Warehouse Demand Forecasting based on Long Short-Term Memory neural networks

Abstract

In modern market it is very important to deliver products to customers fast. That delivery can be on site or to customer's homes. In order to achieve that it is important to have enough goods stored in warehouses and prepared for delivery. It is not a good decision to clutter up warehouses with the goods because space is limited and expensive and it makes it more complicated to collect orders. Those are the reasons why it is important that number of stored goods converge to the exact number of product units that will be ordered in the future. Demand forecasting tries to solve that problem. In this work demand forecasting algorithm based on Long Short-Term Memory recurrent neural network is described and compared with demand forecasting algorithms developed by authors before.