Optimization of Lateral Transfer Inventory of Auto Spare Parts Based on Neural Network Forecasting

Apply

Optimization of Lateral Transfer Inventory of Auto Spare Parts Based on Neural Network Forecasting

Journal Title: Journal of Intelligent Systems and Control - Year 2022, Vol 1, Issue 1

Abstract

Creating a fair replenishment strategy is one of the most significant instruments in the inventory management for automotive spare parts. It is also crucial to controlling the enterprise's inventory level. This study considers the significance of retailers' demand forecasting at the conclusion of the sales period to build a lateral transfer inventory optimization scheme with high scientific rigor, aiming to ensure the correctness and logic of the replenishment strategy. To provide a more scientific direction for the inventory management of an automotive spare parts company, this research constructs an upgraded particle swarm optimization (PSO)-backpropagation (BP) neural network prediction model, and a lateral transfer inventory optimization method based on demand forecasting. Finally, 26 retailers of Company B in Central China's Hunan Province were taken as examples to confirm the model's efficacy. The outcomes demonstrate an improvement in the lateral transfer's applicability in Company B.

Authors and Affiliations

Xinhao Shao, Daofang Chang, Meijia Li

Keywords

Inventory optimization Lateral transfer Demand forecasting Classification of automotive spare parts

EP ID EP731834
DOI https://doi.org/10.56578/jisc010102
Views 105
Downloads 1