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
4Unique collaborators
1/1OpenAlex citation coverage
Publication span: 2026. Corpus fetch span: 2026.
Identity provenance
Provider IDs
- Openalex:
A5147745400
ORCID evidence
No valid ORCID is stored.
Observed aliases (2)
- Munashe Naphtali Mupa (openalex, provider refresh)
- Munashe Naphtali Mupa (openalex, source metadata)
Topics and outcomes in this view
Assessment themes
- Adoption: 1 paper
- Productivity: 1 paper
Claim outcomes
- Error Rate: 1 paper
- Output Quality: 1 paper
- Organizational Efficiency: 1 paper
- Task Allocation: 1 paper
Papers in the OpenAlex view
Latest stored OpenAlex 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 |
|---|---|---|---|
| A lightweight POS+ERP demand-sensing system halved the pain of simple heuristics: on a public 10-store, 50-item benchmark, gradient-boosted demand sensing cut simulated stockout incidence by roughly 30% compared with a week-lag rule while keeping order volume essentially unchanged. The paper shows small grocers can gain meaningful availability and working-capital benefits from modest analytics and disciplined feature engineering rather than enterprise AI rollouts.openalex | Munashe Naphtali Mupa provider id |
2026-08-19 | 0 |
Citation observation summary
OpenAlex 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.