AI framework for health supply chain optimization - technical documentation for humanitarian AI systems in Uganda
# AI Framework for Humanitarian Supply Chain Optimization
**Offline-first forecasting, stock visibility, and decision support for low-resource humanitarian settings**
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## Overview
This framework provides a comprehensive approach to optimizing health supply chains in low-resource humanitarian settings. Maintained by Neuravox Foundation, it builds on humanitarian innovation research conducted in Uganda with support from the UK Foreign, Commonwealth & Development Office and the Elrha Humanitarian Innovation Fund. It addresses the unique challenges of Uganda's health system, where **89% of facilities experience unreliable connectivity** and infrastructure constraints directly impact medicine availability.
> [!NOTE]
> The framework is the **foundation phase** of a broader humanitarian AI project now advancing into **tool development** for forecasting, stock visibility, and decision support. Read the project overview at neuravox.org.
## Core Design Principles
| Principle | Implementation |
|-----------|----------------|
| **Offline-First** | All critical functions work without internet connectivity |
| **Human-in-the-Loop** | AI provides recommendations; health workers make the final decisions |
| **Infrastructure-Aware** | Storage capacity is treated as a hard constraint, not a variable |
| **Paper Digital Harmony** | The system augments existing paper workflows |
## Three-Tier Forecasting Architecture
Functionality is placed where it is needed most: the most critical forecasting runs **fully offline at the facility**, while districts and the national level add consolidation and oversight as connectivity allows.
```mermaid
flowchart TB
N[" National · Central Data Hub National reporting & system integration DHIS2 · e-LMIS · eAFYA"]
D[" District Services Tier 2 · Hierarchical Statistical Forecasting (HES) Tier 3 · ML Models (Random Forest / XGBoost)"]
F[" Facility Level · Offline Tier 1 · Rule-Bas …