| 摘 要: 近年来，人力资源和物流及仓储成本的不断攀升，导致零件制造成本不断上升，而准确的库存预测有助 于企业据此调整生产计划，降低制造成本，有助于实现企业利润最大化。本文通过PCA主成分分析方法确定影响企业 库存的因素，编写python代码分析出影响库存的主要因素，包括订单、当月销量等因素，提出JIT即零库存作为企业库 存管理的发展方向。随后选取影响库存的因素，分析并计算相关网络参数，建立BP神经网络，用MATLAB编写预测算 法，预测9月的库存，确认预测的合理性，验证了算法的有效性。 |
| 关键词: PCA主成分分析；BP神经网络；库存预测；Python；机器学习 |
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中图分类号: TP311
文献标识码: A
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| Research on Inventory Prediction Based on Principal Component Analysis and BP Neural Network |
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TENG Yanggang,CHEN Jinjie, GE Guilin
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( School of Machine Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China)
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| Abstract: In recent years,the ever-increasing costs of human resources,logistics,and warehousing have led to the rise of component manufacturing costs.Accurate inventory forecasts can help enterprises adjust production plans accordingly,reduce manufacturing costs,and help maximize profits.This paper adopts the PCA method to determine the factors that affect the inventory,writes python codes to analyze the main factors affecting the inventory,including orders,monthly sales and other factors,puts forward the JIT zero inventory as the development direction of enterprise inventory management.Then the factors that affect the inventory are selected,analyzing and calculating relevant network parameters,establishing BP neural network,using MATLAB to write the prediction algorithm,predicting the inventory in September,confirming the rationality of the prediction,and verifying the effectiveness of the algorithm. |
| Keywords: PCA (Principal Component Analysis);BP neural network;inventory prediction;Python;machine learning |
