The Average Age of Inventory, also known as Inventory Days or Days Sales of Inventory (DSI), is a financial metric that measures the average number of days it takes for a company to sell its entire inventory during a specific period. It provides insights into how efficiently a company manages its inventory, indicating the average time it takes for goods to move through the production and sales cycle.

The formula for calculating the Average Age of Inventory is:

\[ \text{Average Age of Inventory} = \frac{\text{Number of Days in Period}}{\text{Inventory Turnover Ratio}} \]

The components of the formula are:

1. **Number of Days in Period:** This represents the time period for which the calculation is being made (e.g., a quarter or a year).

2. **Inventory Turnover Ratio:** The Inventory Turnover Ratio is calculated as:

\[ \text{Inventory Turnover Ratio} = \frac{\text{Cost of Goods Sold (COGS)}}{\text{Average Inventory}} \]

where COGS is the cost of goods sold during the period, and Average Inventory is the average of the beginning and ending inventory for the same period.

By dividing the number of days in the period by the Inventory Turnover Ratio, the Average Age of Inventory provides an estimate of how many days, on average, it takes for the company to sell its entire inventory.


– A lower Average Age of Inventory suggests that the company is selling its inventory more quickly, which is generally considered favorable as it minimizes holding costs and the risk of obsolescence.

– A higher Average Age of Inventory may indicate slower inventory turnover, potentially signaling overstocking, inefficiencies in production, or challenges in selling products.

It’s important to note that the Average Age of Inventory varies by industry. Different industries may have different norms based on the nature of their products, supply chains, and market demand.

Analyzing changes in the Average Age of Inventory over time can help identify trends and assess the effectiveness of inventory management strategies. However, it is often useful to compare this metric with industry benchmarks or similar companies for a more meaningful interpretation.