The stock-out rate

The most expensive sale is the one that never happens. A customer who cannot find an item on the shelf and walks away is invisible in the reports: there is no "not sold" line in 1C, revenue simply comes out lower. The stock-out rate turns that invisible loss into a number: the share of days when, at the moment a customer came, the item was not in the warehouse or on the shelf.

Formula and example

Stock-out rate, % = number of days the item was unavailable ÷ number of days in the period × 100.

Example: a 30-day month and the item was missing for 4 days. 4 ÷ 30 × 100 ≈ 13.3 percent. That means on nearly every seventh day of the month a customer could not find it.

For the whole range use item-days. Suppose there are 200 active items and on average 14 of them are missing each day. A month has 200 × 30 = 6,000 item-days, and in 14 × 30 = 420 of them the item was missing. 420 ÷ 6,000 = 7 percent. Active items means goods that have sold recently; leave goods that do not sell at all out of this calculation.

A monthly average can smooth a problem away, so watch the weeks too. For example, 7 percent for the month does not look bad, but if the rate reached 20 percent in the week before a holiday, that is when the most sales were lost.

Estimating lost sales

The percentage alone moves few people; an amount in so'm does. An approximate formula: lost sales ≈ average daily sales × days out of stock × price.

Example: an item sells 25 pieces a day at 40,000 so'm and was missing for 4 days. 25 × 4 = 100 pieces were lost, which is 100 × 40,000 = 4 million so'm of revenue. If the profit margin is 25 percent of the price, the lost profit is 1 million so'm.

This estimate is an upper bound: some customers wait until tomorrow and others buy a similar item. Even so, it shows the scale. What it leaves out is customer trust: a shopper who leaves empty-handed several times may move to another shop.

Traps in the data

1C usually stores the end-of-day balance. Several problems come up when calculating stock-out:

• If an item sold out mid-day and was restocked after lunch, the end-of-day balance is not zero, yet the morning customer has left. Define a rule in advance, for example: "if the end-of-day balance is below average daily sales, we count the item as out of stock".
• In the records but not on the shelf. The system shows 5 pieces, in fact there are none, so the stock-out is invisible. This is an accuracy problem, and only a count reveals it.
• Off-season periods. Do not treat an item whose sales have stopped naturally as a stock-out.

Causes and priority by ABC

Typical causes of an item running out: late deliveries, a reorder point set too low, a sharp rise in demand (a promotion, the days before a holiday), cargo stuck in receiving, and inaccurate balances.

Not every item needs the same attention. One day without a group A item can cost more than ten days without a group C item. So set a strict limit for group A (for example, stock-out no higher than two percent), while for group C do not aim at zero: that would require far too much stock.

How to reduce it

1. Make a list of the ten items that run out most often.
2. Establish the cause for each: late delivery or a low reorder point.
3. Recalculate the reorder point and safety stock. Example: sales of 25 pieces a day, delivery in 8 days and safety stock of 30 pieces give a reorder point of 25 × 8 + 30 = 230 pieces. As soon as the balance drops below that number, an order must be placed.
4. Introduce a warning: when days of cover fall below the lead time, the item joins a risk list.
5. Plan demand separately before holidays and promotions, comparing with the same period last year.

Conclusion

The stock-out rate makes an invisible loss visible, but only with reliable balances. Cases of "in the records but not on the shelf" spoil the analysis, so check the balances with a count first. CountLab combines stock-count results with ABC/XYZ analysis to show on which items running out costs the most.