摘要
本文首先分析了物资需求预测的现状和挑战;然后提出了基于大数据的物资需求预测模型的优化与验证方法,包括模型框架选择、优化和性能评估等;接着构建了基于预测结果的优化物资采购决策模型,并通过模拟实验验证了其效果;最后提出了保障性措施,包括数字化环境保障、专业模型架构人才保障和制度保障,以确保物资需求预测与采购决策模型的有效发挥.
Abstract
This paper first analyzes the current situation and challenges of material demand forecasting;Then,an optimization and validation method for a material demand prediction model based on big data is proposed,including model framework selection,optimization,and performance evaluation;Subsequently,an optimized material procurement decision model based on prediction results is constructed,and its effectiveness is verified through simulation experiments;Finally,protective measures are proposed,including digital environment protection,professional model architecture talent protection,and institutional protection,to ensure the effective implementation of material demand forecasting and procurement decision model.
维普
万方数据