bakery-ia/services/inventory/tests/test_weighted_average_cost.py

#!/usr/bin/env python3
"""
Test script to demonstrate and verify the weighted average cost calculation
Location: services/inventory/tests/test_weighted_average_cost.py
"""
import sys
from decimal import Decimal


def calculate_weighted_average(current_stock: float, current_avg_cost: float,
                               new_quantity: float, new_unit_cost: float) -> float:
    """
    Calculate weighted average cost - mirrors the implementation in ingredient_repository.py

    Args:
        current_stock: Current stock quantity before purchase
        current_avg_cost: Current average cost per unit
        new_quantity: Quantity being purchased
        new_unit_cost: Unit cost of new purchase

    Returns:
        New average cost per unit
    """
    if current_stock <= 0:
        return new_unit_cost

    total_cost = (current_stock * current_avg_cost) + (new_quantity * new_unit_cost)
    total_quantity = current_stock + new_quantity
    return total_cost / total_quantity


def print_test_case(case_num: int, title: str, current_stock: float, current_avg_cost: float,
                   new_quantity: float, new_unit_cost: float):
    """Print a formatted test case with calculation details"""
    print(f"\nTest Case {case_num}: {title}")
    print("-" * 60)
    print(f"Current Stock: {current_stock} kg @ €{current_avg_cost:.2f}/kg")
    print(f"New Purchase: {new_quantity} kg @ €{new_unit_cost:.2f}/kg")

    new_avg_cost = calculate_weighted_average(current_stock, current_avg_cost,
                                              new_quantity, new_unit_cost)

    if current_stock > 0:
        total_cost = (current_stock * current_avg_cost) + (new_quantity * new_unit_cost)
        total_quantity = current_stock + new_quantity
        print(f"Calculation: ({current_stock} × €{current_avg_cost:.2f} + {new_quantity} × €{new_unit_cost:.2f}) / {total_quantity}")
        print(f"           = (€{current_stock * current_avg_cost:.2f} + €{new_quantity * new_unit_cost:.2f}) / {total_quantity}")
        print(f"           = €{total_cost:.2f} / {total_quantity}")

    print(f"→ New Average Cost: €{new_avg_cost:.2f}/kg")

    return new_avg_cost


def test_weighted_average_calculation():
    """Run comprehensive tests of the weighted average cost calculation"""
    print("=" * 80)
    print("WEIGHTED AVERAGE COST CALCULATION - COMPREHENSIVE TEST SUITE")
    print("=" * 80)

    # Test Case 1: First Purchase (Bootstrap case)
    print_test_case(
        1, "First Purchase (No Existing Stock)",
        current_stock=0,
        current_avg_cost=0,
        new_quantity=100,
        new_unit_cost=5.00
    )

    # Test Case 2: Same Price Purchase
    print_test_case(
        2, "Second Purchase at Same Price",
        current_stock=100,
        current_avg_cost=5.00,
        new_quantity=50,
        new_unit_cost=5.00
    )

    # Test Case 3: Price Increase
    avg_cost = print_test_case(
        3, "Purchase at Higher Price (Inflation)",
        current_stock=150,
        current_avg_cost=5.00,
        new_quantity=50,
        new_unit_cost=6.00
    )

    # Test Case 4: Large Volume Discount
    avg_cost = print_test_case(
        4, "Large Purchase with Volume Discount",
        current_stock=200,
        current_avg_cost=5.25,
        new_quantity=200,
        new_unit_cost=4.50
    )

    # Test Case 5: Small Purchase After Consumption
    avg_cost = print_test_case(
        5, "Purchase After Heavy Consumption",
        current_stock=50,
        current_avg_cost=4.88,
        new_quantity=100,
        new_unit_cost=5.50
    )

    # Test Case 6: Tiny Emergency Purchase
    print_test_case(
        6, "Small Emergency Purchase at Premium Price",
        current_stock=150,
        current_avg_cost=5.29,
        new_quantity=10,
        new_unit_cost=8.00
    )

    # Summary
    print("\n" + "=" * 80)
    print("KEY INSIGHTS")
    print("=" * 80)
    print("""
✓ The weighted average considers both QUANTITY and PRICE:
  - Larger purchases have more impact on the average
  - Smaller purchases have minimal impact

✓ Behavior with price changes:
  - Price increases gradually raise the average (dampened by existing stock)
  - Price decreases gradually lower the average (dampened by existing stock)
  - Volume discounts can significantly lower costs when buying in bulk

✓ Business implications:
  - Encourages bulk purchasing when prices are favorable
  - Protects against price spike impacts (averaged over time)
  - Provides accurate COGS for financial reporting
  - Helps identify procurement opportunities (compare to standard_cost)

✓ Implementation notes:
  - Calculation happens automatically on every stock addition
  - No user intervention required
  - Logged for audit purposes
  - Works with FIFO stock consumption
    """)

    print("=" * 80)
    print("✓ All tests completed successfully!")
    print("=" * 80)


if __name__ == "__main__":
    test_weighted_average_calculation()