Decision analysis of individual supplier in a vendor-managed inventory program with revenue-sharing contract
Bibliographic record
Abstract
As a useful strategy to improve the flexibility of the system to manage uncertainty in supply and demand and to improve the sustainability of the supply chain, vendor-managed inventory (VMI) programs have attracted widespread attention in the field of supply chain management. However, a growing body of empirical literature has shown that participants’ decisions deviate significantly from the standard theoretical predictions. Under a VMI program, the supplier bears not only the production cost, but also the risk of leftover inventory. Moreover, the inequality among participants and different personalities of decision-makers in VMI programs may lead to the divergence of decision-making. To understand the supplier’s replenishment decision in view of the behavioral pattern, we propose a new inventory model for the supplier with the focus theory of choice. The proposed model conceives that the retailer evaluates each replenishment quantity based on the most salient demand for him/her instead of calculating the expected utility. By employing this inventory model, we construct a two-tier supply chain model with revenue-sharing contract and theoretically derive the optimal sharing percentage of the revenue and replenishment quantity. Results analysis gains managerial insights into the strategic selection of the retailer who faces suppliers with different personalities. Comparisons between the classic revenue-sharing contract model and the proposed model are also carried out by illustrative examples. This research provides a new perspective to analyze individual supplier’s behavior in a VMI program with revenue-sharing contracts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".