Vendor-managed inventory: a literature review on theoretical and empirical studies and future research directions
Bibliographic record
Abstract
Vendor-managed inventory (VMI) is a business practice in which the supplier manages the inventory at the customer's premises and makes replenishment decisions. VMI has been extensively studied over a short period of time since it was successfully implemented in the industry in the late 1980s. In this paper, we classify and review theoretical and empirical studies on VMI. In particular, we provide three classifications of theoretical papers focusing on the contractual agreement between supply chain partners, drivers of the benefits of VMI, and research approaches, while we classify empirical papers based on their research purposes and methodologies. One of our findings is that most theoretical studies examine event-based VMI contracts such as (z, Z)-type contracts, while the penalties/rewards are based on long-term performance in many VMI agreements observed in practice. We also find that most theoretical studies focus on cost reductions from VMI and their drivers, while empirical studies report various benefits of VMI such as lower inventory and fewer stockouts. We suggest several future research directions based on our literature review.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".