Safety Stock Calculator

Written byOleh Soloninupdated 5 October 2026

Average daily demand

Daily demand deviation

Lead time, days

Lead time deviation, days

Service level, %

Result

Safety stock346
Reorder point1,046
Demand over the lead time700
Service level factor1.645
Days the buffer covers3.5
What matters
Service level is expensive: going from 95 to 99 percent raises the safety stock about 1.4 times, from 99 to 99.9 by another third, and no finite stock gives 100 percent under a normal distribution.

You are not buffering demand, you are buffering surprises

Calculates safety stock and the reorder point from demand variability, lead time variability and the service level you want.

Ordinary stock covers the demand you expect during the lead time. Safety stock covers everything you did not expect: a week where sales double, or a supplier who arrives four days late.

Both sources of variation add in quadrature — the square root of the sum of the squares — which is why halving one of them helps far less than it feels like it should.

How to use

Enter average daily demand and its standard deviation.
Enter the lead time in days and its deviation — both uncertainties feed the buffer.
Pick the service level: it sets the factor drawn from the normal distribution.
Compare the reorder point with the inventory position — stock on hand plus stock already on order, minus backorders: you order when the position falls to it.

What a service level costs

Each row is a run of this calculator at 100 units of daily demand, a deviation of 25, a 7-day lead time and a 2-day deviation on it.

The factor comes from the normal distribution: 90 percent is 1.282 deviations, 95 is 1.645, 99 is 2.326. The tail of the curve flattens, so the last points are the expensive ones — from 99 to 99.9 percent the buffer grows by about 33 percent, while the chance of a stockout per cycle falls from 1 to 0.1 percent.

Runs of this calculator at 100 units of daily demand, a deviation of 25, a 7-day lead time and a 2-day deviation.
Service level	Factor	Safety stock	Reorder point	Days of cover
90%	1.282	270	970	2.7
95%	1.645	346	1,046	3.5
98%	2.054	433	1,133	4.3
99%	2.326	490	1,190	4.9
99.9%	3.09	651	1,351	6.5

What this calculator shows

The safety stock and the reorder point
The demand consumed over the lead time
The service level factor and how many days the buffer covers

What to keep in mind

Demand and lead time are treated as normally distributed and independent of each other.
The service level is the probability of not running out during one replenishment cycle, not over the whole year.

FAQs

What service level should I use?

There is no universal standard: the level is set by what a stockout costs against what spare stock costs. The calculator shows the price of the level you pick: the closer to 100 percent, the faster the stock grows.

Why does 99 percent cost so much more?

The factor goes from 1.65 to 2.33 — about forty percent more stock for four percentage points of service. The last fraction of a percent is the most expensive stock you own.

What if the lead time is completely reliable?

Set its deviation to zero and only the demand variation remains. In the example that alone cuts the safety stock by more than two thirds.

What is the reorder point?

The level at which the next order has to go out: demand over the lead time plus the safety stock. At 100 a day and a 7-day lead time that is 700 plus the buffer. The cycle stock covers the expected demand until delivery; the safety stock covers the deviations from it.

How is safety stock calculated?

The service-level factor times the square root of two terms: the variation in demand across the lead time, and the variation in the lead time itself multiplied by daily demand. The two uncertainties add in squares, not one after the other.

Worked example

A hundred units a day

Input: 100 a day ± 25, a 7-day lead time ± 2 days, 95 percent service

Output: a safety stock of 346 and a reorder point of 1,046

Note: The lead-time variation dominates: two days of uncertainty on a hundred a day contributes far more than the daily swing. Tightening delivery times beats forecasting better.

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Result

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