Receivables Management
The decision on whether to grant or not to grant credit to a particular customer can be taken if certain subjective probabilities of the payment pattern of the customer can be specified based on an analysis of the customer's financial viability, the nature of profession or employment or business he is engaged in, his relationship with other suppliers, his past record of payments to other suppliers, etc. has been carried out. The decision to grant credit or not to grant credit is usually based on expected value calculations.
Example
A textile manufacturer must decide whether a credit of Rs.30 lakh can be extended to a new customer who manufactures dresses. The customer is expected to make a repeat purchase. The textile manufacturer on credit evaluation of the customer feels that the probability that the customer will pay is 0.85 and the probability that the customer will default is 0.15. If the customer pays for the first purchase, the probability that he will pay for the repeat purchase would increase to 0.95. The repeat purchase is also expected to be worth Rs.30 lakh. The cost of sales for both orders is 80 percent of sales value. Should the textile manufacturer grant credit?
The possibilities when credit is granted for the first order are as follows:
-
The customer defaults and no repeat purchase is allowed.
-
The customer pays and places a repeat purchase order.
The outcome of the repeat order could be that the customer pays or does not pay.
In the example, at the first decision node we have two choices: to allow credit or not to allow credit. If credit is allowed on the first order, the subsequent events are the customer will pay with a probability of 0.85 or he will not pay with a probability of 0.15. If the customer pays then at the second decision node he is expected to make a repeat purchase. The subsequent events on allowing credit for the second order are that the customer will pay with a probability of 0.95 or not pay with a probability of 0.05.
We can draw the following decision tree:
Figure
Evaluating expected pay-offs from right to left,
At node D2:
Decision - Go for 2nd order
|
|
prob.
|
x
|
pay-off
|
=
|
Exp. pay-off
|
|
Event: customer will pay
|
0.85
|
x
|
(6 + 4.5)
|
=
|
8.925
|
|
Event: customer will not pay
|
(+)0.15
|
x
|
0.8*(-)30
|
=
|
(-)3.6
|
|
|
Net Expected Pay-off
|
=
|
5.325
|
Therefore, by going for the 2nd order the net expected pay-off is Rs.4.5 lakh.
At node D1:
Decision - Allow Credit and go for 1st order
|
|
prob.
|
x
|
pay-off
|
=
|
Exp. pay-off
|
|
|
Event: customer will pay
|
0.85
|
x
|
(6 + 4.5)
|
=
|
8.925
|
|
Event: customer will not pay
|
(+)0.15
|
x
|
0.8*(-)30
|
=
|
(-)3.6
|
| |
|
|
Net Expected Pay-off
|
=
|
5.325
|
|
* (Note that for the event, customer will pay, we consider the total expected pay-off i.e. 6 + 4.5. This is because if the customer pays for the 1st order he will make a profit of Rs.6 lakh on the 1st order as well as make a net expected pay-off of Rs.4.5 lakh on the 2nd order which takes place as a result of the customer paying for the 1st order.)
Decision - Do not allow credit
Here the pay-off is 0.
We find that if the decision of allowing credit is followed then a net expected pay-off of Rs.5.325 lakh can be made as opposed to no pay-off by not allowing credit. Hence the decision of allowing credit is followed.