| |
| """ |
| 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) |
|
|
