In my last post, I discussed the importance of leveraging data science to develop the most accurate forecasts possible. However, an assumption many companies make when building forecasting models is that historical sales are an accurate estimate of future demand. There are two risks in doing this:
1) Historical sales are a function of both primary demand and substitution effects due to stockouts.
2) Even if you could estimate both accurately, future primary demand will likely differ from historical primary demand.
In this post I will discuss an approach to solve the first problem.