In a previous article we discussed how Datascan’s preferred inventory accuracy percentage calculation relies on the absolute unit difference to describe how far the inventory records are from reality. The ECR Community and the RFID industry prefer a formula based on SKU integrity, which treats all inventory unit adjustments with the same weight. In this article, we will explore this formula and compare it to the formula based on absolute variance. The SKU integrity formula for inventory accuracy is very easy to calculate and understand. Notice that the formula does not need total units by SKU nor the total units in the store. A SKU’s counted units either match or don’t match the inventory record. All variances are viewed the same way no matter how high the unit variance. Some retailers using the SKU integrity formula consider SKUs within +/-1 units to be correct.

Here’s a quick comparison of the SKU integrity formula versus the absolute variance formula. Now let’s dig into the details…See my article “What is Inventory Accuracy?” if you need a refresher on how to calculate Inventory Accuracy using the absolute variance method. Since it requires summing the absolute variance of each SKU and then dividing by the store’s total units, it is a bit more difficult to calculate than the SKU integrity method. Many systems carry high units for non-retail SKUs (e.g. gift cards) which are never counted and tracked, so these are always inaccurate. The Absolute Variance method is very sensitive to the typically high variances for non-retail SKUs, which requires you to exclude these to get a more reasonable representation of your actual Inventory Accuracy. It’s important to scrub these SKUs from your dataset - in fact when the sum of the absolute unit variances exceeds the store’s total units the Absolute Variance method will be negative. While the SKU Integrity method benefits from data scrubbing of this type, it’s generally not necessary.

The following example shows the effect of 2 non-retail SKUs which increase the absolute unit variance by 3,000. You can see that the difference in the SKU integrity-based formula is negligible, while the absolute variance-based formula shows a much more significant difference. The absolute variance method is sensitive to the overall scale of variances in a store. With the SKU integrity method it is impossible to get a sense of variance sizes. Consider an example store with 100 SKUs, 40 of which have some kind of variance. The store’s inventory records indicate there are 20,000 total units. The table below demonstrates how the SKU integrity formula remains the same across three very different scenarios, while the absolute variance formula highlights the differences. Let’s look at an example of an SKU with 10 units in the inventory record and 7 units counted in a just-completed physical inventory. This SKU has a shortage of 3 units which makes the absolute value of the unit variance equal to 3 units. The SKU integrity formula reports this SKU as 0% accurate because the actual units in the store do not match the units in the inventory record. The absolute variance formula reports this SKU as 70% accurate because it recognizes the 7 units that actually exist in the store. Inventory accuracy degrades over time. This fact is easy to understand when you consider that the causes of inaccurate inventory records occur over time as well. See my previous article “Inventory Accuracy % is not Enough” on why it’s important to use a monthly degradation percentage instead of an inventory accuracy percentage. The absolute variance formula accounts for this degradation while the SKU integrity formula does not. You can see this in action for SKUs that suffer from multiple events affecting their accuracy. It is not possible to analyze inventory accuracy by extended cost or extended retail when using the SKU integrity formula. It is possible to substitute extended cost or extended retail for units with the absolute variance formula. For example, if your inventory accuracy is 80% using units compared to 85% using extended retail, then you can surmise that higher-priced items are generally more accurate than your lower-priced items. Inventory accuracy is an important KPI for omnichannel retailers. We prefer the absolute variance formula for calculating a monthly degradation percentage because it’s more flexible and accounts for the degradation of inventory accuracy over time. Whichever formula your company uses, it is important to know which factors have significant influence over the final result. And if you are comparing your results to other retailers, it is equally important to know how they calculate their inventory accuracy results.