Maximizing Medicine Access through Trade Credit Optimization in Subsidized Fixed-Price Markets
Bibliographic record
Abstract
In regulated pharmaceutical markets similar to Canada, Japan, and Iran, government-determined fixed prices, permits for multiple producers, and subsidies support public health by ensuring medication availability and fostering social equity. However, intense competition driven by fixed pricing and profit motives, in the end leading to bankruptcies among supply chain members, undermines access to medicines. Although this fixed-price tampering, producers frequently rely on trade credit while overlooking wider financial streams, aggravating supply issues and access barriers. This oversight leads to distribution interruptions and diminishes medicine supply. Diverging from previous research centered on physical logistics, this study emphasizes financial currents and introduces a decision-aid framework for a two-stage pharmaceutical supply chain with temperature-sensitive perishability. The framework refines trade credit levels to boost overall supply chain earnings under fixed-price and subsidy-backed conditions, fostering collaboration among stakeholders. Validated against centralized and decentralized scenarios, the optimal discount rate strengthens chain stability. Results measure the trade credit required to stabilize financial currents, reduce access constraints, and ensure medicine availability, providing practical guidance for policymakers and administrators in subsidized fixed-price markets.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".