Why Machine Learning is more Practical than Econometrics in the Real World

I've read several studies and articles that claim Econometric models are still superior to machine learning when it comes to forecasting. In the article, "Statistical and Machine Learning forecasting methods: Concerns and ways forward", the author mentions that, "After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined." In many business environments a data scientist is responsible for generating hundreds or thousands (possibly more) forecasts for an entire company, opposed to a single series forecast. While it appears that Econometric methods are better at forecasting a single series (which I generally agree with), how do they compare at forecasting multiple series, which is likely a more common requirement in the real world? In this article, I am going to show you an experiment I ran that compares machine learning models and Econometrics models for time series forecasting on an entire company's set of stores and departments.