Summary  
This chapter covers computing aggregate metrics and summarizing categorical data in a tabular dataset using DataFrame operations like mean, filtering, and value_counts, and printing the results.  

General domain of usage  
Supply chain management

Order fulfillment rate is a **key performance metric** in supply chain management that measures the proportion of customer orders successfully delivered as requested. It is defined as the **percentage of orders** that are fully completed and shipped to customers without any issues or delays. This rate is significant because it directly reflects how efficiently a supply chain meets customer demand. **High fulfillment rates** indicate reliable processes and satisfied customers, while **low rates** can signal operational challenges or gaps in service.

import pandas as pd

# Create a DataFrame with order fulfillment information
df = pd.DataFrame({
    "order_id": [101, 102, 103, 104, 105],
    "fulfilled": [True, False, True, True, False]
})

# Calculate the fulfillment rate as the percentage of fulfilled orders
fulfillment_rate = df["fulfilled"].mean() * 100
print(f"Order Fulfillment Rate: {fulfillment_rate:.2f}%")

By calculating the **order fulfillment rate** using the proportion of orders marked as `fulfilled`, you gain immediate insight into supply chain efficiency. If the fulfillment rate is lower than expected, it may indicate bottlenecks such as inventory shortages, delayed shipments, or process errors. Monitoring this metric over time helps identify trends and areas where improvements can boost customer satisfaction and streamline operations.

# Suppose each unfulfilled order has a reason recorded
df["reason"] = ["", "Out of stock", "", "", "Address error"]

# Filter unfulfilled orders
unfulfilled_orders = df[df["fulfilled"] == False]

# Summarize reasons for non-fulfillment
reason_summary = unfulfilled_orders["reason"].value_counts()
print("Reasons for Unfulfilled Orders:")
print(reason_summary)

What does a low order fulfillment rate suggest about a supply chain?

Which pandas function can be used to calculate the mean of a boolean column?

Learn how to leverage Python to solve real-world supply chain problems, from inventory management to demand forecasting and logistics optimization. This course blends practical coding with supply chain theory, using hands-on examples and engaging challenges.

Explore the fundamentals of supply chain data, learn how to represent and analyze it in Python, and gain insights using data visualization.

Delve into inventory control techniques and demand forecasting methods, applying Python to model and predict supply chain needs.

Apply Python to optimize logistics operations, including route planning, shipment scheduling, and cost analysis.

Analyzing Order Fulfillment Rates

1. What does a low order fulfillment rate suggest about a supply chain?

2. Which pandas function can be used to calculate the mean of a boolean column?

3. Fill in the blank: To filter orders where 'fulfilled' is False, use `df[df['fulfilled'] == ____]`.

Analyzing Order Fulfillment Rates

1. What does a low order fulfillment rate suggest about a supply chain?

2. Which pandas function can be used to calculate the mean of a boolean column?

3. Fill in the blank: To filter orders where 'fulfilled' is False, use df[df['fulfilled'] == ____].

3. Fill in the blank: To filter orders where 'fulfilled' is False, use `df[df['fulfilled'] == ____]`.