Driver Based Planning
for Confectionary/FMCG Business
For any FMCG/Confectionery Major to grow continuously and to maintain its position in the market, the organization has to develop the DNA to innovate and invest on new markets to arouse new consumption habits, aiming a growing target, especially with the Research and Development department, one of the main forces of the company, bringing daily new confectionery concepts. One of the Key areas is to get the profitability of their various brands and even SKUs in different geographies or regions and even by customers. The organization can then make their long term decisions based on this analysis. These decisions could be:
And many more, based on the organizations needs and requirements.
To gather such
analysis, we would require several data points at a regular intervals and we
need to analyze the cost of collections of these data points should be less
than the benefits that we accrue by making decisions from them.
We had provided
a similar solution to one of our clients. The client ask was very specific as
below:
Getting Profit
and Loss statements at the below levels:
Now, with so
much data coming at regular intervals, it may be easy to get the revenues
generated for each SKU, region, Customer etc, but it becomes quite difficult to
manually enter costs for the same and there we would need drivers which would
not only help us allocate various costs spend at overall organization level to
each SKU, but also specific spends at brand level to be allocated to each SKU.
Similarly, FMCG business requires a lot of spending on developing the sales
channels in terms amount invested on Modern trade, Traditional trade, stockiest
and distributors and at the end it becomes quite difficult to identify right
drivers so that all these costs would be allocated to right product level.
The biggest challenge
in implementing this driver based planning/budgeting/forecasting is getting the
people of the organization to be aligned around the basic framework. Each
employee of the organization should be provided the clarity as what is their
accountability and ownership. Once they have their roles and responsibilities
defined and the use of drivers as why we are using them and what their impact
would be in the overall scheme of things, they would be able to contribute
which would be beneficial to the organization. Though each region or market may
have some different drivers but we would have to find some commonality amongst
them and simultaneously give options for custom features specific to the
market.
Some of the
common calculations used are:
Gross Sales(KG)
= Volumes(KG)*MRP*ConversionFactor
Conversion
Factor(KG) = 100/(No. of units in Each SKU * Grammage of Each unit within SKU)
Calculations of
Realization for each SKU based on the channel mix and Market Mix
Calculations of
Freight both primary and secondary based on the truck used and the volume of
SKU that could be placed on that Truck Volume.
Sea freight in
case the Product is imported and the custom duty involved.
Returns,
Damages as a percentage of Production.
Expired
Products and cost of destruction of returned and expire goods as a percentage
of Goods Sold.
Warehouse costs
and overhead costs would also matter on the turnover as they are more or less
fixed over a tenure with little variable costs. So greater the turnover, lesser
the per kg cost allocated.
Taxes and product
grants provided to the customer based regions.
Green points
based on the norms set by government for each region and geography.
The serious problem
is if we have good data on drivers. Getting the ratios right for all brands and
different SKUs involved. Generally, organizations may use a common driver
across all brands and Products involved, but doing so may not result in effective
allocations of costs. It is necessary to identify the fixed parts and the
variable parts and once we have that clarity we can bring them in the calculations.
This would in turn help us perform the break-even analysis and the profitability
of each SKU/Product/Brand. If we can gather data to build models to include
data from distribution outlets and sales channels, it will be practical to
bring that kind of insights into the model which would provide the extra edge
to take meaningful decisions. But, we need to be cautious that while building
these models is to get too much involved into it theoretically and spreadsheets
and not able to correlate this drives with actual business. We would be able to
add all the additional details, but it may not bring in that benefits which would
help you take insightful decisions. For line items that do not see much
movements, driver-based planning is perhaps not the best choice. Traditional or
choice-based planning would be better, depending on whether you're dealing with
a discretionary or non-discretionary expense.