Supply chain efficient inventory management as a service offered by a cloud-based platform
Bibliographic record
Abstract
This paper proposes a Cloud-centric platform offering efficient inventory management as a service for supply chain stakeholders. The offered service will enable all stakeholders in a supply chain to minimize both their ordering costs and their shortage costs, while managing their inventories. The proposed cloud-based platform stores all the data, relating to a given product item, released by the involved stakeholders throughout its life cycle; from its manufacture up to its retail, passing through its distribution, storage, etc. The objective of the offered service is to provide stakeholders, in a supply chain, with efficient replenishment schemes derived collaboratively on the basis of real-time information flow, including retail information. A stakeholder calls the proposed service in order to compute a real-time optimal “inventory threshold”, with regard to a given product category, to be considered in its replenishment policy. By optimal we mean that it minimizes the stock disruption likelihood, while minimizing the allocated resources in term of storage space and inventory cash value. The computation of the optimal parameters is based on a probabilistic model. Analytical results illustrate the efficiency of the inventory optimization service offered by our proposed cloud-centric platform.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".