Reorder point calculator
The stock level at which you have to place the order to avoid running out.
≈ 3 minutes
Work out my reorder point
The safety stock you actually need
Safety stock is the buffer that protects you from what you cannot forecast: a week where you sell twice as much, a supplier who runs four days late. No buffer covers everything, so the question is not how much stock you need to never fail, but how much you need to fail only as often as you decided to.
Every calculation runs in your browser. The figures you type are never sent to a server and are never stored.
Your results will appear here
Pick a method and enter your demand and lead-time figures.
- Method: simple or statistical
- Demand and lead time, average or maximum
- Service level, if you use the statistical method
Calculation results
Safety stock
—
The buffer of units that absorbs demand peaks and delays.
Method used—
Unrounded calculation—
Expected demand in lead time—
Indicative reorder point—
Days of cover—How long the buffer lasts
Service level—
z factor—From the standard normal
Combined variability—
See the breakdown
| Average lead time | — |
| Expected demand in lead time | — |
| Unrounded calculation | — |
| Safety stock | — |
| Indicative reorder point | — |
Recommended next step
The buffer is an ingredient, not a decision. Combine it with lead-time demand and with what you already have in transit to know when to order.
Work out my reorder pointResults are estimates based on the information you enter. Fees, taxes and platform terms can change: always check the figure that matters to you against your invoice or the official platform. They do not replace your real invoices, your official accounts or professional tax, financial or legal advice.
What this tool works out
This safety stock calculator works out how many units you need to hold above expected demand to absorb the normal deviations in your operation.
With no buffer, one good sales week or one late delivery leaves you out of product. With too much buffer, you have cash tied up in units that have not moved for months. The calculation puts a number on that balance.
Two methods that do not mix
The simple method compares the worst case against the normal one: what you sell on your best day times your longest lead time, minus your average daily sales times your average lead time. It needs no statistics and works when all you have are observed maximums. It produces generous buffers.
The statistical method starts from measured variability — the standard deviation of demand and of lead time — and from the service level you want to reach. It is what inventory systems use and it produces tighter buffers.
The two methods are independent: each asks for its own inputs and neither feeds the other. Pick one based on the data you have, not on which gives you a more comfortable number.
How the formula works
In the statistical method, the lead-time demand deviation combines both sources of variability.
Simple method:
stock = (max daily sales x max lead time)
- (avg daily sales x avg lead time)
Statistical method:
z = normal value for the service level
sigma = sqrt( avg lead time x demand deviation^2
+ avg demand^2 x lead-time deviation^2 )
stock = z x sigma
In both cases: rounded up to the next whole unit
That sigma formula is the one Oracle NetSuite documents for its safety stock calculation, and it allows for both demand and lead time varying.
A worked example
Statistical method for a product with average demand of 10 units a day and a standard deviation of 3, an average lead time of 7 days with a deviation of 1 day, and a target service level of 95 %.
| Item | Value |
|---|---|
| Average daily demand | 10 |
| Demand standard deviation | 3 |
| Average lead time | 7 |
| Service level | 95.00% |
| Corresponding z-score | 1.64 |
| Safety stock | 22 |
| Indicative reorder point | 92 |
| Days of cover | 2.2 |
The buffer is 22 units. With 70 units of expected demand during the lead time, the indicative reorder point lands at 92, and those 22 units cover about 2.2 days of sales. Raising the service level to 99 % would push the buffer above 29 units: 33 % more stock for four points of service.
Service level and z-score
Service level is the probability of not running out during the lead time. 95 % means that out of every twenty replenishment cycles, you expect to come up short in one.
That percentage translates into a z-score from the normal distribution: 95 % is 1.645; 99 % is 2.326. The relationship is not linear, and that is where the economic decision lives: moving from 95 % to 99 % does not cost 4 % more stock, it costs around 40 % more buffer.
Which is why service level should not default to the highest number available: each extra point of service costs more buffer than the one before. Choosing it means deciding how much you are willing to tie up to reduce the risk of running out.
How to read the result
Days of cover helps you judge whether the buffer makes sense in your operation. A buffer covering two days of sales is reasonable if your supplier is reliable; if they routinely run a week late, two days protect nothing.
The indicative reorder point shown alongside is the buffer plus lead-time demand. It is a reference: to manage replenishment properly use the dedicated tool, which also accounts for what you already have on order and what is allocated.
The result is always rounded up, because the buffer is measured in sellable units: there is no half unit of protection.
Where the statistical method breaks down
The statistical calculation assumes demand is roughly normally distributed, and there are cases where that does not hold at all.
With intermittent demand — weeks at zero and the occasional large order — standard deviation describes the behavior badly and the buffer comes out misleading. With promotional or seasonal demand, the variability is not random but planned: you do not need a bigger buffer, you need to forecast the spike.
In those cases the simple method with observed maximums is usually more honest, even if it comes out more expensive.
What to decide next
The buffer on its own manages nothing: it is an ingredient. What turns safety stock into concrete decisions is the reorder point, which combines that buffer with lead-time demand and with what you already have in transit.
What this assumes
- Demand is roughly normally distributed
- The standard deviations were measured over the same period
- Demand and lead time are independent of each other
- Service level refers to the replenishment cycle
- The result is rounded up to the next whole unit
What it does not cover
- Intermittent demand, with many weeks at zero
- Promotional or seasonal spikes, which are forecast, not buffered
- The cost of holding that buffer, which is calculated separately
- Stockouts caused by inventory errors or shrinkage
- Multiple warehouses or allocation across locations
Frequently asked questions
Which method should I choose?
The statistical one if you have the standard deviation of your demand and lead time; it gives tighter buffers. The simple one if all you have are observed maximums: it asks for less and gives more generous buffers. Do not feed one method's data into the other.
What service level should I set?
The one you can afford, not the highest. The relationship is not linear: going from 95 % to 99 % raises the z-score from 1.645 to 2.326, meaning a buffer around 41 % larger. Save the high levels for the products you genuinely cannot fail on.
Where do I get the standard deviation of my demand?
From your daily sales history: most spreadsheets have a function for it. Use a period without promotions or stockouts, because a campaign inflates the deviation and a day out of stock artificially reduces it by never recording the real demand.
Why is the result rounded up?
Because the buffer is measured in units you can sell, and half a unit protects nothing. Inventory systems do the same: if the calculation gives 21.1 units, the buffer is 22.
What if my demand is very irregular, with weeks at zero?
The statistical method gives a misleading buffer, because standard deviation describes that behavior badly. With intermittent demand it is better to use the simple method with the maximums you have observed, even if the result costs more.
Does safety stock protect me during a promotion?
No, and relying on it is a common mistake. A promotional spike is planned: it is not uncertainty, it is demand you know is coming. You cover it by ordering earlier and larger, not by raising your permanent buffer.
What does holding this buffer cost me?
This calculator does not say, and it is worth knowing: units sitting still all year generate capital cost, storage, insurance and obsolescence risk. That calculation has its own tool and the figure usually surprises people.
What should I work out after this?
The reorder point, which is where the buffer becomes decisions. It combines this safety stock with lead-time demand and with what you already have on order to tell you when to place it.
Official sources
Oracle NetSuite — inventory optimization calculationshttps://docs.oracle.com/en/cloud/saas/netsuite/ns-online-help/article_0702045810.html
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