Integrating Forecasting and Inventory Decisions Using Machine Learning

Abstract

Can inventory ordering decisions be improved by integrating forecasting and inventory decisions using machine learning? That is the question addressed in this study of three large Belgian companies in the food industry. Van der Haar, Sagaert, and Boute investigate the performance of methods that predict optimal order quantities directly, instead of !rst forecasting and then calculating optimal inventory quantities. Their results show that using an integrated approach can lead to substantial cost savings for smoother time series, yet the opposite holds when applying it to erratic and lumpy time series