Drug recall management and channel coordination under stochastic product defect severity: a game-Theoretic analytical study
Bibliographic record
Abstract
This paper analytically explores drug recall programmes in the pharmaceutical industry by considering the product defect severity as a source of uncertainty. Under the Stackelberg game model, a pharma-manufacturer outsources the drug recall management and pays collecting fees to a third party logistics provider (3PL) for collecting the defective medications. On the other side, the 3PL provides incentives to customers to facilitate product recall. In this research, we first analytically show the negative effect of lack of coordination between the pharma-manufacturer and 3PL. Then, a new coordination model, namely collecting fee agreement is proposed under which the pharma-manufacturer aims to motivate the 3PL to collect more defective medications. This research also analytically explores the effect of orchestrating the collecting fees and incentives under stochastic product defect severity. Finally, a Nash-bargaining game model is proposed to share the profits between the pharma-manufacturer and 3PL under the collecting fee agreement. Both analytical and numerical results reveal that the collecting fee agreement not only increases the collection rate of defective items and protects the patients from unsafe products, but also simultaneously improves the performances of whole pharmaceutical supply chain and its members while reducing the governmental penalties imposed on the pharma-manufacturer.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".