Can vendor-managed inventory (VMI) reduce inventory and stockouts simultaneously?
Bibliographic record
Abstract
Vendor-managed inventory (VMI) is a supply chain practice where the supplier manages the inventory at the customer and makes replenishment decisions. This paper first provides a systematic literature review on the benefits of VMI to show that most of the analytical studies focus on cost reduction from VMI, while empirical and simulation studies have reported various benefits, including lower inventory and fewer stockouts, which seem difficult to achieve simultaneously. Motivated by this, we then present and study an exact analytical model to measure inventory and stockouts under VMI and RMI (Retailer-Managed Inventory). The model considers a supply chain of a supplier and a retailer, where a continuous-review (Q, r) policy is used to meet demands at the retailer. Both deterministic and Poisson demand cases are examined. With deterministic demands, this study provides a necessary condition for VMI to reduce both inventory and stockouts. With Poisson demands, our computational results suggest that VMI may be able to achieve both lower inventory and fewer stockouts, but only when the ordering efficiency is improved or the replenishment leadtime is reduced under VMI. Our study sheds light on the importance of ordering efficiency improvement and leadtime reduction in VMI implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".