Automating Inventory Management for Clothing Stores with Python & MongoDB

Track Stock Levels and Product Availability in Real-Time

Code Example:

from pymongo import MongoClient
import pandas as pd

# Connect to MongoDB
client = MongoClient('mongodb://localhost:27017/')
db = client['clothing_store']
inventory = db['inventory']

# Fetch the current inventory data from MongoDB
inventory_data = pd.DataFrame(list(inventory.find()))

# Display the inventory data
print(inventory_data[['product_name', 'stock_level']])

# Example of tracking stock levels
def check_stock(product_id):
    product = inventory.find_one({'product_id': product_id})
    if product and product['stock_level'] < 10:  # Alert when stock is low
        return f"Stock for {product['product_name']} is low, replenish soon."
    return "Stock level is sufficient."

# Check stock level for a specific product
product_id = '12345'
print(check_stock(product_id))

Automate Stock Replenishment Alerts Based on Demand

Code Example:

import numpy as np

# Simple demand forecasting model using past sales data
def forecast_demand(sales_data):
    sales_data['forecast'] = sales_data['sales'].rolling(window=7).mean()  # 7-day moving average
    return sales_data

# Sample sales data (daily sales for the past month)
sales_data = pd.DataFrame({
    'date': pd.date_range(start='2023-01-01', periods=30, freq='D'),
    'sales': np.random.randint(10, 50, size=30)
})

# Apply demand forecasting
sales_forecast = forecast_demand(sales_data)
print(sales_forecast[['date', 'sales', 'forecast']])

# Replenishment decision: If forecasted demand for next 7 days is high, create order
def replenish_stock(product_id):
    forecasted_sales = sales_forecast['forecast'].iloc[-1]  # Last forecasted value
    if forecasted_sales > 30:  # Threshold for placing an order
        order_quantity = 50  # Example order quantity
        print(f"Stock for product {product_id} is low. Replenish by ordering {order_quantity} units.")
    else:
        print(f"Stock for product {product_id} is sufficient. No order needed.")

# Trigger replenishment for a product
replenish_stock('12345')

Optimize Inventory with AI-Driven Insights and Save Time

Code Example:

from sklearn.linear_model import LinearRegression

# Use Linear Regression for sales trend prediction
def predict_sales_trend(sales_data):
    sales_data['day'] = sales_data['date'].dt.dayofyear
    X = sales_data[['day']]  # Feature: day of the year
    y = sales_data['sales']  # Target: sales

    model = LinearRegression()
    model.fit(X, y)

    # Predict next day's sales
    next_day = np.array([[sales_data['day'].max() + 1]])  # Next day prediction
    predicted_sales = model.predict(next_day)
    print(f"Predicted sales for next day: {predicted_sales[0]:.2f} units")

# Apply sales trend prediction
predict_sales_trend(sales_data)

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Why Choose Lillqvist Strat?

By leveraging Python, MongoDB, and Pandas, Lillqvist Strat helps clothing stores automate inventory management, eliminate stockouts, and optimize replenishment strategies using real-time data and AI-powered insights. With our tailored solutions, you can save time and improve decision-making efficiency, ensuring your stock levels align with demand while reducing manual errors.

Let Lillqvist Strat handle your backend automation so you can focus on growing your store.