ABC analysis (Inventory) Definition - Inventory Optimization Software - Lokad

ABC analysis (Inventory) Definition - Inventory Optimization Software

ABC analysis (Inventory)

By Joffrey Collignon, Joannes Vermorel, February 2012

Inventory optimization in supply chain, ABC analysis is an inventory categorization method which consists in dividing items into three categories, A, B and C: A being the most valuable items, C being the least valuable ones. This method aims to draw managers’ attention on the critical few (A-items) and not on the trivial many (C-items).

Prioritization of the management attention

Inventory optimization is critical in order to keep costs under control within the supply chain. Yet, in order to get the most from management efforts, it is efficient to focus on items that cost most to the business.

The Pareto principle states that 80% of the overall consumption value is based on only 20% of total items. In other words, demand is not evenly distributed between items: top sellers vastly outperform the rest.

The ABC approach states that, when reviewing inventory, a company should rate items from A to C, basing its ratings on the following rules:

  • A-items are goods which annual consumption value is the highest. The top 70-80% of the annual consumption value of the company typically accounts for only 10-20% of total inventory items.
  • C-items are, on the contrary, items with the lowest consumption value. The lower 5% of the annual consumption value typically accounts for 50% of total inventory items.
  • B-items are the interclass items, with a medium consumption value. Those 15-25% of annual consumption value typically accounts for 30% of total inventory items.

The annual consumption value is calculated with the formula: (Annual demand) x (item cost per unit).

Through this categorization, the supply manager can identify inventory hot spots, and separate them from the rest of the items, especially those that are numerous but not that profitable.

eCommerce example


The graph above illustrates the yearly sales distribution of a US eCommerce in 2011 for all products that have been sold at least one. Products are ranked starting with the highest sales volumes. Out of 17000 references:

  • Top 2500 products (Top 15%) represent 70% of the sales.
  • Next 4000 products (Next 25%) represent 20% of the sales.
  • Bottom 10500 products (Bottom 60%) represents 10% of the sales.

This example is fairly close to the canonical Pareto situation.

Inventory management policies

Policies based on ABC analysis leverage the sales imbalance outlined by the Pareto principle. This implies that each item should receive a weighed treatment corresponding to its class:

  • A-items should have tight inventory control, more secured storage areas and better sales forecasts. Reorders should should be frequent, with weekly or even daily reorder. Avoiding stock-outs on A-items is a priority.
  • Reordering C-items is made less frequently. A typically inventory policy for C-items consist of having only 1 unit on hand, and of reordering only when an actual purchase is made. This approach leads to stock-out situation after each purchase which can be an acceptable situation, as the C-items present both low demand and higher risk of excessive inventory costs. For C-items, the question is not so much how many units do we store? but rather do we even keep this item in store?
  • B-items benefit from an intermediate status between A and C. An important aspect of class B is the monitoring of potential evolution toward class A or, in the contrary, toward the class C.

Splitting items in A, B and C classes is relatively arbitrary. This grouping only represents a rather straightforward interpretation of the Pareto principle. In practice, sales volume is not the only metric that weighs the importance of an item. Margin but also the impact of a stock-out on the business of the client should also influence the inventory strategy.

Lokad Gotcha's

Pareto priciple is over a century old and ABC analysis has been around for multiple decades already. Those concepts provide interesting insights in supply chain, but we believe, fail to some extent to embrace a more modern approach where software can automate the bulk of the inventory management. For example, as far demand forecasting is concerned, tools such as our forecasting engine can indifferently forecast A, B and C items without any extra effort once the historical data is fed into the system.

Further reading