Council Post: Advanced Demand Forecasting using ML – My Experiments

The post-COVID supply chain world has become significantly more unpredictable and complex. Predicting customer demand is as important as ever, and a precise demand forecasting model is worth its weight in gold. Improving forecast accuracy aids in inventory optimization, warehouse resource/labor planning, supply & logistics planning, financial planning, and enhancing customer service levels.

It has become imperative for large organizations to invest in advanced forecasting. While statistical forecasting has been around for decades, this area of time series forecasting is evolving at a breakneck speed. Due to this speed of innovation, traditional supply chain planning platforms are sometimes not able to catch up with the latest technology and AI innovations. As a result, many enterprises today are investing in building advanced forecasting models to augment the base forecast coming out of the supply chain platforms.

As someone who is leading the inbound supply chain digital transformation of a Fortune 100 company, I have been fortunate to be in the middle of some of these cool innovations in advanced forecasting, where we are experimenting with the latest time series forecasting machine learning models. I have been regularly working with some of the public datasets in Kaggle competitions as a test bed for the advanced forecasting work, and here are some of my learnings from those experiments.

Never underestimate Data Preparation & EDA: While it is natural for us to want to jump straight into experimentation with various models, we must never underestimate the data preparation, EDA, and feature engineering portions.