rishirajpathak/warehouse-inventory-management

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

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

4. Validation

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

5. Usage

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

Citation

@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