Learn Interpreting Supply Chain KPIs | Supply Chain Data Analysis

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Understanding and interpreting supply chain KPIs is essential for driving operational efficiency and meeting customer expectations. Three of the most common KPIs in supply chain management are inventory turnover, order cycle time, and fill rate.

Inventory turnover measures how many times inventory is sold and replaced over a specific period;
Order cycle time tracks the average time it takes from receiving an order to delivering it to the customer;
Fill rate quantifies the percentage of customer demand that is met without stockouts.

These metrics matter because they help you identify inefficiencies, optimize inventory levels, and improve service levels. High inventory turnover can indicate efficient sales and inventory management, while a low order cycle time suggests fast, responsive fulfillment. A high fill rate demonstrates the ability to meet customer demand reliably, which is crucial for customer satisfaction and retention.


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import pandas as pd

# Example sales and inventory data
sales = 12000  # total units sold in a year
average_inventory = 3000  # average inventory units held

# Calculate inventory turnover ratio
inventory_turnover = sales / average_inventory

print("Inventory Turnover Ratio:", inventory_turnover)

The inventory turnover ratio is calculated as the total units sold divided by the average inventory held during the same period. In the code above, you use the formula inventory_turnover = sales / average_inventory, where sales represents the total number of units sold (12,000) and average_inventory is the average number of units in stock (3,000). The resulting inventory turnover ratio tells you how many times you sold and replaced your inventory in that year. A higher ratio generally signals efficient inventory management, while a lower ratio may suggest excess stock or slow-moving products.


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import pandas as pd

# Example order and delivery dates
df = pd.DataFrame({
    "order_date": pd.to_datetime(["2024-06-01", "2024-06-03", "2024-06-05"]),
    "delivered_date": pd.to_datetime(["2024-06-04", "2024-06-08", "2024-06-07"])
})

# Calculate order cycle time in days
df["order_cycle_time"] = (df["delivered_date"] - df["order_date"]).dt.days

print(df[["order_date", "delivered_date", "order_cycle_time"]])

1. Which KPI measures how quickly inventory is sold and replaced?

2. What does a high fill rate indicate about a supply chain?

3. Fill in the blank: To calculate the difference between two pandas datetime columns, use df['delivered'] - df['____'].

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Section 1. Chapter 6

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Section 1. Chapter 6