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Driver Based Planning for Confectionery/FMCG Business

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:

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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:

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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.

 

 

 

 

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