Neural Network Forecasting Method for Inventory Management in the Supply Chain

Abstract

Determining the optimal level of inventory comes down to the timeliness of the procurement and replenishment procedures, which ensure the minimum total costs associated with procurement and storage. The problem of insufficient prediction accuracy for inventory management arising in supply chains is considered. A neural network prediction model based on a Time-Delay Restricted Boltzmann Machine with unit delays cascades in the output and input layers is proposed. During the structural identification of this model, the neurons count in the hidden layer was calculated, and the parametric identification was performed based on the CUDA parallel processing technology. This improves the prediction efficiency by increasing the prediction accuracy and decreasing the computational complexity. Software has been developed by the Matlab package that realizes the offered method. The created software is used to implement the prediction in the supply chain management problem.