Abstract: In the age of Machine Learning, many data scientists have started using more complicated approaches for forecasting, such as XGBoost, k-NN, Artificial Neural Networks etc. Due to the increased interest in these methods, the simpler and robust approaches might look less attractive than the more innovative ones and as a result tend to be neglected. However, the good old exponential smoothing still works well in many situations and is still widely used in practice, especially in demand planning. In this talk, Ivan will give a short overview of the exponential smoothing methods, explain why they have been so popular and discuss how they can be used in the modern demand forecasting in several cases, including FMCG, promotional modelling and slow moving products.

This talk will be based on the similar talk I have already given a couple of times, for example, in the CMAF FFT webinar in December 2023.