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README.md

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+---
+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/)

generate_dataset.py

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+#!/usr/bin/env python3
+"""
+Literature-Informed Warehouse & Inventory Management Dataset
+==============================================================
+
+Each record = ONE warehouse/store observation for ONE commodity
+category during ONE monthly reporting period.
+
+Sources:
+[1] USAID GHSC-PSM. Warehouse management best practices. Storage
+    conditions, inventory accuracy, order fulfilment.
+[2] WHO (2014). Good storage and distribution practices. Temperature,
+    humidity, pest control, FEFO compliance.
+[3] JSI/SIAPS. Strengthening pharmaceutical supply chains.
+    Inventory accuracy, stock record discrepancies.
+[4] UNICEF Supply Division. Warehouse capacity assessments.
+    Only 40-60% of SSA warehouses meet WHO GDP standards.
+"""
+
+import numpy as np
+import pandas as pd
+import argparse
+import os
+
+COMMODITY_CATEGORIES = [
+    ('essential_medicines', 'ambient', 'high', 0.35),
+    ('ARVs', 'ambient', 'high', 0.15),
+    ('vaccines', 'cold_chain_2_8C', 'critical', 0.08),
+    ('laboratory_reagents', 'cold_chain_2_8C', 'medium', 0.05),
+    ('contraceptives', 'ambient', 'medium', 0.08),
+    ('malaria_commodities', 'ambient', 'high', 0.10),
+    ('IV_fluids', 'ambient', 'high', 0.05),
+    ('PPE_IPC_supplies', 'ambient', 'medium', 0.04),
+    ('surgical_supplies', 'ambient', 'medium', 0.03),
+    ('nutrition_commodities', 'ambient', 'medium', 0.03),
+    ('medical_devices_consumables', 'ambient', 'low', 0.02),
+    ('controlled_substances', 'secure_ambient', 'critical', 0.02),
+]
+
+INVENTORY_ISSUES = [
+    'stock_record_discrepancy', 'expired_stock', 'damaged_goods',
+    'theft_pilferage', 'pest_infestation', 'temperature_excursion',
+    'overstocking', 'understocking', 'FEFO_not_followed',
+    'receiving_error', 'dispatch_error',
+]
+
+SCENARIOS = {
+    'national_central_medical_store': {
+        'warehouse_level': 'national_CMS',
+        'warehouse_size_sqm': 5000,
+        'has_WMS': True,
+        'has_temperature_monitoring': True,
+        'has_generator_backup': True,
+        'has_pest_control': True,
+        'has_security_system': True,
+        'staff_count': 40,
+        'inventory_accuracy_rate': 0.82,
+        'order_fulfilment_rate': 0.78,
+        'wastage_rate': 0.08,
+        'storage_conditions_adequate': 0.75,
+        'fefo_compliance': 0.70,
+        'stock_record_up_to_date': 0.80,
+        'capacity_utilisation': 0.85,
+    },
+    'regional_warehouse': {
+        'warehouse_level': 'regional_warehouse',
+        'warehouse_size_sqm': 1500,
+        'has_WMS': False,
+        'has_temperature_monitoring': False,
+        'has_generator_backup': False,
+        'has_pest_control': False,
+        'has_security_system': False,
+        'staff_count': 12,
+        'inventory_accuracy_rate': 0.55,
+        'order_fulfilment_rate': 0.52,
+        'wastage_rate': 0.18,
+        'storage_conditions_adequate': 0.42,
+        'fefo_compliance': 0.35,
+        'stock_record_up_to_date': 0.45,
+        'capacity_utilisation': 0.65,
+    },
+    'district_store': {
+        'warehouse_level': 'district_store',
+        'warehouse_size_sqm': 200,
+        'has_WMS': False,
+        'has_temperature_monitoring': False,
+        'has_generator_backup': False,
+        'has_pest_control': False,
+        'has_security_system': False,
+        'staff_count': 3,
+        'inventory_accuracy_rate': 0.28,
+        'order_fulfilment_rate': 0.30,
+        'wastage_rate': 0.30,
+        'storage_conditions_adequate': 0.15,
+        'fefo_compliance': 0.10,
+        'stock_record_up_to_date': 0.15,
+        'capacity_utilisation': 0.40,
+    },
+}
+
+
+def generate_dataset(n=10000, seed=42, scenario='regional_warehouse'):
+    rng = np.random.default_rng(seed)
+    sc = SCENARIOS[scenario]
+    records = []
+    n_cat = len(COMMODITY_CATEGORIES)
+
+    for idx in range(n):
+        rec = {'id': idx + 1}
+        rec['warehouse_level'] = sc['warehouse_level']
+        rec['warehouse_id'] = f"WH_{rng.integers(1, 100):04d}"
+        rec['warehouse_size_sqm'] = max(20, int(rng.normal(
+            sc['warehouse_size_sqm'], sc['warehouse_size_sqm'] * 0.3)))
+        rec['region_type'] = rng.choice(['urban', 'peri_urban', 'rural'],
+            p=[0.05, 0.15, 0.80] if scenario == 'district_store'
+            else ([0.60, 0.25, 0.15] if scenario == 'national_central_medical_store'
+                  else [0.25, 0.40, 0.35]))
+        rec['has_WMS'] = 1 if sc['has_WMS'] else (1 if rng.random() < 0.05 else 0)
+        rec['has_temperature_monitoring'] = 1 if sc['has_temperature_monitoring'] else (
+            1 if rng.random() < 0.08 else 0)
+        rec['has_generator_backup'] = 1 if sc['has_generator_backup'] else (
+            1 if rng.random() < 0.10 else 0)
+        rec['has_pest_control'] = 1 if sc['has_pest_control'] else (
+            1 if rng.random() < 0.08 else 0)
+        rec['has_security_system'] = 1 if sc['has_security_system'] else (
+            1 if rng.random() < 0.05 else 0)
+        rec['staff_count'] = max(1, int(rng.poisson(sc['staff_count'])))
+        rec['staff_trained_gdp'] = 1 if rng.random() < (
+            0.70 if scenario == 'national_central_medical_store' else
+            (0.25 if scenario == 'regional_warehouse' else 0.05)) else 0
+
+        cat_idx = idx % n_cat
+        cat = COMMODITY_CATEGORIES[cat_idx]
+        rec['commodity_category'] = cat[0]
+        rec['storage_requirement'] = cat[1]
+        rec['criticality'] = cat[2]
+        rec['volume_share_pct'] = cat[3] * 100
+
+        rec['year'] = rng.choice([2021, 2022, 2023, 2024], p=[0.15, 0.25, 0.30, 0.30])
+        rec['month'] = rng.integers(1, 13)
+
+        rec['storage_conditions_adequate'] = 1 if rng.random() < sc['storage_conditions_adequate'] else 0
+        if cat[1] in ('cold_chain_2_8C',) and not rec['has_temperature_monitoring']:
+            rec['storage_conditions_adequate'] = 1 if rng.random() < 0.10 else 0
+        if cat[1] == 'secure_ambient' and not rec['has_security_system']:
+            rec['storage_conditions_adequate'] = 1 if rng.random() < 0.15 else 0
+
+        rec['inventory_accuracy_pct'] = round(np.clip(
+            rng.normal(sc['inventory_accuracy_rate'] * 100, 12), 10, 100), 1)
+        rec['stock_record_up_to_date'] = 1 if rng.random() < sc['stock_record_up_to_date'] else 0
+        rec['fefo_compliance'] = 1 if rng.random() < sc['fefo_compliance'] else 0
+
+        rec['order_fulfilment_rate_pct'] = round(np.clip(
+            rng.normal(sc['order_fulfilment_rate'] * 100, 15), 5, 100), 1)
+        rec['orders_received_month'] = max(0, int(rng.poisson(
+            50 if scenario == 'national_central_medical_store' else
+            (15 if scenario == 'regional_warehouse' else 5))))
+        rec['orders_fulfilled_complete'] = max(0, min(rec['orders_received_month'],
+            int(rec['orders_received_month'] * rec['order_fulfilment_rate_pct'] / 100)))
+        rec['orders_backordered'] = rec['orders_received_month'] - rec['orders_fulfilled_complete']
+
+        rec['wastage_rate_pct'] = round(np.clip(
+            rng.normal(sc['wastage_rate'] * 100, 5), 0.5, 50), 1)
+        rec['expired_stock_value_usd'] = max(0, round(rng.exponential(
+            5000 if scenario == 'national_central_medical_store' else
+            (1000 if scenario == 'regional_warehouse' else 100)), 0))
+        rec['damaged_goods_value_usd'] = max(0, round(rng.exponential(
+            500 if scenario == 'national_central_medical_store' else
+            (200 if scenario == 'regional_warehouse' else 30)), 0))
+
+        rec['capacity_utilisation_pct'] = round(np.clip(
+            rng.normal(sc['capacity_utilisation'] * 100, 15), 5, 110), 1)
+        rec['overflow_to_other_space'] = 1 if rec['capacity_utilisation_pct'] > 95 else 0
+
+        rec['temperature_excursion_month'] = 0
+        if cat[1] in ('cold_chain_2_8C',):
+            rec['temperature_excursion_month'] = max(0, int(rng.poisson(
+                0.5 if scenario == 'national_central_medical_store' else
+                (2 if scenario == 'regional_warehouse' else 5))))
+        rec['pest_damage_reported'] = 1 if rng.random() < (
+            0.03 if scenario == 'national_central_medical_store' else
+            (0.12 if scenario == 'regional_warehouse' else 0.25)) else 0
+        rec['theft_reported'] = 1 if rng.random() < (
+            0.02 if scenario == 'national_central_medical_store' else
+            (0.05 if scenario == 'regional_warehouse' else 0.10)) else 0
+
+        rec['inventory_issue'] = 'none'
+        if rec['inventory_accuracy_pct'] < 80 or rec['wastage_rate_pct'] > 15:
+            if scenario == 'district_store':
+                issue_p = [0.15, 0.18, 0.08, 0.08, 0.05, 0.05, 0.08, 0.12, 0.10, 0.05, 0.06]
+            elif scenario == 'regional_warehouse':
+                issue_p = [0.15, 0.15, 0.08, 0.05, 0.08, 0.08, 0.10, 0.10, 0.08, 0.06, 0.07]
+            else:
+                issue_p = [0.12, 0.10, 0.08, 0.05, 0.05, 0.05, 0.15, 0.10, 0.10, 0.10, 0.10]
+            rec['inventory_issue'] = rng.choice(INVENTORY_ISSUES, p=issue_p)
+
+        rec['stockout_at_warehouse'] = 1 if rng.random() < (
+            0.10 if scenario == 'national_central_medical_store' else
+            (0.30 if scenario == 'regional_warehouse' else 0.55)) else 0
+        rec['facilities_affected_by_stockout'] = 0
+        if rec['stockout_at_warehouse']:
+            rec['facilities_affected_by_stockout'] = max(0, int(rng.exponential(
+                20 if scenario == 'national_central_medical_store' else
+                (8 if scenario == 'regional_warehouse' else 2))))
+
+        rec['report_submitted'] = 1 if rng.random() < (
+            0.90 if scenario == 'national_central_medical_store' else
+            (0.50 if scenario == 'regional_warehouse' else 0.15)) else 0
+
+        records.append(rec)
+
+    df = pd.DataFrame(records)
+    print(f"\n{'='*65}")
+    print(f"Warehouse & Inventory — {scenario} (n={n}, seed={seed})")
+    print(f"{'='*65}")
+    print(f"  Inventory accuracy: {df['inventory_accuracy_pct'].mean():.1f}%")
+    print(f"  Order fulfilment: {df['order_fulfilment_rate_pct'].mean():.1f}%")
+    print(f"  Wastage rate: {df['wastage_rate_pct'].mean():.1f}%")
+    print(f"  Storage adequate: {df['storage_conditions_adequate'].mean()*100:.1f}%")
+    print(f"  FEFO compliance: {df['fefo_compliance'].mean()*100:.1f}%")
+    return df
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--all-scenarios', action='store_true')
+    parser.add_argument('--n', type=int, default=10000)
+    parser.add_argument('--seed', type=int, default=42)
+    args = parser.parse_args()
+    os.makedirs('data', exist_ok=True)
+    if args.all_scenarios:
+        for sc in SCENARIOS:
+            df = generate_dataset(n=args.n, seed=args.seed, scenario=sc)
+            df.to_csv(os.path.join('data', f'warehouse_{sc}.csv'), index=False)
+            print(f"  -> Saved\n")
+    else:
+        df = generate_dataset(n=args.n, seed=args.seed)
+        df.to_csv(os.path.join('data', 'warehouse_regional_warehouse.csv'), index=False)

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+#!/usr/bin/env python3
+"""Validation & Diagnostic Visualization for Warehouse & Inventory Management Dataset."""
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+SCENARIOS = ['national_central_medical_store', 'regional_warehouse', 'district_store']
+
+
+def load_scenarios(data_dir='data'):
+    dfs = {}
+    for sc in SCENARIOS:
+        path = os.path.join(data_dir, f'warehouse_{sc}.csv')
+        if os.path.exists(path):
+            dfs[sc] = pd.read_csv(path)
+    return dfs
+
+
+def make_report(dfs, output='validation_report.png'):
+    fig, axes = plt.subplots(4, 2, figsize=(16, 24))
+    fig.suptitle(
+        'Warehouse & Inventory Management — Validation Report\n'
+        '(National CMS → Regional Warehouse → District Store)',
+        fontsize=15, fontweight='bold', y=0.99)
+    colors = ['#2ecc71', '#f39c12', '#e74c3c']
+    x = np.arange(len(SCENARIOS))
+    labels = ['National CMS', 'Regional WH', 'District Store']
+
+    ax = axes[0, 0]
+    inv = [dfs[sc]['inventory_accuracy_pct'].mean() for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, inv, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(inv):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Accuracy (%)'); ax.set_title('Inventory Accuracy'); ax.set_ylim(0,100)
+
+    ax = axes[0, 1]
+    ofr = [dfs[sc]['order_fulfilment_rate_pct'].mean() for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, ofr, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(ofr):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Fulfilment (%)'); ax.set_title('Order Fulfilment Rate')
+
+    ax = axes[1, 0]
+    waste = [dfs[sc]['wastage_rate_pct'].mean() for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, waste, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(waste):
+        ax.text(i, v+0.5, f'{v:.1f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Wastage (%)'); ax.set_title('Wastage Rate (Expired + Damaged)')
+
+    ax = axes[1, 1]
+    fefo = [dfs[sc]['fefo_compliance'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, fefo, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(fefo):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Compliance (%)'); ax.set_title('FEFO Compliance')
+
+    ax = axes[2, 0]
+    df = dfs.get('regional_warehouse', list(dfs.values())[0])
+    issue_df = df[df['inventory_issue']!='none']
+    if len(issue_df)>0:
+        issues = issue_df['inventory_issue'].value_counts().head(8)
+        ax.barh(range(len(issues)), issues.values, color='#e74c3c', alpha=0.7)
+        ax.set_yticks(range(len(issues)))
+        ax.set_yticklabels([s.replace('_',' ').title() for s in issues.index], fontsize=7)
+        ax.set_xlabel('Count')
+    ax.set_title('Top Inventory Issues (Regional)')
+
+    ax = axes[2, 1]
+    stor = [dfs[sc]['storage_conditions_adequate'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, stor, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(stor):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Storage Conditions Adequate')
+
+    ax = axes[3, 0]
+    cap = [dfs[sc]['capacity_utilisation_pct'].mean() for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, cap, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(cap):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Utilisation (%)'); ax.set_title('Warehouse Capacity Utilisation')
+
+    ax = axes[3, 1]
+    so = [dfs[sc]['stockout_at_warehouse'].mean()*100 for sc in SCENARIOS if sc in dfs]
+    ax.bar(x, so, color=colors, alpha=0.8)
+    ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=9)
+    for i, v in enumerate(so):
+        ax.text(i, v+1, f'{v:.0f}%', ha='center', fontsize=10, fontweight='bold')
+    ax.set_ylabel('Rate (%)'); ax.set_title('Stockout at Warehouse Level')
+
+    plt.tight_layout(rect=[0,0,1,0.97])
+    plt.savefig(output, dpi=150, bbox_inches='tight')
+    print(f'Saved validation report to {output}')
+    plt.close()
+
+
+if __name__ == '__main__':
+    dfs = load_scenarios()
+    if dfs:
+        make_report(dfs)