Organization of information for forecasting sales in the consumer goods industry

The organization of information is a critical factor for a good sales forecast in the fast-moving consumer goods industry, which produces and markets the so-called Fast Moving Consumer Goods. Generally, there are three dimensions for organizing information that need to be considered when structuring the forecasting: time, product and place. Here, I will make some considerations about the “product” dimension.

The organizational areas of a company, such as marketing, sales, production and logistics, need to forecast product sales at different grouping levels. For example, the product manager analyzes information at an aggregation level that allows him to assess the performance of the family he is responsible for. The production manager, on the other hand, needs SKU-level forecasting to try to optimize the sequencing of his lines. It is evident, therefore, the need to generate estimates of future demand at different grouping levels.

It is unreasonable for the sales forecast to be carried out for all possible combinations of data, firstly because the necessary effort would be enormous and, above all, because each organizational area would have a different number, since it would not be possible to guarantee that the sum of the forecasts made at the SKU level would give the same result as the forecast obtained through the consolidated sales series. Like this, it is necessary to decide at what level of organization the information will be treated in the forecast and how it will be fragmented (top-down) or consolidated (bottom-up) posteriorly.

The consumer goods industry usually organizes information for its sales forecasting process guided by an internal marketing vision, making sales forecasts for the product family within a given brand. While historically this approach has not been incorrect, the recent diversification of product lines makes possible new approaches that result in fewer errors. For this, the company needs to stop looking inwards and align its sales forecasting process with the pattern, or “moment”, of its customers' consumption.

For example, let's imagine a company with five different lines of business. snacks, sold in packs of 30, 80 and 200 grams. The consumer intends to buy a snack to consume at the cinema, but did not find the 30-gram package of his favorite brand. What is easier to happen: the consumer buys another brand in the individual 30-gram package or goes to the supermarket and buys his snack preferred in the 200 gram package? In general, the “moment of consumption” predominates in relation to “brand preference” and the consumer will choose another 30-gram snack. Therefore, forecasting by brand (“preference”) usually leads to a greater error than by packaging (“time of consumption”).

Obviously, this rule only applies if the positioning of the brands, that is, the customer profile, is the same. Consumer goods companies that have several brands of a given product, washing powder or juices, for example, but each brand is targeted at a different public, would not benefit from this practice. Companies with brands (or lines) that are substitutes from the consumer's point of view (negative sales correlation), yes, benefit greatly from this practice. It's worth taking the test!

References

<https://ilos.com.br/web/quais-sao-as-caracteristicas-de-um-planejamento-da-demanda-eficiente-analise-das-definicoes-fundamentais-e-estruturais-do-processo/>

<https://ilos.com.br/web/comparacao-entre-as-abordagens-top-down-e-bottom-up-para-previsao-de-vendas/>