Measuring the effectiveness of credit operations poses a number of problems, simply because the objective of a credit function cannot be clearly separated from that of other departments of a business. Hence its net contribution to end results in terms of profits cannot be measured.

The main objective of a credit function is to maximise sales and minimise bad debt losses; however, maximising sales is also the primary objective of the sales department. Invariably departmental objectives are not clearly defined either by management or by the company's credit policy, perhaps because to varying degrees most objectives are competing objectives. Maximum sales cannot be obtained without additional exposure to bad debt losses, and yet minimum bad debt losses can be achieved by selling only to the safer risk accounts at the expense of maximum sales. Targets are thus difficult to set and subsequently measure against. Some of the most difficult measures include the contribution credit extension makes to sales results, the profitability of credit policy changes and the nature or manner in which collections are made. The effectiveness of the credit manager cannot be determined in precise terms because standards of measure are lacking and many other factors must be considered in a meaningful way to establish these standards. These factors include the credit manager's attributes, the extent of their responsibility and factors concerning the work situation.

In the main, measures are made of figures closely correlated to the department's operations and with this approach, the size and scope of the department is accommodated. Credit policies differ as widely as the size and organisational structure of some credit functions and these factors determine the extent of measure by way of adapted ratio analysis and use of various indices in the reporting of results.

Measuring the efficiency factor

The most important measure used in credit management is the measure for determining the efficiency factor. This is simply the rate at which remittances of credit sales are received over time; that is, the chronological patterns according to which the receivables created during a given interval are converted into cash. If a month is taken to be a standard unit of account, the issue is the liquidation rate for each month's credit sales. The efficiency factor therefore refers to the rate of account conversion into cash. This is determined by dividing the selected collection period into the sales total for the same period to identify the actual daily sales volume. Depending on the method of computation chosen the daily sales figure is then divided into the total debtors' balance at the end of the collection period, or alternatively, expressed as a percentage of thirty days, for each thirty-day period.

There are three approaches which are widely used in determining the efficiency factor, which, as an often-used misnomer, is referred to as the DSO or day's sales outstanding. The unreliability of two of the methods will be seen in a review of the effects of given circumstances, the results of which reflect the shortcomings of these approaches within the efficiency factor. In all three methods of review the total sales for the four-month calendar period will be the same, with only their distribution differing in order to demonstrate the differing sales profile effects of steady sales, rising sales and falling sales. By stipulation the manner in which collections are made shall be constant. This is often termed the 'collection experience'. The ageings of outstandings as a percentage of the debtors' total shall consistently reflect 10 per cent – ninety days; 20 per cent – sixty days; 50 per cent – thirty days; and 90 per cent – current sales.

The DSO (daily sales outstanding) method

This method of computing the efficiency factor is determined by the selected collection period (usually thirty days, sixty days or ninety days) being divided into the sales total for the same period, to reduce sales to a daily total, and then dividing this sum into the debtors' balance outstanding for that period. The figures are taken to one or two decimal places; rounding off to whole numbers may be misleading ¬– for example 48.44 becomes 48 and 48.56, becomes 49, when in fact the change is marginal. Table 1 shows the results obtained according to this method.

The schedule reveals the efficiency factor the credit manager would record at the end of each four-monthly period. Within a steady sales profile the factor is constant at 51.0 days, as neither the sales profile nor the collection period chosen influences the factor in any way.
In the rising profile, the factor falls to 39.0 days, suggesting an improvement in collection efficiency, yet by stipulation the manner of collection is constant. Furthermore, the choice of the collection period significantly changes the factor from 39.0 days, to 58.1 days, which in turn suggests a slowing down in collections.

In falling sales profile, the factor falls from 51.0 days to 72.0 days, which naturally would generate concern. By lengthening the collection period, which as the effect of averaging monthly sales, the factor is reduced to 48.0 days.

Clearly the efficiency factor is sensitive not only to the sales profile, but to the effects of aggregation through the collection period chosen, given that the nature of collection is constant and unchanged from period to period. No collection period can be considered as a 'happy medium', because comparable ambiguities prevail for any period chosen. For example, should a ninety-day collection period be chosen, the efficiency factor in the rising profile would suggest a deterioration in collection efficiency, when in monetary terms, ninety-day outstandings have reduced from $12,000 to $6,000. In the falling profile, although the factor at 48.0 days suggests an improvement in collections, in monetary value, 90 days' arrears have increased from $6,000 to $21,000, at a time of declining sales when debtors cash flow would be critical. For objective analysis and particularly as a basis for forecasting from debtors, the signals as revealed are clearly misleading. It should be emphasised that milder increases and decreases in the sales volume merely moderate the discrepancies.

A variation of the DSO method is to divide outstanding debtors by annual sales and multiply by 360, annual sales being established from a moving total of the last twelve months' sales. Even comparisons made at the same point in seasonal sales cycles contain permutations of the foregoing distortions unless all computations of the analysis period are identical. This condition of course is unlikely. Accordingly, a monitoring mechanism which transmits misleading signals about non-existent changes in collection efficiency may also fail to give the true warning when needed.

Table 1: Schedule illustrating the DSO method

The 'thumb rule' approach

The popularity of the 'thumb rule' approach lies perhaps in its simplicity: the current and preceding months' sales are deducted from a given debtors' balance, and the number of days involved in each month are noted. Any residual of the debtors' balance is then expressed in terms of days of the respective month's sales. Summation of the days noted represents the number of days debtors take to pay their accounts. Table 2 illustrates this method.
Although by stipulation the nature of collections made is consistent, and although the efficiency factors calculated in this way suggest a reduced influence of the sales profile, the variance nevertheless is substantial. A further shortcoming of this approach is that generally the nature of eollections is not accounted for and therefore is not reflected within the efficiency factor. It is possible to make substantial collections on current sales, particularly where large accounts are concerned, which may camouflage a deteriorating arrears situation.

Table 2: Schedule Illustrating the ‘Thumb Rule’ Approach

The 'relating to original sales' approach

The common element of distortion of the two foregoing methods of computation is that either sales profiles or balances are aggregated in the calculations, making it impossible to detect changes in the various components of credit sales or aged balances outstanding. The approach discussed here analytically separates the components of a debtors' balance by relating respective ageing groups back to the month of sales in which the balances were incurred. By illustrating the aged balance as a percentage of thirty days of the respective original sales totals, customer payment rates are automatically traced to their source. Appraisals of collections made readily follow, without the prejudices of sales profiles and cumulative effects of aggregation. Furthermore, the nature of collections are highlighted, such as any substantial movement in current sales collected. A variation of this method is to relate to actual days of each respective month instead of a time constant of thirty days. In either consideration the manner of calculation is the same: the monthly sales figure is divided by the residual balance in each age factor and multiplied either by thirty days as a time constant, or, for February, by twenty-eight days; for June by thirty days; for August by thirty-one days; or as the case may be. A small variance will reflect in the efficiency factor attributable to the time variation between months. As Table 3 illustrates, provided the collection manner remains unchanged, the same answers will prevail regardless of the sales volume or profile. Accordingly, actual changes in collection efficiency will be detected immediately and not concealed by aggregation or other outside conditions.

For any forecasting exercise based on this approach, the projected debtors' balance e or efficiency factor is determined in the same manner, by restricting consideration to the respective 'aged' components. The summation of these considerations represents the particular projections – a debtors' balance, efficiency factor, or even a cash flow total. Mathematically the forecasting exercise is more involved; however, appraising variances in results subsequently obtained would more accurately reflect genuine circumstantial changes and accordingly enhance its value as a reliable tool for measuring collection performance.

Table 3: Schedule showing the ‘relating to original sales’ approach

In Table 4, the efficiency factors which result from calculations using each of these three approaches, are shown.

Other approaches

The ‘ageing’ criterion

Another common device for monitoring receivables is the ‘ageing’ criterion. When one examines the respective sales profiles using this method (Table 5), the schedule reveals that in the rising profile collections have improved, whereas in the falling profile a deterioration in collections is suggested. This is understandable when one recognises that the most recent month’s sales always dominate the calculations. Thus, the proportion of total receivables in accounts less than thirty days’ old will naturally be relatively high in a period of rising sales, and low in a period of falling sales, even when the payment profile is stable. The result will be a continual series of spurious warning signals being flashed to the credit manager, simply in response to normal sales fluctuations. Only during the unusual intervals when sales are level from month to month will the indicator be of any potential use.

The ageing schedule suffers from another inherent deficiency. It is difficult to interpret meaningfully any figures that are contributed from differing sources but are constrained to add up to 100 per cent. The fact that, say, 37.5 per cent of a company’s receivables outstanding at a particular time are under thirty days’ old, while 62.5 per cent are over thirty days’ old, may not mean that there is an extraordinarily large number of overdue accounts and that receivables are out of control. It could merely be that an unusually, and desirably, high percentage of payments were made on the most recent month’s sales, leaving fewer of them outstanding and raising the apparent weight of older accounts.

The collection index

Another commonly used index is the collection index, which is computed by taking the total of collections made during a given period and dividing this by the total of receivables outstanding at the beginning of the period. The weakness of this index is that it does not highlight the nature of the collections made. A substantial collection could be made on current sales, still within terms, with overdues deteriorating further. Because collections would be an aggregate of a period and considered as a sum total, the index would conceal the early warning of a deterioration in the collection of overdues. Some people prefer to amplify the index by multiplying by 10 or 100 for ease of subsequent graphical illustration or charting. There is not distortion in doing this as long as consideration of the index, in fact any index, is based on the same criteria or computation for all considerations over all relevant periods under review.

The bad debts loss index

The bad debts loss index is used in most situations and is computed by dividing the total of bad debts for a period (usually six months or one year) by the total of credit sales for the same period. It invariably reveals a small percentage loss of total credit sales, although these losses in themselves may be quite substantial. This ratio has appeal with some sections of top management as indisputable evidence of efficiency in credit administration. However, a low index can be achieved quite easily by accepting only the bet credit risks, and therefore this approach does not accurately reflect performance efficiency. By contrast, other sections of management, particularly sales oriented management, view a low bad debts loss index as evidence of conservative and constrictive credit policy costing the business certain sales opportunities.

The 'past due' index

The 'past due' index is determined by dividing the total 'past due' (that is, over thirty days) by the total outstandings (debtors' balance) and multiplying by 100. When computed for several successive e periods, the index serves as a barometer, indicating whether the trend of the slower paying accounts in a ledger is slowing, or collections are in fact being made more quickly. This index would help to offset the concealing effects of the collections index as to the nature of collections made.

Other indices

There are a number of indices in use within certain areas of the credit vocation that may be commented upon where the use, adaptation, or value of that index is peculiar to a particular function. The credit manager of a large retail store may use a credit application acceptance/rejection ratio that would allow the measurement of performance and standards of an interviewing or credit assessment panel both individually and collectively. The same ratio could also be used for evaluating credit policy.

A range of credit costs may be converted to ratios. These include:
1. legal court costs;
2. bad debts written off less recoveries;
3. credit investigation costs;
4. mercantile agency costs;
5. training costs for credit staff;
6. doubtful debts to bad debts etc.

Traditionally, methods of measuring credit efficiency and performance have been based on past results and on statistics closely correlated to the credit functions operations. However, with the aid of technology, business standards and results are becoming increasingly demanding and exacting, with a greater emphasis on forward budgeting and planning. Setting forward goals allows for more exacting measurement of performances and contribution to overall objectives. Accordingly, credit management of the future will no doubt become more closely scrutinised and monitored so that the contribution by the function may be maximised for the overall benefit of the business.

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