Days Inventory Outstanding Calculator

Days inventory outstanding (DIO) shows how long inventory sits before it is sold or used. Enter average inventory, COGS, and period length to calculate DIO and turnover.

Compute DIO and turnover in days. Private by design—everything runs locally in your browser.

Inputs

Results

Days inventory outstanding (DIO):
Inventory turnover:
Average inventory:
COGS:
Formula: DIO = (Average Inventory / COGS) * Period Days.

What DIO tells you

Days inventory outstanding (DIO) measures the average number of days inventory remains in your operation before it is sold or consumed. It is the time-based counterpart to inventory turnover. While turnover tells you how many cycles occur in a period, DIO shows the average age of your stock. This makes DIO useful for comparing inventory efficiency across categories, warehouses, or periods, even when sales volumes change.

DIO is calculated using average inventory and COGS rather than sales. Using COGS aligns the metric with inventory valuation and avoids distortion caused by price changes or margin differences. The period length should match your reporting cadence, such as 365 days for annual reporting or 90 days for quarterly analysis. If you run a highly seasonal business, calculate DIO by season to avoid misleading averages that blend peak and off-peak performance.

Interpreting DIO requires context. A lower DIO often indicates efficient inventory management and faster cash conversion. However, a DIO that is too low may signal insufficient buffer stock, putting service levels at risk. For items with long lead times or supply risk, maintaining a higher DIO can be prudent. Similarly, slow-moving or high-margin products may naturally have a higher DIO that is still acceptable. DIO should be evaluated alongside stockouts, service targets, and working capital goals.

Because DIO is expressed in days, it is easy to compare with lead time and replenishment cadence. If DIO is close to or below lead time, you may be exposed to stockouts when demand spikes. If DIO is far above lead time, you may be carrying excess inventory. This calculator provides a quick way to monitor that balance while keeping your cost data private and local to your device.

Many teams pair DIO with service level targets to avoid over-optimizing for low inventory days. A lower DIO can be positive for cash flow, but it can also increase the likelihood of expedited freight or missed sales if demand surges. Reviewing DIO alongside fill rate, backorders, and forecast accuracy helps ensure reductions are sustainable and not simply shifting costs into other parts of the network.

Formula

DIO: (Average Inventory / COGS) * Period Days

Turnover: COGS / Average Inventory

Example calculation

If average inventory is $80,000, COGS is $480,000, and the period is 365 days, DIO is (80,000 / 480,000) * 365 = 60.8 days. The implied turnover is 480,000 / 80,000 = 6.0 turns per year.

FAQs

What is DIO?

Days inventory outstanding measures how many days of inventory you hold on average.

How is DIO related to turnover?

DIO is the inverse of turnover in time units: DIO = period days / turnover.

What period should I use?

Use a consistent period such as 365 days for annual analysis or 90 days for a quarter.

Is this calculator private?

Yes. All calculations are performed locally in your browser.

How it works

This calculator converts average inventory and COGS into DIO and turnover. All computation is client-side for privacy.

5 Fun Facts about DIO

DIO is a working capital lever

Lower DIO can free cash by reducing inventory days on hand.

Cash flow

Seasonality skews averages

High seasonal peaks can inflate average inventory and DIO.

Seasonality

DIO connects to lead time

Comparing DIO to lead time helps assess service risk.

Risk

High margin items can justify higher DIO

Some products require more stock coverage for customer experience.

Product mix

DIO is the inverse of turnover

As turnover rises, DIO falls, making them easy to interpret together.

Metrics pair

Disclaimer

DIO is a summary metric. Interpret results alongside service levels, forecast accuracy, and lead time variability.

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