  Choosing the technique that is the best at forecasting your data, is a
problem that arises in any forecasting application. Decades of research have
resulted into an enormous amount of forecasting methods that stem from
statistics, econometrics and machine learning (ML), which leads to a very
difficult and elaborate choice to make in any forecasting exercise. This paper
aims to facilitate this process for high-level tactical sales forecasts by
comparing a large array of techniques for 35 times series that consist of both
industry data from the Coca-Cola Company and publicly available datasets.
However, instead of solely focusing on the accuracy of the resulting forecasts,
this paper introduces a novel and completely automated profit-driven approach
that takes into account the expected profit that a technique can create during
both the model building and evaluation process. The expected profit function
that is used for this purpose, is easy to understand and adaptable to any
situation by combining forecasting accuracy with business expertise.
Furthermore, we examine the added value of ML techniques, the inclusion of
external factors and the use of seasonal models in order to ascertain which
type of model works best in tactical sales forecasting. Our findings show that
simple seasonal time series models consistently outperform other methodologies
and that the profit-driven approach can lead to selecting a different
forecasting model.
