Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review.
How this is built →
1Distinct papers
5Unique collaborators
1/1Semantic Scholar citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Semantic Scholar:
2350266428
ORCID evidence
No valid ORCID is stored.
Observed aliases (1)
- Reza E Rabbi Shawon (semantic scholar, provider refresh)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Organizational Efficiency: 1 paper
- Decision Quality: 1 paper
- Adoption Rate: 1 paper
- Output Quality: 1 paper
Papers in the Semantic Scholar view
Latest stored Semantic Scholar author observations only. Citation counts below are from the same provider and are not combined with other services.
Scroll the table horizontally to see every column.
| Paper | Author evidence | Date | Provider citations |
|---|---|---|---|
| Better forecasts alone do not solve inventory problems: conservative policy and cost calibration matter more for fill rates than marginal gains from advanced ML forecasting. In tests on a high-volume U.S. SKU, simple exponential smoothing matched or outperformed an ML point model, while LightGBM quantiles added value through uncertainty estimates but did not by themselves reduce stockouts under realistic (s,S) policies.openalex | Reza E Rabbi Shawon provider id |
2026-01-23 | 0 |
Citation observation summary
Semantic Scholar supplied counts for 1 of 1 papers in this view; 0 are missing. The observed paper counts sum to 0 cumulative citations. This is a coverage summary, not an author score or h-index.