Supply chain models with consignment stock agreement, payment schemes and delay-in-payments
Bibliographic record
Abstract
Supply chain management (SCM) has shown to be a successful strategy to manage the flow of goods, materials, information and services between multiple entities in one organization or multiple businesses working together to provide final customers with final products or services with the objective of improving and enhancing the performance of the chain and maximizing its profit. Inventory management (IM) is one element of the SCM that has shown researchers’ interests as it plays a major role in increasing supply chain profits and satisfying customers. Different coordination mechanisms have been developed to improve the collaboration and the integration of supply chain players. Consignment stock (CS) is one of the coordination mechanisms that is extensively studied by researchers to reflect its benefits, drawbacks, and the proper techniques of implementing it between two or more players in the chain. The studies of the CS still have some gaps that can be covered by researchers such as studying its effect in a three-level supply chain or when a delay-in-payment exists. Optimizing the number of payments or studying a three-level supply chain system with multiple suppliers and multiple buyers has not been developed. This thesis covers these gaps and considers different scenarios where a CS, a traditional policy (TP) or a combination between both of them might exist in case a system consists of three players. The main findings are optimizing the number of payments and incorporating a delay-in-payment increase the profit of the chain. In addition, a combination of a TP between the upstream players and a CS between the downstream players has shown to be better than adopting the same policy between all players. Some results of adopting a CS by all players have shown to be very close to the best scenario which could be the best option when demands highly fluctuate.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".