It’s not an easy task to manage thousands of stock items that come from a range of suppliers and get sent out to an extensive customer base. To help control such complexity, businesses can introduce models to prioritise their management. They can then apply a level of control to each group, based on their importance to the business. One way to do this is to use a model such as ABC analysis, also known as ABC inventory classification.
In this blog post we’ll delve deeper into how ABC analysis/classification can be used in inventory management and how it can help businesses improve their stock control practices.
ABC inventory analysis (ABC inventory classification) is a method used by inventory management teams to classify stock items into three categories – A, B and C – based on their ‘value’ or ‘importance’ to their business. ABC analysis is a fairly simple way to help managers focus their time and efforts on controlling more significant items of stock. They can also adapt their inventory control policies for each category to help ensure they carry optimised levels of the right stock.
In a business every stocked item (or SKU) does not have the same value. Some items will be more profitable, urgent or critical than others, and so will require more focus in terms of forecasting, management and replenishment activities. One way to help differentiate and prioritise items is to segment them into categories (A, B, and C).
In ABC inventory classification the most common way to dissect inventory is to rank items based on their usage value (this could be based on sales or consumption). Usage value is calculated by multiplying an item’s ‘usage rate’ by its ‘individual value’, or ‘sales volume’ by its ‘unit cost’.
You’re no doubt familiar with the ’80/20 rule’, also known as the Pareto Principle. This rule of thumb can also be applied to inventory consumption, where a relatively small number of stock items account for a large proportion of the usage value.
So, A items are the 20% or so of items with a high usage value that account for around 80% of the total usage value.
B items are the next 30% or so of medium usage value items, which account for around 10% of the total usage value.
And C items are low usage value items, which, despite making up around 50% of the total items stocked, only account for around 10% of the total usage value.
The graph below shows how ABC classification of inventory conforms with the Pareto Principle. You can see that 80% of the inventory’s total usage value comes from a small number of A category items, while a large number of B, C and D items make up the remaining 20%. In this example we have included category D items, as some companies may want to classify their inventory beyond, A, B and C.
Whilst usage value is commonly used in ABC stock analysis, it’s not the only way to classify stock. You could decide to group inventory items based on other ‘operation critical’ criteria, including:
Risk of stock-outs – how likely items are to be out of stock
Consequences of stock-outs – this could be based on production delays or customer service levels
Uncertainty of supply – items with erratic demand could warrant more attention
Item deterioration risk – items are risk of going obsolete might need extra monitoring
Once you have analysed your stock (read how to calculate ABC analysis here) and assigned each item to a category, you can then begin to benefit by using the information to achieve better efficiencies in terms of stock control and management.
Firstly, inventory teams can improve how they manage their time. For example, they can focus mostly on managing category A items to improve their stock availability. This could include updating demand forecasts or reviewing stock levels more frequently, or interacting more regularly with suppliers to improve lead times.
Secondly, ABC analysis can also help you work out appropriate inventory rules for each category. It makes sense to set different service levels, safety stock levels and re-ordering parameters for each category. For example, a few improvements in the order quantities or safety stocks of A items could lead to significant overall savings. Whilst improving fulfilment rates of A items could also dramatically increase overall availability.
Whilst ABC analysis is a relatively easy way to prioritise the management of your inventory, it also has a number of limitations:
ABC analysis arguably over-simplifies the classification of inventory. Especially if the analysis is done based on ‘gut feel’ and not hard data. The ABC classes are also very one-dimensional as they only take into account one variable, such as sales value. Factors such as demand variability or sales/pick frequency are arguably equally as important to consider when categorising your most important inventory.
With only three categories, ABC analysis lacks granularity. With 100’s, sometimes 1000’s of items in one segment, it’s a big generalisation to suggest that all SKUs have the same characteristics and should be treated equally.
ABC analysis lacks dynamism. In a marketplace where trends come and go and product sales can be erratic, items can move from category C to B very quickly. Without constant analysis and reclassification, your ABC classification groups can quickly become out of date. Treating A products as C products and visa versa can be very harmful to a business, leading to out of date inventory policies and consequently stock-outs or excess inventory.
With the risk of your ABC categorisation getting out-of-date quickly, it can be time-consuming to constantly re-evaluate your As, Bs and Cs and look for signs of movement between the groups. Inventory management teams could face spending more time on classifying their goods than acting on the implications of the results.
Taking ABC analysis to the next level, it’s possible to cross-analyse the usage value of your items with their demand variability. This allows you to classify products based on their value and their forecastability e.g how likely demand will vary from the forecast.
For example, some products will have a regular demand, whilst others will have intermittent demand. Having this level of insight helps you to make informed decisions about which products to stock and what safety stock levels to set.
In its simplest form this is known as XYZ analysis. Our post on ABC XYZ analysis has more details.
You could also choose to cross-analyse usage value with pick frequency or the number of times an item is sold. This can prevent over-stocking of relatively high-value, slow-moving items and ensure that low-value items with regular sales are identified as ones to watch. (These can be equally important, as sometimes these low value items have high margins!)
There is a way to overcome the shortfalls above and use ABC analysis effectively. The answer lies in utilising software to analyse and categorise your inventory. Whilst some ERP systems will have basic ABC (and XYZ) analysis functionality to do this, for more advanced capabilities you should look to invest in an inventory optimisation tool.
ERP apps, such as EazyStock, will take ABC inventory analysis to the next level, carrying out multi-dimensional item categorisation that considers a range of variables including:
Value of annual usage – sales volume x unit cost
No of picks – number of times picked over the year
Demand volume – number of units sold
Sales frequency – what % of historical periods have a sale
The result is a far more advanced inventory classification matrix which EazyStock then uses to recommend your inventory policies, providing service level targets and safety stock levels for each product category – and down to sku level.
Because the classifications are re-calculated and updated on a daily basis, EazyStock can provide alerts when products move from one category to another, so the segmentation is always in line with market dynamics. For more information on how EazyStock can deliver advanced ABC analysis functionality download our whitepaper.
With the analysis and hard work done for you, you’re then able to manage by exception, focusing on the categories or even SKUs that EazyStock suggests need your attention.
Post first published 10 July 2015, updated July 2021.