Aging schedule

Aging schedule
See also
Balance an account Liquidity management Bank reference Accounts Receivable Aging Average collection period Aging receivables Comparative statements Current portion of long-term debt Customer deposits

Aging schedule is the one of commonly used indicators of accounts receivable collection efficiency. It has a form of a table, usually created by accounting software. It can be used to monitor whether customers pay in time, as well as whether the company pays to suppliers on time.

Description of the Aging schedule

The Aging Schedule is a popular accounts receivable tool and is widely referred to in the normative accounting and finance literature. It comprises a classification of outstanding balances according to the period of time they have been outstanding:

$P_{t}={\frac {r_{t}}{\sum \ r}}$

or stated in 30 day categories:

$Current={\frac {r_{1}}{\sum \ r}}\;\;\;\;\;\;\;\;\;\;30\,Days={\frac {r_{2}}{\sum \ r}}$

$60\,Days={\frac {r_{3}}{\sum \ r}}\;\;\;\;\;\;\;\;\;\;90\,Days={\frac {r_{4}}{\sum \ r}}$

$120+\,Days={\frac {\sum \limits _{t=5}^{\infty }r_{t}}{\sum \ r}}$

Where

$P_{t}$ - the proportion of accounts receivable "t" financial periods in the past.

$r_{t}$ - the total accounts receivable sourced from credit sales issued t financial periods in the past.

These age categories usually are calibrated according to days, weeks or months, which depends on an organization's requirements, and are often expressed as a percentage relative to the total accounts receivable balance. In case if all debts are collecting on time, most debts should be younger, and only few should be older. It is assumed that percentage of debt in the older categories would be reduced by increase collection efficiency.

Prediction of the timing of accounts receivable collections is an important element of the forecasting cash flows process. It would be ideal if the exact time, from the billing date to the payment date, always were known in advance for each debt. Such knowledge could be aggregated for all accounts, and accurate forecasts could be made from this source of cash receipts. But in real the strategy of payment of due invoices depends on strength of supplier and possibility of negotiation. Similarly in case of customers. The company usually wants to keep as many invoices as possible in first category and not let them to move to other categories.

Weakness of the Traditional Aging Schedule

Aging Schedule by the analizing of accounts receivable balances should provide collection efficiency indicators. However, accounts receivable balance of companies is depend on the both credit sales and collections and by this way balance of accounts receivable changes when either credit sales or collections are made. Because both credit sales and collections affect the balance of accounts receivable, it is difficult to identify whether a change in this balance has been caused by a change in credit sale activity, a change in collection activity, or both of them.

The Aging Schedule is expressed in percentages, with the total of all categories equalling 100%. This calculation means that all of categories will change when anyone of the accounts receivable categories will altered. This interdependence generate trubles during the interpretation a change in a particular category. This problem of interdependency in an Aging Schedule is most visible at time of observation of changes in credit sales.Rise of credit sales will result in a schedule exhibiting increasing values in the younger categories, thereby a misleading suggestion of collection efficiency increased for the older categories. As a result, an Aging Schedule can be seen to represent a tool that accurately depicts collection efficiency only in periods of evenly occurring credit sales. By this way it is poissible to conclude that the Aging Schedule can produce an incorrect analysis and false warning patterns can be provide by normal sales fluctuations. One can conclude also that the sum of credit sales observed in the last 30 day period is positively correlated to the current category in a traditional Aging Schedule.

Correcting the Aging Schedule

Corrected Aging Schedule should also display accounts receivable as a percentage schedule, where each of the age categoriws show the percentage of total accounts receivable contained within that category. It is possible by the inclusion of the information about credit sales in the percentage calculation:

$P=({\frac {r_{1}}{s_{1}}}\;{\frac {r_{2}}{s_{2}}}\;...\;{\frac {r_{t}}{s_{t}}})$

Or more concisely:

$P_{t}={\frac {r_{t}}{s_{t}}}$

Where

$P_{t}$ - Aging Schedule percentage category for financial period $t$.

$s_{t}$ - the total credit sales $s$ generated in period $t$ which means that corrected Aging Schedule use the total credit sales within each period:

$s=(s_{1}\;s_{2}\;...\;s_{t})$

$r_{t}$ - the total accounts receivables $r$ generated by credit sales from period $t$. Accounts receivable are divided into age categories matching the periods used for the credit sale:

$r=(r_{1}\;r_{2}\;...\;r_{t})$

One can conclude that in the corrected Aging Schedule the accounts receivable categories, $r_{t}$, are transformed into percentages of their original sales, $s_{t}$, creating a percentage Aging Schedule, $P_{t}$.

The traditional Aging Schedule relates credit sales over a longer period to accounts receivables balances, and corrected Aging Schedule relates accounts receivable categories to their original credit sales. This corrects for the shortcoming of the Aging Schedule, because removes period interdependencies from the measure. Categories are no related to each other now as they are no longer required to sum to 100%.

References

• Denčić-Mihajlov, K., Cvetanovic, S., Andjelkovic, A., Popović, Ž., Stojkovic, N., Radovic, O., ... & Marinkovic, S. (2015). Impact of Accounts Receivable Management on the Profitability During the Financial Crisis: Evidence from Serbia. Journal of Environmental Planning and Management, 11, 16.

• Leitch, Ph., Lamminmaki, D. (2011). Refining Measures to Improve Performance Measurement of the Accounts Receivable Collection Function. Journal of Applied Management Accounting Research,Vol. 9 Issue 2, 1-20.

• Zeng, S., Melville, P., Lang, C.A., Borier-Martin, I., Murphy, C. (2008). Using Predictive Analysis to Improve Invoice-to-Cash Collection. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, 1043-1050.

Author: Mykyta Yezhevskyi