This post continues topic described in two previous posts – ABC analysis. Very popular, simple method, however in most of cases non-accurate.
In previous post I showed how to increase accuracy of ABC-analysis by using special method – Triangle method. And now time to show real example.

Assume that we have list of sales


To apply method of triangle we have to
1. Sort data descending by Net Value


2. Convert Net Value to Net Sales – exclude possible Returns.
=IF([@[Net Value]]<0, 0, [@[Net Value]])


3. For each row calculate part of total Net Value – NS Normalization: [@[Net Sales]] / SUM([Net Sales])


4. For each row calculate [Contribution in result]: sum of [part of Net Value] from 1st ranked position till current row =SUM(SALES[[#Headers],[NS Normalization]]:[@[NS Normalization]])


5. Make normalization of rank: =[@Position] / MAX([Position])

6. Find Pareto Point – point where [@[Contribution in result]] + [@[Position Normalization]] = 100% (or closest point).


Resulting table


Then we will be able to find Xp and Yp – coordinates of Pareto Point.
In example file I placed these coordinates in separate cells and gave corresponding names to that ranges: “Xp” and “Yp”.


After that, using formulas

Which in Excel are:
=(Xp*Yp – Xp^2) / (2*Yp)
=(3*Xp*Yp + Xp^2) / (2*Yp)

We can find upper bound of groups A and B. And then mark all positions with corresponding class.


Such calculation can be easily done in PowerPivot where we can avoid sorting and grouping of transactional sales data – DAX can handle all necessary calculations.

Example workbook can be found here.

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