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What is Collection Matrix?
The notable drawback of traditional methods of monitoring receivables is that they are dependent on aggregated data. Moreover, methods such as Average Collection Period (ACP) and aging schedule fail to relate outstanding accounts receivables of a certain period with the credit sales in that particular period of time.
Therefore, two professionals can arrive at two different results when they aggregate sales and receivables data differently. How can this anomaly be resolved? The best way to remove such a problem is to use disaggregated data for analyzing collection experiences. The key here is to relate receivables to sales data in the same period of time.
When sales over a period are placed horizontally and the associated receivables data are vertically in a matrix, it is called the collection experience matrix. It is the latest and most advanced way of monitoring accounts receivables till now.
How to Construct a Collection Experience Matrix?
To start constructing a collection experience matrix, an analyst must start with the period for which the monitoring of receivables has to be made.
The credit sales over that period should be collected in a monthly manner the next.
Now from the sales ledger, the analyst must collect the outstanding receivables data.
For example, if the analyst is monitoring the receivables for six months, from July to December, he must gather dales in July, and outstanding receivables for July, August, and September. Similarly, sales for other months must be ascertained. All of the sales data can be shown in a tabular form.
The table obtained in such a manner can be converted to a collection experience matrix by dividing the outstanding receivables in each column by the sales amount in that column.
The matrix would then contain the percentage of receivables to the credit sales which has been found from the receivables.
For example,, for a certain amount of sales in July, there would be a percentage of receivables outstanding. Similar percentages of receivables outstanding can be obtained for August end, and September end.
This will then show how many percentage of credit has been collected in July, August, and September. These amounts of credit sales can be obtained by simply subtracting the receivables outstanding from the next month-end from the previous month-end. That is, July’s credit sales can be obtained by subtracting July’s outstanding receivables from September’s outstanding.
This method should be continued until the debt in the books becomes zero. Suppose the amount of the receivables becomes NIL in the month of October in our case. This can be continued for the remaining months as well.
Insights from Experience Collection Matrix
When we read the matrix top-down, it gives an idea of the process a firm collects the month’s sales. When the matrix is read diagonally, it shows the current month’s collections and how the company does collect the dues.
So, if 80% of sales have not been collected in a given month, 20% of the credit sales have been collected from the borrowers by the lender. If the percentage increases when the data is adjudged diagonally, the firm is at risk of having too many uncollected dues and it should take remedial actions immediately.
Conclusion
The collection matrix is a superior way to the other traditional methods in portraying the overall monitoring of accounts receivables. The only requirement is to collect dependable data so that the results obtained from the matrix give a correct idea about the outstanding and current accounts receivable of a firm.
Given the organization is large, collecting the required data for constructing the matrix won’t be any problem. However, for smaller firms that do not keep a correct record of sales and where data collection is cumbersome, there may be some problems in constructing the Collection Experience Matrix.
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