Skill: Optimize Retail Inventory

Domain

retail

Description

Analyzes sales patterns, stock levels, and demand forecasts to optimize inventory allocation and replenishment across retail locations.

Tags

retail, inventory-management, demand-forecasting, supply-chain, stock-optimization

Use Cases

• •Inventory replenishment planning
• •Stock allocation optimization
• •Markdown timing decisions
• •Safety stock calculation

Proprietary Business Rules

Rule 1: Demand Forecasting

Proprietary demand model using historical sales, seasonality, and promotional calendars.

Rule 2: Safety Stock Calculation

Dynamic safety stock based on demand variability and service level targets.

Rule 3: Store Clustering

Allocation adjustments based on store performance clusters.

Rule 4: Markdown Optimization

Optimal markdown timing based on sell-through velocity.

Input Parameters

• •sku (string): Product SKU
• •store_id (string): Store identifier
• •current_stock (int): Current inventory on hand
• •sales_history (list): Historical daily sales
• •lead_time_days (int): Supplier lead time
• •service_level (float): Target service level (0-1)
• •promotion_planned (bool): Upcoming promotion flag
• •season (string): Current season

Output

• •reorder_recommendation (dict): Quantity and timing
• •safety_stock (int): Recommended safety stock
• •stockout_risk (float): Probability of stockout
• •days_of_supply (int): Current days of supply
• •markdown_recommendation (dict): Markdown timing if applicable

Implementation

The optimization logic is implemented in inventory_optimizer.py and references parameters from CSV files:

• •seasonality_factors.csv - Reference data
• •z_scores.csv - Reference data
• •store_clusters.csv - Reference data
• •markdown_rules.csv - Reference data
• •replenishment_rules.csv - Reference data
• •parameters.csv - Reference data.

Usage Example

from inventory_optimizer import optimize_inventory

result = optimize_inventory(
    sku="SKU-12345",
    store_id="STORE-001",
    current_stock=150,
    sales_history=[12, 15, 10, 18, 14, 16, 11],
    lead_time_days=7,
    service_level=0.95,
    promotion_planned=True,
    season="spring"
)

print(f"Reorder: {result['reorder_recommendation']}")

Test Execution

from inventory_optimizer import optimize_inventory

result = optimize_inventory(
    sku=input_data.get('sku'),
    store_id=input_data.get('store_id'),
    current_stock=input_data.get('current_stock', 0),
    sales_history=input_data.get('sales_history', []),
    lead_time_days=input_data.get('lead_time_days', 7),
    service_level=input_data.get('service_level', 0.95),
    promotion_planned=input_data.get('promotion_planned', False),
    season=input_data.get('season', 'base')
)