each supplier gives rise to some unused parts left with the vendor in each cycle. These parts are utilised in the next cycle. Different coordination mechanisms for controlling the supply chain inventory are studied. A cost minimisation model is given for each. A numerical example is given to compare the coordination mechanisms.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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