Using Pandas and MongoDB for Automated Demand Forecasting

Introduction

Accurate demand forecasting is essential for businesses to optimize inventory, reduce waste, and increase profitability. By using Pandas and MongoDB, businesses can automate demand forecasting and make smarter decisions.

Lillqvist Strat offers custom solutions that leverage data-driven insights to predict demand and streamline supply chain management.

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1. Demand Forecasting Basics

Demand forecasting involves using historical data to predict future product demand. This helps businesses:

• Anticipate customer demand
• Optimize inventory levels
• Plan production more efficiently

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2. MongoDB Sales Data

Storing Sales Data in MongoDB

First, let’s set up MongoDB to store sales data, which will be used for demand forecasting.

from pymongo import MongoClient

client = MongoClient("mongodb://localhost:27017/")
db = client["sales_forecasting"]
sales = db["sales_data"]

Inserting Sales Data

sales_entry = {
    "product_id": 101,
    "date": "2025-02-01",
    "quantity_sold": 150,
    "price": 19.99
}

sales.insert_one(sales_entry)

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3. Python Forecasting Scripts

Forecasting Using Python

We can use machine learning algorithms, such as ARIMA or Prophet, to predict future demand based on historical data. For this example, we’ll use simple linear regression to forecast demand.

import pandas as pd
from sklearn.linear_model import LinearRegression
import numpy as np

# Load sales data from MongoDB
sales_data = list(sales.find({}, {"_id": 0, "date": 1, "quantity_sold": 1}))
df = pd.DataFrame(sales_data)

# Convert date to datetime and set it as the index
df["date"] = pd.to_datetime(df["date"])
df.set_index("date", inplace=True)

# Prepare features for forecasting
df['day'] = np.arange(len(df))

# Train a linear regression model
model = LinearRegression()
model.fit(df[["day"]], df["quantity_sold"])

# Make future predictions
future_days = np.arange(len(df), len(df) + 7).reshape(-1, 1)
forecast = model.predict(future_days)

# Print the forecasted values for the next 7 days
print(f"Forecast for the next 7 days: {forecast}")

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4. Pandas Trend Analysis

Analyzing Trends with Pandas

After making predictions, it’s important to analyze historical trends and seasonality to improve forecast accuracy. Use Pandas to analyze the data and identify patterns.

# Group data by week and calculate total sales for each week
weekly_sales = df.resample('W').sum()

# Plot sales trend over time
import matplotlib.pyplot as plt

weekly_sales['quantity_sold'].plot()
plt.title("Weekly Sales Trend")
plt.ylabel("Quantity Sold")
plt.show()

Moving Average for Smoothing

# Apply a moving average to smooth out short-term fluctuations
weekly_sales['smooth_sales'] = weekly_sales['quantity_sold'].rolling(window=4).mean()

# Plot the smoothed data
weekly_sales[['quantity_sold', 'smooth_sales']].plot()
plt.title("Smoothed Weekly Sales Trend")
plt.ylabel("Quantity Sold")
plt.show()

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5. Accuracy Tips

Improving Forecast Accuracy

• Use More Advanced Models: Consider using Prophet or ARIMA models for more complex forecasting.
• Data Quality: Ensure your data is clean, with accurate and up-to-date information.
• Seasonality: Always account for seasonality, especially for businesses with fluctuating demand during certain times of the year.
• External Factors: Incorporate external data such as marketing campaigns or weather data to improve forecasts.

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Conclusion

By leveraging Pandas and MongoDB, businesses can automate the process of demand forecasting, reduce overstocking and understocking, and optimize inventory management.

✅ Data-driven demand predictions
✅ Automated forecasting processes
✅ Improved business decision-making

Lillqvist Strat offers tailored demand forecasting solutions to help your business stay ahead of demand and improve operational efficiency. Start forecasting smarter today!