Performance optimization of pharmaceutical supply chain by a unique resilience engineering and fuzzy mathematical framework
Bibliographic record
Abstract
Abstract Pharmaceutical supply chains (PSCs) are responsible for guaranteeing that the right people receive the right medication at the right time and in the right conditions. These responsibilities make PSC very complex and subsequently increase their vulnerability and disturbance probability. Resilience engineering (RE) can enable supply chain managers to cope with disruptions and to help them maintain their efficient performance. This study proposes a unique RE framework for performance optimization of the pharmaceutical sector in a veterinary organization. A standard questionnaire was used to collect the required data. Next, data envelopment analysis (DEA) and fuzzy data envelopment analysis (FDEA) approaches were employed to formulate the problem. Sensitivity analysis was performed based on the most appropriate model of DEA and FDEA. The results showed that redundancy was the most effective factor in enhancing efficiency in PSCs in the veterinary organization. This is one of the first studies that investigate the influence of resilience indicators on PSC through DEA/FDEA and statistical methods.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".