Coordination of competitive advertising via investing in transportation lead time reduction
Bibliographic record
Abstract
In this study, a contract for vertical and horizontal coordination is developed in which transportation mode and carbon emissions tax play a key role in determining the values of the contract parameters. The contract is designed for simultaneous coordination of cooperative advertising and periodic review replenishment decisions of a supplier and two competitive retailers. To obtain the optimal decisions, firstly, the traditional decision-making structure is modeled. After that, the centralized structure is modeled to obtain decisions that are profitable for the whole supply chain. Finally, for convincing the competitive retailers to accept the centralized decisions, the supplier applies a lead time crashing contract in which two transportation modes, i.e. fast and slow, can be used. Considering the carbon emissions tax imposed by the government, the coordination contract is designed in such a way that the supplier considers the trade-off between reducing lead time and paying tax on carbon emissions while providing enough incentives for the competitive retailers. Results of the sensitivity analyses showed that the proposed model is profitable from economic and environmental viewpoints. From environmental viewpoint, considering the carbon tax leads to a decrease in the carbon emissions that will be released by the transportation modes. From economic viewpoint, coordinating coop (cooperative) advertising and replenishment decisions of the SC members, enhances demand and provides a higher service level, which increases the SC profit. The contract is conditionally applicable under situations where the carbon emissions tax or lead time reduction costs become high.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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 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".