Transshipment and coordination in a two-echelon supply chain
Bibliographic record
Abstract
To better match supply and demand, many distributors need to make the strategic decision on whether to collaborate with their competing distributors by adopting the transshipment strategy. In this paper, we mainly aim to answer the following questions: whether and when distributors should adopt the transshipment strategy in the presence of inventory competition? If the transshipment strategy is adopted, how supply chains can be coordinated? To answer these questions, in this paper, we model a supply chain with one manufacturer selling to two competing distributors. For the first question, we find that regardless of centralization or decentralization, the transshipment strategy is better when transshipment cost is lower or competition is less intense. Moreover, under decentralization, there always exists a threshold of transshipment cost. When transshipment cost is lower than the threshold, regardless of the competitive intensity, the distributors should always adopt the transshipment strategy. We further extend the model from symmetric distributors to asymmetric distributors and show our results are robust using numerical studies. For the second question, we design a buyback with sale rebate and penalty contract which can achieve coordination as well as win-win outcomes for all supply chain members.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".