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Oliver Wight Blog

ABC Analysis in Practice: Part 1

23 January 2017

To demonstrate how effective ABC analysis can be in practice, consider the following example: if a company was to use the volume, the revenue and the margin /profit to classify products, they would create a simple column classification, as depicted in Table 1.  This shows that the product with the highest volume of sales is actually not the most profitable.


Table 1 Annual PRODUCT Sales in a Simple Column Classification

Choosing different ‘lenses’ to view products can be helpful in accurate classification; in this example, it is not unusual to see changes in classification from volume, to revenue, to profits. Some companies will use a reference code, such as AAC, but it is important not to make the code so subtle that it loses its meaning.

Once decisions are made about ABC classification, the communication of behavioural elements can drive a huge cultural shift.  If a product isn’t making a profit for a company, why make so much of it? However, sometimes things are not so clear-cut. A consumer packaging company that worked with Oliver Wight identified through ABC analysis that a jumbo pack of product was not making a profit, so cut the product line. However, this product was a special item found only at one retailer, and this soured customer relations. By considering the product through the ‘lenses’ or category of customer satisfaction, this could have been avoided. It is important to build a clear and complete picture, using both ABC analysis and Integrated Business Planning when making business decisions.

Other areas that these concepts have been applied to are in the tables below.




Manufacturing Cycle



Focus Should Be



Very often
















Table 2 Inventory Holding to Optimise Service and Cost


Interestingly, working through this break up does not mean holding more inventory for the A Class items and less of the Cs. The reverse is actually true. Holding more safety stock of the Cs makes sure that time spent managing them is minimised, freeing up time to focus on the smaller number, but more important, A Class Products.

The general logic is that demand for As is usually more consistent, and as they can be made more often because of their volume, so they are easier to ramp up or down as demand fluctuates. For Cs, the reverse is true, and if Cs make up 5 per cent of the overall volume, then holding a little more inventory of those is still only a little. For the As however, even small percentage movements in inventory can have a significant financial impact.



Low Volatility

High Volatility


A focus item for Marketing and Sales

Aggregate statistical forecast used for projections

Planning bills used to create detailed SKU projections

Assumptions captured at an aggregate level

A focus item for Marketing and Sales`

Detailed-level statistical forecasting used for projections

Assumptions captured at a detailed level Cross-functional forums used to reach a consensus on the forecast


Demand manager led

Aggregate statistical forecast used for projections

Demand manager led

Detailed statistical forecast models used


Use statistical forecasts

Cover with inventory while manage out of the business

Table 3 Demand Management to Drive Focus


In Table 3, ABC is used to set up the Demand Review, and demand management and forecasting processes.

One of the reasons for segmenting in this way for the forecasting system is to reduce the number of data points forecasters have to manage. For example, a forecaster might have the following data set:

·       250 products

·       Six channels or customers

·       24-month statistical forecast

·       24-month projections for this month

·       23-months projections from last month

·       12-months budget

·       12 months for last year

When this is multiplied out, the number of data points ends up being in the hundreds of thousands. The question is, “How do you effectively manage that many data points?” The answer is you can’t; it just isn’t possible. Therefore, segmenting in the above way will significantly reduce the number of data points and increase focus on the vital few.