Multiproduct Contribution Margin
Finding the Contribution Margin:
It begins with the same old way. Companies have to compute the contribution margin for each of their products. This is a possibility if a company has a couple of hundred products. If there are thousands of products, we may have to use the sales based approach mentioned in the end of this article.
So, for each of the products find the contribution margin and divide it by the selling costs, to derive the contribution margin ratio.
Finding the Weights:
The next step is to find the relative weights of each of these products. The common way to do this is to estimate the relative proportion of sales that different products have. For instance, if for every 1 product A, we sell two product Bs, their ratio is 1:2. We will need a ratio of the sales of all the products. This is found out by going into the empirical sales data that the company has. On the other hand, we can also find out if the sales are expected to change in the future period and make adjustments in our ratio based on this expectation.
Weighted Contribution Margin:
The final step is to calculate a weighted average. This is done by multiplying each contribution margin ratio of every item with its relative weight, adding all of them together and then dividing them by the total number of weights. The number we obtain is our multiproduct contribution margin.
Now, this calculation is completely based on our assumption about the relative weights of the sales. If the weights change too much, the contribution of the weights will also completely change. So we need to understand that if the proportion of sales is changing, our contribution margin is obsolete.
Sales Dollars Approach:
For large companies like Wal-Mart where there are thousands of products, the multiproduct contribution margin is calculated based on the sales dollars. Instead of finding contribution margin ratio for each item, one ratio is found out for increase in every sales dollar. This number is pretty accurate, if it is computed using the correct empirical estimates.
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