ABC Analysis


ABC analysis is an inventory management technique that determines the value of inventory items based on their importance to the business. ABC ranks items on demand, cost and risk data, and inventory management group items into classes based on those criteria.

We have developed a  multi-criteria ABC analysis since a single-criteria analysis has a serious drawback that may inhibit the effectiveness of the outcome in some situations. For example, when using price as criteria, class C might contain low pricing items with huge lead times. Not focusing on these items can eventually lead to significant financial losses when production is disrupted due to stockout.  


Our ABC analysis can provide the following benefits to your company:

  • Classify inventory in three groups: A, the most important; B, important; and C, the least important.
  • Establishing appropriate levels of control over each item
  • Multi-criterion instead of single allows it to include all crucial qualitative criteria at once


For this analysis we make use of several criteria. As these attributes are very company specific we provide some custom fields to enter all criteria relevant to your business.


The resulting classes will be made available in:

  • Report (.pdf)
  • Datafile (.csv)

For a fully automised solution classifying product items real-time get in contact to get more information on our webservice.

Report example

Multi-dimensional output

Instead of the traditional ABC analysis, were the clusters are based on a single dimension, our algorithm is able to cluster your purchases over multiple dimensions leading to better classes.

Advanced ABC visualisation

Using our multi-dimensional visualisations you will have a better understanding on how and why product items and services fall in one of the A, B or C classes.

Demo ABC Analysis

Explanation demo ABC Analysis

In the 'Advanced ABC analysis ', the algorithm calculates the ABC classification on more then one data attribute like spend amount. In this demo the leadtime exceedance and criticallity of the product are added to the dataset (a multi-dimensional approach). The result is a more and better balanced ABC category classification, which improves decisions based on the ABC classification.

Note: all data-attributes and visuals in this webpage are based on fictitious data

Zuiver ICT

Get in touch to receive more information on how we can bring your data to actionable insights