ABC Classification in Inventory Management

How to calculate ABC classification – a working example

4 min read

Calculating ABC classification of inventory

In a recent blog post we looked at the importance of ABC analysis in inventory management and it’s benefits and drawbacks.

In this post we’re going to discuss how you can practically classify your inventory into three ABC categories, using a working ABC classification example. We’ll then show you how to take things one step further by introducing the concept of XYZ analysis.

But first, here’s a quick reminder of what ABC classification is:

What is ABC inventory classification?

ABC inventory classification or ABC analysis is used by inventory management teams to help identify the most important products in their portfolio and ensure they focus on managing them above those less valuable. The ABC classification framework is based on the theory that all inventory is not of equal value. Instead it follows the Pareto Principle, where 20% of stock accounts for 80% of the value to the business. Using ABC classification you can therefore split inventory into three categories, A, B and C.

Category A: this is the smallest category and consists of the most important stock items
Category B: will generally be slightly larger in terms of volumes of SKUs and will usually be made up of products of less value
Category C: this will typically be the largest category where products will contribute the least to your businesses bottom line.

Your inventory’s ‘value’ can be defined based on a number of criteria, such as annual sales revenue, profitability or annual consumption value.

The graph below illustrates how 80% of a company’s sales revenue comes from 20% of their stock items.


ABC classification & The Pareto Rule for inventory management


ABC classification of inventory – an example

Here is a working example of ABC classification, showing how to divide up your inventory, using annual consumption value.

We’re going to use Sam’s Stationery business as the example:


1. Use the formula ‘annual number of units sold x cost per unit’ to calculate the annual consumption value of each item

Annual number of units sold (per item) x cost per unit

ABC classification example table one 

2. List your products in descending order, based on their annual consumption value

ABC classification example - table two 

3. Total up the number of units sold and the annual consumption value

ABC classification of inventory - table three

4. Calculate the cumulative percentage of items sold and cumulative percentage of the annual consumption values

ABC classification of inventory - table four

5. Determine the thresholds for splitting the data into A, B and C categories. The threshold for determining the ABC split will be unique to your company and your product mix, but typically it’s close to 80% / 15% / 5%.

ABC classification example - table five


How to put your data to good use

With the calculations complete, you can use your final data to review how you currently manage the inventory in each category. If you find that you’re treating all items the same, in terms of the stock you hold and the purchases you make – regardless of their category – then you’re most likely to have inefficient inventory policies. This means you’re probably over and under ordering on many product lines.

The good news is that there’s plenty of room for improvement! And this will bring about reduced storage, delivery and management costs.

Good practice is to adapt your purchasing and inventory policies for each group. This could include setting up sophisticated ordering processes for all A items, such as checking every purchase order and spending more time discussing lead times with suppliers to guarantee best value and timely deliveries.

In contrast, C items should take up much less of your time and could be ordered automatically to save valuable human resource.


EazyStock Software Inventory Optimisation

From ABC classification in excel to automation

ABC analysis is a simple framework that allows you to categorise your inventory based on its value. You can then prioritise what to stock and how to manage your time, so you can focus on your most important products. However, the model does have its drawbacks.

For starters it’s one-dimensional, so can only use one factor to evaluate and categorise products. For a more advanced classification framework that is still possible to carry out in excel, you could consider ABC XYZ analysis. This introduces the concept of forecastability, e.g how easy it is to forecast the demand for an item, based on its variability of demand. ABC XYZ analysis therefore allows you to segment items based on their value AND forecastability, again improving your stocking policies and inventory management processes.

ABC classification and XYZ analysis both still have a glaringly obvious limitation – they are manual processes, taking up value time and quickly becoming out-of-date. Some some enterprise resource planning (ERP) systems or inventory management software solutions can provide basic inventory classification functionality, often inventory planners will use excel spreadsheets for such calculations. This can lead to classification models quickly becoming out of date. Products can rapidly move between categories as their sales rise and fall. Updating calculations once a quarter, or even every month can be very time consuming. Unfortunately, as soon as your spreadsheet is completed it will begin to go out-of-date.

The answer is to use an inventory optimisation tool to automate the process. Software, such as EazyStock will allow you to categorise your inventory based on multi-dimensional criteria including demand, sales frequency, number of picks, and annual consumption value.

By automating your inventory classification process, you can be sure that it stays up-to-date with re-classifications taking place daily, so products are always managed according to the most relevant inventory policy.

For more information on EazyStock or to book a demo click here.


Whitepaper - Inventory Classification ABC XYZ Analysis


Originally published 01 Dec 2014 – updated June 2019


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