Mastering Inventory Projection: A Step-by-Step Guide to Creating PSI with Python Pandas

Accurate inventory projection is the backbone of any successful supply chain management. It enables businesses to make informed decisions, reduce costs, and optimize their inventory levels. In this article, we’ll delve into the world of inventory projection and show you how to create a Projected Stock Inventory (PSI) using Python Pandas, leveraging three different data frames: Inventory, Purchase, and Shipment.

The Importance of Inventory Projection

Before we dive into the nitty-gritty of creating a PSI, it’s essential to understand why inventory projection is crucial for businesses. Here are a few compelling reasons:

• Optimized Inventory Levels**: Accurate projection helps businesses maintain optimal inventory levels, reducing the risk of stockouts and overstocking.
• Cost Savings**: By predicting inventory needs, businesses can avoid costly last-minute purchases, reduce storage costs, and minimize waste.
• Improved Customer Satisfaction**: Inventory projection enables businesses to ensure timely delivery, leading to higher customer satisfaction and loyalty.

Preparing Your Data

For this tutorial, we’ll assume you have three separate data frames: Inventory, Purchase, and Shipment. Each data frame should contain the following columns:

Data Frame	Columns
Inventory	Item ID, Current Stock, Reorder Point, Reorder Quantity
Purchase	Item ID, Purchase Date, Quantity
Shipment	Item ID, Shipment Date, Quantity

Make sure your data is clean, and the columns are correctly formatted. You can use the following Python code to import and view your data frames:

import pandas as pd

inventory_df = pd.read_csv('inventory.csv')
purchase_df = pd.read_csv('purchase.csv')
shipment_df = pd.read_csv('shipment.csv')

print(inventory_df.head())
print(purchase_df.head())
print(shipment_df.head())

Step 1: Merge Data Frames

To create a unified view of your inventory data, you’ll need to merge the Inventory, Purchase, and Shipment data frames. You can use the `merge` function in Pandas to achieve this:

merged_df = pd.merge(inventory_df, purchase_df, on='Item ID', how='left')
merged_df = pd.merge(merged_df, shipment_df, on='Item ID', how='left')

The resulting `merged_df` will contain all the columns from the three original data frames.

Step 2: Calculate Projected Stock Inventory (PSI)

Now that you have a merged data frame, it’s time to calculate the Projected Stock Inventory (PSI). PSI represents the projected stock level at a given point in time, taking into account incoming shipments and outgoing purchases.

Use the following formula to calculate PSI:

merged_df['PSI'] = merged_df['Current Stock'] + merged_df['Quantity_x'] - merged_df['Quantity_y']

In this formula, `Quantity_x` represents the incoming shipments, and `Quantity_y` represents the outgoing purchases.

Step 3: Handle Reorder Points and Quantities

To ensure accurate inventory projection, you need to consider the reorder points and quantities. When the PSI falls below the reorder point, you’ll need to place an order for the reorder quantity.

Use the following code to handle reorder points and quantities:

merged_df['Reorder Flag'] = np.where(merged_df['PSI'] <= merged_df['Reorder Point'], 1, 0)
merged_df['Reorder Quantity'] = np.where(merged_df['Reorder Flag'] == 1, merged_df['Reorder Quantity'], 0)

The `Reorder Flag` column will indicate whether an item needs to be reordered (1) or not (0). The `Reorder Quantity` column will display the quantity to be reordered.

Step 4: Visualize Your Results

Finally, let’s visualize the projected stock inventory using a line chart. This will help you identify trends and patterns in your inventory data:

import matplotlib.pyplot as plt

plt.figure(figsize=(10,6))
plt.plot(merged_df['Item ID'], merged_df['PSI'])
plt.xlabel('Item ID')
plt.ylabel('Projected Stock Inventory')
plt.title('Projected Stock Inventory Over Time')
plt.show()

This chart will display the projected stock inventory for each item over time, enabling you to make informed decisions about your inventory management.

Conclusion

Creating an accurate Projected Stock Inventory (PSI) is crucial for businesses to optimize their inventory levels, reduce costs, and improve customer satisfaction. By following this step-by-step guide, you’ve learned how to merge three different data frames, calculate PSI, handle reorder points and quantities, and visualize your results using Python Pandas.

Remember to regularly update your data and re-run the PSI calculation to ensure accurate inventory projection. With this powerful tool, you’ll be able to make data-driven decisions and take your inventory management to the next level.

Additional Tips and Variations

If you want to take your inventory projection to the next level, consider the following variations:

• Use machine learning algorithms to predict demand: Integrate machine learning algorithms, such as ARIMA or Prophet, to predict demand and improve the accuracy of your PSI calculation.
• Incorporate external factors: Consider incorporating external factors, such as weather data, seasonal trends, or holidays, to improve the accuracy of your PSI calculation.
• Integrate with other systems: Integrate your PSI calculation with other systems, such as ERP or CRM, to automate inventory management and improve overall supply chain efficiency.

By mastering inventory projection and adapting to changing market conditions, you’ll be able to stay ahead of the competition and drive business growth.

Frequently Asked Question

Get ready to master the art of creating inventory projections with Python Pandas!