Skill: Calculate Inventory Turnover

Domain

retail

Description

Calculates inventory turnover metrics and identifies slow-moving inventory for working capital optimization and markdown planning.

Use Cases

•Inventory efficiency analysis
•Working capital optimization
•Slow-mover identification
•Markdown planning

Proprietary Business Rules

Rule 1: Turnover Calculation

Days of inventory and turns calculation by category.

Rule 2: ABC Classification

Inventory classification by velocity and value.

Rule 3: Aging Analysis

Inventory aging bucket classification.

Rule 4: Markdown Trigger

Automatic markdown recommendation triggers.

Input Parameters

•analysis_id (string): Analysis identifier
•inventory_data (list): Current inventory positions
•sales_data (list): Sales history
•category_info (dict): Category hierarchies
•cost_data (dict): Inventory cost information
•analysis_period (dict): Analysis time range

Output

•overall_turnover (float): Overall inventory turns
•category_turnover (dict): Turnover by category
•slow_movers (list): Slow-moving inventory items
•aging_analysis (dict): Inventory aging breakdown
•recommendations (list): Optimization recommendations

Implementation

The calculation logic is implemented in turnover_calculator.py and references data from CSV files:

•industry_benchmarks.csv - Industry-specific turnover benchmarks
•performance_thresholds.csv - Performance rating thresholds
•abc_classification.csv - ABC inventory classification parameters
•carrying_costs.csv - Inventory carrying cost components
•parameters.csv - Additional configuration parameters

Usage Example

python

from turnover_calculator import calculate_turnover

result = calculate_turnover(
    analysis_id="INV-001",
    inventory_data=[{"sku": "SKU-001", "units": 500, "cost": 10000}],
    sales_data=[{"sku": "SKU-001", "units_sold": 100, "period": "2025-12"}],
    category_info={"SKU-001": {"category": "apparel", "subcategory": "tops"}},
    cost_data={"avg_cost_method": "weighted_average"},
    analysis_period={"start": "2025-01-01", "end": "2025-12-31"}
)

print(f"Overall Turnover: {result['overall_turnover']} turns")

Test Execution