README.md

---
license: cc-by-4.0
task_categories:
  - tabular-classification
  - tabular-regression
language:
  - en
tags:
  - healthcare
  - supply-chain
  - warehouse
  - inventory
  - storage
  - GDP
  - FEFO
  - wastage
  - central-medical-store
  - sub-saharan-africa
  - lmic
pretty_name: "Warehouse & Inventory Management (Inventory Accuracy, Storage Conditions, Wastage, FEFO Compliance)"
size_categories:
  - 10K<n<100K
configs:
  - config_name: national_central_medical_store
    data_files: data/warehouse_national_central_medical_store.csv
  - config_name: regional_warehouse
    data_files: data/warehouse_regional_warehouse.csv
    default: true
  - config_name: district_store
    data_files: data/warehouse_district_store.csv
---

# Warehouse & Inventory Management Dataset

## Abstract

This dataset provides **30,000 simulated warehouse-level observations** (10,000 per scenario) of health commodity storage, inventory management, and warehousing performance across three tiers of the pharmaceutical supply chain in sub-Saharan Africa. Each record represents one commodity category assessed at one warehouse during one monthly period. The dataset captures 40+ variables spanning warehouse infrastructure, storage conditions, inventory accuracy, FEFO compliance, order fulfilment, wastage (expiry + damage), capacity utilisation, temperature excursions, pest damage, theft, and downstream facility impact. Three scenarios: national CMS (82% inventory accuracy), regional warehouse (55%), district store (28%).

**This dataset is entirely simulated. It must not be used for warehouse operations or procurement decisions.**

## 1. Introduction

### 1.1 Warehouse Management in Health Supply Chains

Warehousing is the critical link between procurement and last-mile distribution. USAID GHSC-PSM has documented that effective warehouse management — including proper storage conditions, inventory accuracy, and FEFO (First Expiry, First Out) compliance — directly impacts commodity availability at health facilities.

### 1.2 Storage Conditions

WHO Good Distribution Practices (GDP) require controlled temperature, humidity, pest management, and security for pharmaceutical storage. However, UNICEF Supply Division assessments indicate that only 40-60% of SSA warehouses meet WHO GDP standards, with district-level stores frequently lacking basic infrastructure including temperature monitoring, generator backup, and pest control.

### 1.3 Inventory Accuracy and Wastage

Stock record discrepancies between physical counts and records are widespread, with inventory accuracy as low as 25-30% at district stores. Wastage from expired and damaged stock reaches 15-30% at sub-national levels, representing significant financial losses and contributing to downstream stockouts.

### 1.4 Rationale

This dataset integrates warehouse infrastructure, storage quality, inventory management performance, and downstream impact indicators for supply chain optimization research and warehouse management system development.

## 2. Methodology

### 2.1 Parameterization

| Parameter | National CMS | Regional WH | District Store | Source |
| --- | --- | --- | --- | --- |
| Inventory accuracy | 82% | 55% | 28% | JSI/SIAPS assessments |
| Order fulfilment | 78% | 52% | 30% | GHSC-PSM data |
| Wastage rate | 8% | 18% | 30% | Warehouse audits |
| Storage adequate | 75% | 42% | 15% | UNICEF assessments |
| FEFO compliance | 70% | 35% | 10% | WHO GDP audits |
| Capacity utilisation | 85% | 65% | 40% | Infrastructure data |

### 2.2 Commodity Categories

12 categories: essential medicines, ARVs, vaccines (cold chain), laboratory reagents (cold chain), contraceptives, malaria commodities, IV fluids, PPE/IPC supplies, surgical supplies, nutrition commodities, medical device consumables, controlled substances (secure storage).

## 3. Schema

| Column | Type | Description |
| --- | --- | --- |
| warehouse_level | categorical | national_CMS / regional_warehouse / district_store |
| warehouse_size_sqm | int | Storage area in square metres |
| commodity_category | categorical | 12 commodity categories |
| storage_requirement | categorical | ambient / cold_chain_2_8C / secure_ambient |
| criticality | categorical | critical / high / medium / low |
| has_WMS | binary | Warehouse management system |
| has_temperature_monitoring | binary | Temperature monitoring |
| has_generator_backup | binary | Backup power |
| storage_conditions_adequate | binary | Meets GDP standards |
| inventory_accuracy_pct | float | Physical vs record match |
| stock_record_up_to_date | binary | Records current |
| fefo_compliance | binary | FEFO practiced |
| order_fulfilment_rate_pct | float | Orders fulfilled completely |
| orders_backordered | int | Unfulfilled orders |
| wastage_rate_pct | float | Expired + damaged rate |
| expired_stock_value_usd | float | Value of expired stock |
| capacity_utilisation_pct | float | Space used |
| temperature_excursion_month | int | Cold chain breaks |
| pest_damage_reported | binary | Pest damage |
| theft_reported | binary | Theft/pilferage |
| inventory_issue | categorical | 11 issue categories |
| stockout_at_warehouse | binary | Warehouse-level stockout |
| facilities_affected_by_stockout | int | Downstream facilities impacted |

## 4. Validation

<p align="center">
  <img src="validation_report.png" alt="Validation Report" width="100%">
</p>

## 5. Usage

```python
from datasets import load_dataset

dataset = load_dataset(
    "electricsheepafrica/warehouse-inventory-management",
    "regional_warehouse"
)
df = dataset["train"].to_pandas()

# Wastage by commodity category
print(df.groupby('commodity_category')['wastage_rate_pct'].mean().sort_values(ascending=False))
```

## 6. Limitations

- **Simulated**: Not from real WMS data or warehouse audits.
- **No seasonal effects**: Humidity/temperature seasonal variation not modelled.
- **Simplified costing**: Wastage costs are estimates, not actual financial records.

## 7. References

1. USAID GHSC-PSM. Warehouse management best practices.
2. WHO (2014). Good storage and distribution practices (GDP).
3. JSI/SIAPS. Strengthening pharmaceutical supply chains.
4. UNICEF Supply Division. Warehouse capacity assessments.

## Citation

```bibtex
@dataset{esa_warehouse_inventory_2025,
  title   = {Warehouse and Inventory Management Dataset},
  author  = {{Electric Sheep Africa}},
  year    = {2025},
  publisher = {Hugging Face},
  url     = {https://huggingface.co/datasets/electricsheepafrica/warehouse-inventory-management},
  note    = {Simulated dataset. Not for warehouse operations or procurement decisions.}
}
```

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)