Malte Tichy
After pursuing his PhD and postdoc research in theoretical quantum physics, Malte joined Blue Yonder as a Data Scientist in 2015. Since then, he has led numerous external and internal projects, which all involved programming python, creating, working with and evaluating probabilistic predictions, and communicating the achieved results.
Sessions
In many domains, machine learning methods predict the future demand of some physical good or virtual service that comes with finite capacity. Those predictions are then typically used to plan an appropriate level of supply. Often, it is not possible to directly measure (and to train on) the actual demand, but only on the fraction of it that could be fulfilled under the given constraints in the past – such as finite stocks or limited capacity. That is, one predicts a different quantity than one measures. This talk explores the various surprising aspects of the demand-sales-distinction that can arise in data science projects. We explore the paradoxes and the most dramatic problems that one encounters and find out how to avoid them. This talk will sharpen your thoughts when dealing with such intricate settings, and allow you to create and utilize demand forecasts in the best possible way.