After a company has established its credit policies it will need to implement KPIs and visualizations to monitor its accounts receivable situation. Through charts the company can gain insights such as the efficiency of collections, the total money tied up in receivables, and if there is an issue with all customers or just a few.
The data available will vary depending on the ERP but basically the following is needed:
-Credit days (ex. some clients may be given 30 days others 90, depending on the policy established)
-Days past the due date (the system most likely provides this but if not it’s simply the number of days since the sale minus the credit days offered)
– Aging (if not provided by the system you can define your own categories such as over 90 days, 60-90 days, 30-60 days, and under 30 days.)
Efficiency and Total AR
One way to measure the efficiency of collections is to analyze periodically (weekly or monthly) what the total amount in AR is and split it by aging. By providing different aging categories KPIs can be established. In the scenario illustrated below the business is fine with invoices aged under 60 days, while those between 60-90 days require attention and those aged over 90 days are definitely a red flag. This type of chart allows to observe the tendencies and if there’s an issue. In this example there was a problem with invoices aged over 90 days that was increasing, but was dealt with starting on month 7.
A good complement to this would be a dashboard or side notes indicating the total amount in each category and by how much each category increased or decreased. This allows the chart to avoid being cluttered with floating numbers while at the same time providing a clear explanation in case management wants a deeper analysis.
Other facts that can also be mentioned on each meeting is the percentage change for each category between periods instead of amounts and what percentage from the total receivables have aged to critical.
Analyzing clients is also vital and can provide many insights about business relationships and performance of the team in charge of collecting. If when taking a look at the invoices heavily aged it turns out all customers haven’t been paying, this may signal an issue with the AR/Collections department. On the other hand, if most of the problematic invoices belong to one or a couple of customers and the other clients have been paying in a timely manner than it may indicate a concern that needs to be addressed with those few that are not paying. By doing the respective analysis the business can easily address only those invoices with an aging problem when negotiating with the client.
When presenting the situation with AR, the company could segment by a particular aging and illustrate out of the total in that aging how much is outstanding by each client. If the number of customers are too many the chart could only illustrate those that make up most of the invoices owed.
Accompanying this type of graph the same information could also be presented but with percentages instead, since this does provide an easier understanding as to not only the amount owed but also easily identifying which client is the one with the highest overdue invoices.
Visualizations don’t need to be complex to illustrate a point, but they should convey the insight that the company is going after.