inventorykit 1.0.0

Custom inventory management library for FreshShelves - stock analysis, expiry predictions, and reorder recommendations

InventoryKit - Custom Inventory Management Library

Version: 1.0.0
Author: Harshitha (NCI Cloud Computing Student)
Purpose: Object-oriented library for FreshShelves inventory calculations

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Overview

InventoryKit is a comprehensive, object-oriented Python library designed specifically for inventory management in the FreshShelves application. It provides advanced algorithms for stock analysis, expiry predictions, inventory valuation, and optimization.

Features

✅ Object-Oriented Design - Clean class hierarchy with SOLID principles
✅ Stock Analysis - Calculate stock status, reorder points, days of stock
✅ Expiry Management - Analyze expiry dates, calculate waste risk
✅ Inventory Valuation - Calculate total value, turnover rates, carrying costs
✅ Optimization - Generate purchasing plans, reorder recommendations
✅ Type Hints - Full type annotation for IDE support
✅ Enums - Type-safe status enumerations
✅ Dataclasses - Structured data models

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Installation

# Install from local directory
pip install -e ./inventorykit

# Or in requirements.txt
-e ./inventorykit

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Architecture

Class Hierarchy

InventoryKit
├── StockAnalyzer (Stock Level Analysis)
├── ExpiryAnalyzer (Expiry Date Analysis)
├── InventoryValuator (Financial Calculations)
└── InventoryOptimizer (Comprehensive Analysis)

Enums

• StockStatus: CRITICAL, LOW, ADEQUATE, OVERSTOCKED
• ExpiryStatus: EXPIRED, CRITICAL, WARNING, CAUTION, FRESH

Data Classes

• Product: Product data model
• InventoryMetrics: Analysis results model

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Usage Examples

1. Stock Analysis

from inventorykit import StockAnalyzer, StockStatus

analyzer = StockAnalyzer(safety_factor=1.2)

# Check stock status
status = analyzer.calculate_stock_status(
    current=15,
    minimum=50
)
print(status)  # StockStatus.CRITICAL

# Calculate reorder quantity
reorder_qty = analyzer.calculate_reorder_quantity(
    current=15,
    minimum=50,
    average_daily_usage=10,
    lead_time_days=7
)
print(f"Reorder: {reorder_qty} units")

# Calculate days of stock
days = analyzer.calculate_days_of_stock(
    current=100,
    average_daily_usage=10
)
print(f"Stock lasts: {days} days")

2. Expiry Analysis

from inventorykit import ExpiryAnalyzer, ExpiryStatus

analyzer = ExpiryAnalyzer()

# Check expiry status
status, days = analyzer.calculate_expiry_status("2026-04-15")
print(f"Status: {status}, Days: {days}")

# Calculate waste risk
risk_score = analyzer.calculate_waste_risk_score(
    expiry_date="2026-04-20",
    quantity=100,
    average_daily_usage=5
)
print(f"Waste risk: {risk_score}%")

3. Inventory Valuation

from inventorykit import InventoryValuator

valuator = InventoryValuator()

products = [
    {'product_id': '1', 'quantity': 100},
    {'product_id': '2', 'quantity': 50}
]

price_map = {'1': 10.0, '2': 15.0}

# Calculate total value
total = valuator.calculate_total_value(products, price_map)
print(f"Total inventory value: ${total}")

# Calculate turnover rate
turnover = valuator.calculate_turnover_rate(
    sales_quantity=500,
    average_inventory=100
)
print(f"Turnover rate: {turnover}")

# Calculate carrying cost
carrying_cost = valuator.calculate_carrying_cost(
    total_value=10000,
    carrying_cost_percentage=0.25
)
print(f"Annual carrying cost: ${carrying_cost}")

4. Comprehensive Analysis

from inventorykit import InventoryOptimizer

optimizer = InventoryOptimizer()

products = [
    {
        'product_id': '1',
        'name': 'Milk',
        'quantity': 20,
        'minimum_stock': 50,
        'expiry_date': '2026-04-15'
    },
    {
        'product_id': '2',
        'name': 'Bread',
        'quantity': 80,
        'minimum_stock': 30,
        'expiry_date': '2026-04-12'
    }
]

usage_data = {
    '1': 10,  # 10 units per day
    '2': 5    # 5 units per day
}

# Analyze inventory
metrics = optimizer.analyze_inventory(products, usage_data)

print(f"Total value: ${metrics.total_value}")
print(f"Low stock items: {metrics.low_stock_items}")
print(f"Expiring items: {metrics.expiring_items}")
print(f"Turnover rate: {metrics.turnover_rate}")
print(f"Reorder recommendations: {len(metrics.reorder_recommendations)}")

# Generate purchasing plan
price_map = {'1': 5.0, '2': 3.0}

plan = optimizer.generate_purchasing_plan(
    products=products,
    budget=500,
    usage_data=usage_data,
    price_map=price_map
)

for item in plan:
    print(f"Order {item['quantity_to_order']} units of {item['product_name']}")
    print(f"  Cost: ${item['total_cost']}, Priority: {item['priority']}")

5. Quick Analysis

from inventorykit import quick_inventory_analysis

products = [...]  # Your product list

results = quick_inventory_analysis(products)

print(f"Health score: {results['health_score']}/100")
print(f"Total items: {results['total_items']}")
print(f"Low stock: {results['low_stock_count']}")
print(f"Expiring: {results['expiring_count']}")

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API Reference

StockAnalyzer

Methods:

• calculate_stock_status(current, minimum) -> StockStatus
• calculate_reorder_quantity(current, minimum, avg_daily_usage, lead_time_days) -> float
• calculate_days_of_stock(current, avg_daily_usage) -> float

ExpiryAnalyzer

Methods:

• calculate_expiry_status(expiry_date) -> Tuple[ExpiryStatus, int]
• calculate_waste_risk_score(expiry_date, quantity, avg_daily_usage) -> float

InventoryValuator

Methods:

• calculate_total_value(products, price_map) -> float
• calculate_turnover_rate(sales_quantity, average_inventory) -> float
• calculate_carrying_cost(total_value, carrying_cost_percentage) -> float

InventoryOptimizer

Methods:

• analyze_inventory(products, usage_data) -> InventoryMetrics
• generate_purchasing_plan(products, budget, usage_data, price_map) -> List[Dict]

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Design Patterns

1. Strategy Pattern

Different analysis strategies encapsulated in separate classes.

2. Facade Pattern

InventoryOptimizer provides a simple interface to complex subsystems.

3. Data Class Pattern

Immutable data structures for type safety.

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Testing

# Run tests (when available)
pytest inventorykit/tests/

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Integration with FreshShelves

# In aws_services.py
from inventorykit import InventoryOptimizer

optimizer = InventoryOptimizer()

def get_dashboard_stats(user_id):
    products = get_all_products(user_id)
    metrics = optimizer.analyze_inventory(products)
    
    return {
        'total_products': metrics.total_items,
        'low_stock_count': metrics.low_stock_items,
        'expiring_soon_count': metrics.expiring_items,
        'inventory_value': metrics.total_value,
        'turnover_rate': metrics.turnover_rate
    }

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Why This Library?

Academic Requirements

✅ Object-Oriented Design - Required by project rubric
✅ Reusable - Can be used in other inventory projects
✅ Well-Documented - Comprehensive documentation
✅ Type-Safe - Full type hints for static analysis

Technical Benefits

✅ Separation of Concerns - Business logic separated from AWS services
✅ Testable - Pure functions, no external dependencies
✅ Extensible - Easy to add new analyzers
✅ Performance - Efficient algorithms for calculations

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Future Enhancements

• Machine learning for demand forecasting
• ABC analysis for inventory classification
• Multi-warehouse support
• Batch processing for large inventories
• Real-time optimization algorithms

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License

Educational Project - NCI Cloud Computing Module

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Author

Harshitha
PG Cloud Computing Student
National College of Ireland (NCI) Dublin

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This library fulfills the 15% "Library Creation" requirement of the project rubric.