Prediction of Warehouse Storage Demand Based on BP Neural Networks

DOI: https://doi.org/10.62517/jike.202404401

Author(s)

Tianjiao Wu1,*, Ran An2

Affiliation(s)

1School of Economics and Management, Jiao Zuo College of Industry and Trade, Jiaozuo, Henan, China 2Beijing Wuzi University, Beijing, China *Corresponding Author.

Abstract

With the rapid advancement of technology, the logistics industry has become an indispensable part of our daily lives. Warehousing, as the core component of the supply chain, plays a crucial role, serving both the needs of businesses and the sustainable development of the supply chain. Over the years, warehousing has evolved into a highly complex system that includes precise inventory management, accurate market demand forecasting, the utilization of cutting-edge logistics technologies, and the provision of various value-added services, all aimed at maximizing customer satisfaction [1]. As technology and products continuously evolve and enterprises mature, the room for optimization diminishes. This paper, based on an in-depth study of Warehouse X, explores production demand forecasting. To address the instability of supply and demand, the study uses the BP neural network method, implemented via MATLAB software, to predict storage needs for Warehouse X, with the aim of alleviating the inventory pressure on the enterprise.

Keywords

BP Neural Networks; Demand Forecasting; Warehouse Layout; Inventory Management

References

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