A credit manager's guide to objective A/R analysis.
In the article, "Better Way to Monitor Accounts Receivable," from the May/June 1972 issue of the Harvard Business Review, Johnson and Lewellen were the first to examine "the chronological pattern according to which the receivables created during a given interval are converted into cash."
Stone, who built on their work to develop a payment pattern technique, defines payment pattern as "the time distribution of cash flows that arise from credit sales at a point in time," in the article, "The Payments-Pattern Approach to the Forecasting and Control of Accounts Receivable" from the Autumn 1976 issue of Financial Management.
Payment pattern analysis matches each payment to the specific invoices it clears, then examines the pattern created by all receipts over time. The superiority of Stone's payment pattern technique can be seen in the following examples.
Payment Pattern Analysis - Example One
Most credit professionals would agree that the quality of a receivables portfolio is neither improving nor deteriorating if the percent of each period's credit sales collected over time remains constants and follows a pattern. For the first example, therefore, let us stipulate net 30 terms, no change in the evaluative standards for new customers, and the following constant collection experience for any given month's credit sales as shown in Table 1.
[TABULAR DATA OMITTED]
Table 2 sets forth a hypothetical sales scenario. Its accompanying receivables information is developed from the above payment pattern. The DSO calculation is taken from the Credit Executive Handbook; it is expressed as the average trade receivables balance of the last three months-ends multiplied by 90, divided by credit sales for the last three months.
Average Trade Receivables Balance Last Three Months-ends x 90/Credit Sales for Last Three Months.
According to Christie and Bracuti in the Credit Executives Handbook, "the three month-ends average accounts receivable balance is used to smooth the data" and "eliminate a sawtooth effect on any DSO trend line being developed."
What Table 2 shows about DSO signals is that they are unreliable indicators of the relative conditions of receivable. For this example, collections performance (and by extension, the relative condition of the receivables portfolio) was stipulated as constant. Yet, the DSO calculation indicates two months (February and March) of improvement followed by five months of slow deterioration, three months of moderate deterioration, and one month with no change.
With any DSO calculation, the subjectively judgmental question always arises as to what constitutes a significant change in the index. In Table 2 there is a swing of 5.7 days between March and November. That 14 percent change can almost surely be classified as significant. Likewise, the 4.8 percent "improvement" shown from February to March can be seen as significant. On the other hand, the generally deteriorating drift between May and August would not be considered significant.
[TABULAR DATA OMITTED]
In fact, the only significant signal given here by the DSO index is that it is an unreliable indicator of the accounts receivable. It has been reflecting only sales-induced changes in the composition of accounts receivable. Few credit professionals would want their efforts evaluated primarily on the basis of how well or poorly the firms' products are sold. Yet, that is exactly what would happen in the above scenario if the DSO index were used as an evaluative technique.
The aging schedule does no better as an indicator of the relative condition of receivables. The question of what represents a significant change in the receivables portfolio becomes more difficult to answer. February's aging looks significantly better than January's, but that is only a subjective judgment. June's aging may or may not be an improvement over May's. November's aging looks much worse than that for March.
However, since we have stipulated no change in the payment habits of customers, all that the aging schedule is really telling us is that sales have not been constant. Again, as with DSO, the supported evaluation of receivables becomes instead merely a reflection of the impact of sales changes.
There currently is no method available that allows either a multi-month DSO calculation of an aging schedule to be completely isolated from the impact of sales changes. Only when the data under evaluation is disaggregated, as in a payment pattern analysis, can the evaluation of receivables be conducted independently of exogenous influences.
Payment Pattern Analysis - Example Two
The second example, using data presented in Table 3, relaxes the constant payment pattern constraint. Terms are still are net 30, and there is no change in the evaluative standards for new credit customers. Monthly sales figures are the same as for Table 2, but actual collections are logged. From this data, the DSO index (Credit Executives Handbook formula), an aging schedule, and the payment pattern are derived. May and November will be discussed to illustrate how a payment pattern analysis helps the credit professional understand the receivables portfolio.
[TABULAR DATA OMITTED]
In May, the DSO index increased from 40.5 to 44.7. This increase of more than 10 percentage points, compared to April, can be interpreted as a strong signal of deterioration in the condition of receivables.
At the same time, the aging schedule presents a contradictory picture. The percentage of total receivables classified as current increased, relative to April, by 5.1 percent. This is indicative of general improvement in the portfolio. Although there was small increase in the percent of receivables 31 or more days past due, there also was a substantial decrease in the percent of items 1 to 30 days past due. Overall, the aging schedule seems to be telling us that the relative condition of the portfolio is improved.
What is the credit analyst to make of these conflicting signals? The payment pattern approach provides an objective technique with which to understand changes that have taken place. It sorts out the conflicting signals and allows the analyst to see what has really happened from a credit standpoint. During May, there was substantial improvement in the collection of current items. In April, only 63.19 percent of current items were collected; that figure increased to 67.23 percent in May, returning to near normal levels.
Collection of items 1 to 30 days past due also improved substantially, climbing from 1.57 percent to 22.94 percent. This is above normal levels. There was a slight decline in the collection of items 31 to 60 days past due. However, collection items in May was still slightly better than average.
In the 61 to 90 days past due column, collections improved somewhat. In addition, write-offs were down slightly. Overall, it is evident that collections performance improved in May relative to April. This means the status of receivables really did improve, and that the aging schedule, not DSO, was giving more reliable signals at this point.
The above results, however, should not be used as an argument in favor of an aging schedule as opposed to DSO. It is purely coincidental that the aging schedule, rather than DSO, agreed with the payment pattern analysis. It is, in fact, possible for DSO and the aging schedule to agree while providing false signals. November's results provide such an example.
Deterioration of Collections Effort?
In November, the DSO index increased from 45.3 to 49.4 days sales outstanding. This increase of more than 9 percent over October's results is nearly as substantial as was seen in May. Again, the signal is one of general deterioration in the status of receivables. This time. unlike in May, the aging schedule appears to confirm the diagnosis. There was a slight decrease in the percent of current items, an undesirable change. There was also a sizeable decrease in the percent of items 1 to 30 days past due. while this would normally be desirable, here it is not. This is because there has been a slight increase in the percent of items 31 to 60 days past due and a sizeable increase in the percent of items 61 to 90 days past due.
What appears to have happened is a general deterioration of the collections effort. It seems that more credit sales have stayed on the books for a longer period of time. This is, in fact, not the case. The negative signals of DSO and the aging schedule are due entirely to the impact changing sales levels have had on the calculations.
The payment pattern analysis, which is independent of sales effects, shows what really happened. There was an increase in the collection of current items. In October, they were 6.00 percent below average while in November they were 3.14 percent above average. There also was some improvement in the collection of items 1 to 30 days past due.
Collection of items 31 to 60 days past due increased substantially, going from 6.89 percent to 9.04 percent. This was offset somewhat by a slight decrease in the collection of items 61 to 90 days past due. However, write-offs also declined slightly.
Overall, what actually happened was a generally improved collections effort. This improved the status of the receivables portfolio, contrary to the impression presented by DSO and the aging schedule.
As can be seen from our brief look at May and November, neither DSO nor a receivables aging schedule can provide unbiased insight into changes in the status of receivables. Analysts depending on DSO and a receivables aging schedule for insight into the condition of receivables would have been misled. Only a payment pattern analysis has told us, in objective terms:
* that during May and November there was improvement in the collection of receivables; and,
* the scope of that improvement.
Because the payment pattern approach disaggregates the data under analysis, it divorces itself from unquantifiable sales effects. This, as shown above, provides the credit professional with an objective look at changes in the status of accounts receivable. At the same time, there are some correlative benefits provided by the analysis. One of these is in general credit management; the other is in cash flow forecasting.
Management By Exception
Use of a payment pattern analysis allows credit managers to practice management by exception. This requires managers to focus their efforts on those activities or events which fall outside the pre-defined parameters of a "normal range" of activity. A normal range of collections activity can be defined statistically in terms of an mean and standard deviation (shown as [mu] and O in Table 3). Because the analysis is not affected by sales changes, the normal range definition is truly objective.
For purposes of illustration, the normal range of activity in Table 2 is defined as the mean collection percentage for each aging bracket, plus or minus one standard deviation. Translated, that means the normal range of collection activity for current items is between 63.7125 and 71.0325 percent (67.3725 percent [+ or -] 3.66 percent.) The normal range could just as easily be defined as any multiple of one standard deviation from the mean. Once the normal range is defined, management can concentrate its efforts on the instances when collections results fall outside the specified normal range. One example of this occurs in May for the collection of amounts 1 to 30 days past due. Collections here were more than two standard deviations above the mean. Why? One reason might be that a customer, who normally pays promptly and whose bill was due in April, actually paid late.
There could be other reasons as well. What is important is that management has been alerted that something out of the ordinary has happened; there is sufficient reason to investigate further to find out what has happened and why.
Cash Flow Forecasting Is Another Benefit
A second correlative benefit comes in the area of cash flow forecasting. Most financial managers would agree that development of accurate cash flow forecasts is important to their businesses. It allows them to plan for such things as capital expenditures, debt retirement, expansion, and normal business operations. The data base, built through monthly payment pattern analysis, makes it possible to attach objective statistical probabilities to cash flow projections.
One final note should be added. A significantly better or worse than average collection effort for current items will mean that smaller or larger than] average amounts of money will move into the past-due columns. This in turn will increase the probability that collections of past-due amounts will be below or above average in subsequent months.
For example, in June 71.00 percent of May sales were collected. This above average collection effort contributed to the below average 15.11 percent rate of collection of May sales during July. The reverse of the May-June-July situation also exists. In April, only 63.19 percent of March sales were collected. This below average collection effort left an above average amount of money open in May as 1 to 30 days past due. In turn, an above average 22.04 percent of March sales were collected during May.
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|Title Annotation:||accounts receivable; part 1|
|Date:||Oct 1, 1991|
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