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Days Inventory Outstanding (DIO) Formula

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Days Inventory Outstanding (DIO) Calculator

Days inventory outstanding, also called days in inventory or days sales of inventory, is the average number of days a company holds stock before selling it. It divides inventory by cost of goods sold and multiplies by the number of days in the period. It is the inventory turnover ratio expressed in days.

Formula

DIO = Average inventoryCost of goods sold × Days in period

Where

Average inventory
(beginning + ending inventory) ÷ 2, or ending inventory
Cost of goods sold
COGS for the same period
Days in period
365 for a year

Calculator

Result and step-by-step solution

Days inventory outstanding
60.83 days

Stock sits for about 60.8 days before it is sold.

  1. Divide inventory by COGS
    50,000.00300,000.00 = 0.166667
  2. Multiply by the days
    0.166667 × 365 = 60.83 days

How to use the formula

A falling DIO means stock is moving faster, which frees up cash and lowers storage and obsolescence costs. A rising DIO can be an early warning that demand is weakening or that the business has over-ordered. Seasonal businesses should use average inventory, or compare the same quarter across years, because stock builds before peak season.

With average inventory of 50,000 and annual COGS of 300,000, DIO is 60.83 days, which is 365 ÷ the inventory turnover of 6. DIO is the first leg of the cash conversion cycle, the time between paying for stock and collecting from customers. For the ratio form and averaging of beginning and ending stock, use the inventory turnover calculator.

Frequently asked questions

What is the DIO formula?
DIO = average inventory ÷ cost of goods sold × days in the period.
How is DIO related to inventory turnover?
DIO = days in the period ÷ inventory turnover.
Is a lower DIO always better?
Usually, but too little stock can lead to shortages and lost sales.
Why use cost of goods sold rather than sales?
Inventory is recorded at cost, so COGS gives a like-for-like comparison.

Last reviewed: September 26, 2026. Calculations run in your browser and were checked against numpy-financial, SciPy and published spreadsheet examples.

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