|
Business Accent
Give early warning signals
Businesses want information on which they can act to prevent
losses. Sanjay Shah focuses on the early warning signals in the concluding
part of his series on the business benefits of BI
Sanjay Shah |
Giving early warning signals is like predicting weather, but
as they say a stitch in time saves nine! What is the use of knowing a customer
receivable has become doubtful. It is already too late? If some warning signals
were received a few months back, this could have been prevented. This is the
story everywhere. Businesses want information on which they can act to prevent
losses. By using BI technology intelligently, reports can be made more meaningful
and actionable. For example, in an AR Report of an organization which is geographically
wide-spread and there are thousands of customers, it would be very difficult
to find out the culprits. It would be the proverbial needle in the haystack.
However, it would be possible to apply business rules to ‘red-flag’
a customer. For example, you could apply business rules like:
- If the overdue amounts are >= 25% of total dues,
or
- If the total dues are > 25% of credit limit,
or
- If the potential interest loss is > 15% of overdue
amount, or
- If weighted average credit period is > 10% of
standard credit period, or
- If the sales trend for a customer is downwards,
but the AR balances are flat, or
- If there are no sales or no receipts from a customer
in a month
If any of the above conditions are true, then the system will flag the customer
as a red flag customer.
Such rules cannot be standardized. They have to be identified in consultation
with the customer. The results of application of such rules can be startling.
The report can single out the culprits amongst thousands of customers. You can
then quickly take corrective action.
Figure 6 categorizes the customers into Red Flag and Ok customers based on several
business rules.
Figure 7 expands the Red Flag, and shows the reason wise flagging. For example,
row 15 is a summary of all customers who are classified as ‘NM’ meaning
‘non-moving’. These are customers who have had no sale and no collections
in a month.
The user can then further drill down into any category to identify the customer,
as shown in Figure 8.
So, in a few clicks, from over 12,000 customers, we are able
to locate 10 for which action is to be taken!
Don’t trust the law of averages
The law of averages does not help in finding the culprits. The report must let
you study the base numbers from which the averages have been derived. Suppose
you have a budgeted inventory turns (consumption in a period divided by stock)
of 4, which translates to about 90 days inventory on hand. In actual also lets
say that you get an inventory turns of 4. At the overall level, this may sound
okay, but not so for many items which may be having a much lower inventory turns.
The inventory report must be able to drill down to the item level (culprit)
so that you can take corrective action.
A report such as the one given in Figure 9 can help you considerably.
Figure 9 shows at a summary level, F13 that the overall inventory turns are
4. However, the report in Figure 10 drills down to the item level, and shows
a different story.
The report in Figure 10 shows that there are several items where the inventory
turns is very low, and therefore a cause of concern !
(All the above examples are fictitious examples created for demonstration purposes.
The data was created in MS-SQL Server on which business rules were applied and
the report visualization was done in MS-Excel Pivot Tables.)
The key to a good BI solution is very close interaction with the actual decision-makers
and identifying the way they read and act on that report. This will give clues
to identify and apply business rules on raw data and create intelligent dimensions.
The activity of identifying such intelligent dimensions, is a continuously evolving
one. These intelligent dimensions are ultimately the difference between success
and failure of a BI application from the business users’ perspective.
Sanjay Shah is the CEO of Prosys Infotech, a Pune based
company specializing in developing BI solutions on the Microsoft BI platform.
He can be contacted at sanjay@prosysinfotech.com
|
