ML-Driven Demand Forecasting in Legacy ERP Environments

Session details:

Classic ERP systems apply forecasting methods that tend to struggle with newer changes in supply chains. This research investigates whether demand forecasts can be improved using ML (Machine Learning) models such as XGBoost (eXtreme Gradient Boosting) and LSTM (Long Short-Term Memory), which have been tested on the M5 Forecasting dataset. Data preparation and feature development allowed ML models to outsmart moving averages and ARIMA (AutoRegressive Integrated Moving Average) in following the details of the demand response. The findings demonstrate that with ML, accuracy in forecasting increases in the ERP system, helping with strategizing and planning inventory choices without upgrading the ERP application. This shows how to improve the application by using data insights at any scale.