Inventory accuracy is one of the most widely reported warehouse KPIs. It is also one of the easiest to misunderstand.

A business might report 99% inventory accuracy and reasonably assume that its warehouse records are in excellent condition.

But the usefulness of that percentage depends entirely on what was measured, how much inventory was physically checked and when those checks took place.

Before setting an inventory accuracy target, it is worth understanding the formula, as well as its limitations.

What is the inventory accuracy formula?

In its simplest form:

Inventory accuracy = correct inventory records ÷ inventory records checked × 100

For example:

• 1,000 records physically checked
• 985 records match physical reality

The inventory accuracy rate is:

985 ÷ 1,000 × 100 = 98.5%

That is straightforward.

The more difficult question is:

What counts as a correct record?

Warehouse inventory can be accurate in one respect and completely inaccurate in another.

Different ways to measure warehouse inventory accuracy

Quantity accuracy

Quantity accuracy asks whether the amount of stock recorded in the system matches the physical amount.

For example:

WMS quantity: 48 cases
Physical quantity: 48 cases

The record is accurate by quantity.

A simple quantity accuracy calculation is:

Quantity accuracy = Correct quantity records ÷ quantity records checked × 100

This is useful in environments where unit quantities are the main inventory risk. But it does not necessarily tell you whether the stock is in the correct warehouse location.

Location accuracy

Location accuracy measures whether stock is physically stored where the WMS says it should be. For pallet operations, this is often extremely important.

Imagine a warehouse where the system shows:

Pallet 123 → Location A01-04-02

The pallet physically exists, but it is actually in A02-04-05.

The quantity may still be correct.

The pallet identity may still be correct.

But the location record is wrong.

A useful calculation is:

Location accuracy = Correctly located inventory ÷ locations checked × 100

Location accuracy can expose risks that disappear inside a quantity only KPI.

Pallet or inventory identity accuracy

Another question is whether the correct inventory is actually in the location.

For example:

WMS expects: pallet ID 123456
Physical check finds: pallet ID 987654

The location is occupied, but the wrong pallet is present.

For warehouses using SSCCs, licence plate numbers or other unique identifiers, this can be a particularly useful measure.

Empty location accuracy

Warehouse inventory accuracy should not only confirm expected stock. It should also confirm expected absence.

If the WMS says a location is empty but a pallet is physically present, there is still an inventory discrepancy.

Likewise, if the WMS expects a pallet and the location is physically empty, the organisation may have phantom inventory.

Empty location accuracy can be measured as:

Empty location accuracy = Correctly identified empty locations ÷ expected-empty locations checked × 100

This is especially useful in high-density operations where location availability affects putaway and warehouse capacity.

The inventory accuracy percentage is only part of the picture

The formula tells you the accuracy of the inventory that was checked. It does not tell you how representative the sample was.

Consider two warehouses.

Warehouse A

• 20,000 pallet locations
• 1,000 locations physically checked
• 998 correct
• reported accuracy: 99.8%

Warehouse B

• 20,000 pallet locations
• 20,000 physically checked during the measurement period
• 19,880 correct
• reported accuracy: 99.4%

Warehouse A reports the better accuracy percentage.

But which warehouse gives management more confidence about the physical condition of the total inventory?

Warehouse B has physically verified far more of its operation.

That is why the inventory accuracy formula should be considered alongside verification coverage.

How to calculate physical verification coverage

A simple calculation is:

Physical verification coverage = locations checked ÷ relevant locations × 100

Using Warehouse A:

1,000 locations checked ÷ 20,000 total locations × 100

= 5% coverage

Warehouse B:

20,000 ÷ 20,000 × 100

= 100% coverage

The two figures together tell a much more meaningful story:

Warehouse A:
99.8% accuracy across 5% coverage

Warehouse B:
99.4% accuracy across 100% coverage

That additional context is really important.

Verification age matters

There is also a time dimension.

A pallet location physically confirmed yesterday gives the business much stronger evidence than one that has not been checked for eight months. This can be thought of as verification age.

Warehouse teams might measure:

• average number of days since locations were last checked
• percentage of locations checked within 7, 30 or 90 days
• number of overdue locations
• maximum time between planned checks

This helps answer another important question:

How long could a new discrepancy exist before our process is likely to detect it?

That is the discrepancy detection window.

What is a good inventory accuracy percentage?

There is no single percentage that is meaningful for every warehouse.

A target needs to consider:

• the type of inventory
• how accuracy is calculated
• operational consequences of an error
• customer requirements
• warehouse velocity
• physical check coverage
• verification frequency

For example, 99.5% accuracy based on broad and recent physical verification is likely to be more valuable than 99.9% calculated from a limited sample.

That does not make the higher percentage wrong. It simply means it needs context.

Should inventory accuracy be measured by SKU or location?

It depends on the operation. SKU or quantity accuracy is likely to be the priority in a unit-pick operation.

Whereas, for palletised warehouses, location and pallet identity accuracy may be more operationally important.

A manufacturer might care particularly about whether critical materials can be found where production expects them.

A 3PL may also need to demonstrate accuracy and physical verification coverage for individual customers.

There is no reason a warehouse has to rely on one inventory accuracy KPI.

Several complementary measures often provide a clearer picture.

Useful warehouse inventory accuracy KPIs

A more complete inventory control dashboard might include:

• overall inventory accuracy
• location accuracy
• pallet identity accuracy
• expected empty location accuracy
• verification coverage
• average verification age
• open discrepancies
• discrepancy closure time
• recurring variance categories
• high-level location coverage

These measures provide management with information about both the result and the control process that's producing it.

Why 99% inventory accuracy can still hide exposure risk

A high accuracy percentage is clearly preferable to a low one. The problem arises when the percentage becomes the end of the conversation.

A warehouse can report 99% inventory accuracy while still having:

• large areas that are rarely checked
• high-level stock excluded from routine counts
• unresolved discrepancies
• no evidence of previous observations
• long gaps between checks
• inventory corrections with no root cause investigation

Those weaknesses may not appear in the headline KPI.

This is why RAWview distinguishes between inventory accuracy and inventory assurance.

Inventory accuracy measures the observed outcome, whilst inventory assurance considers how much confidence the organisation can reasonably place in that outcome.

Use the formula, but measure the control behind it too

The inventory accuracy formula is of course still useful. Warehouses should absolutely measure how closely physical inventory matches system records.

But the strongest operators also understand:

• how much inventory was physically checked
• when it was checked
• where the gaps are
• what discrepancies were discovered
• whether those discrepancies were resolved
• why they occurred in the first place

That turns inventory accuracy into a meaningful management measure rather than simply an attractive percentage.

RAWview's free Inventory Assurance Health Check assesses many of those underlying controls and can help identify where confidence in inventory records may be weaker than the headline accuracy figure suggests.

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Frequently asked questions

What is the formula for inventory accuracy?

A common formula is: correct inventory records ÷ inventory records checked × 100.

How do you calculate location accuracy?

Location accuracy can be calculated by dividing the number of correctly located inventory records by the number of locations physically checked and multiplying by 100.

Is 99% inventory accuracy good?

Potentially, but the percentage needs context. The value of a 99% accuracy result depends on how accuracy was measured, how much inventory was physically verified and how recently those checks occurred.

What is the difference between inventory accuracy and inventory assurance?

Inventory accuracy is the measured relationship between system records and physical stock. Inventory assurance considers the strength of the process behind that figure, including coverage, frequency, evidence, reconciliation and exception handling.