If you start browsing the web about business forecasting, you might cross both sales forecasting and demand forecasting. The two concepts are tightly related but not completely identical. I will try to outline the differences in this post, the distinction being somewhat subtle.

Sales forecasting is the most straightforward: you take your sales history as input in order to produce a sales forecast. This is the bread and butter of most Lokad sales forecasting add-ons. For most retail products, this approach is already fairly efficient. Indeed, sales are the only reliable quantitative indicator available about the customer demand for products.

Yet, it happens that sales data end-up with bias, for example

  • No products are left on the shelf, that’s the inventory rupture. Sales go to zero, although there is certainly a demand for the product. In that situation, sales data are under-estimating the demand.

  • A temporary promotion is applied to the product. Sales go up, but mostly due to the promotion. Although there might be a residual effect after the end of promotion, sales are going to decrease afterward. In this situation, sales data are over-estimating the demand.

eCommerce have their own specific bias as well

  • Temporary front-page display that might significantly increase the product exposure to the customers.
  • Upgrade of the product picture and/or description that suddenly increase the demand because the product looks more attractive.

If you want to produce a demand forecast, then you need to use the demand history as input. In practice, it means that you need to correct (probably manually) your sales history to reflect the demand. For example, you might replace the zeroes caused by ruptures by the sales amounts that “would have been expected” if the product would have been available. Since demand bias are very business specific, such corrections usually require human expertise to be carried out.

We have only scratched the surface of the topic, stay tuned …