摘要
供应链管理随着全球化和信息技术的快速发展而在企业运营中越来越重要。供应链环境日益复杂,市场竞争日趋激烈,传统库存管理方法已经难以实时、准确地反映供应链的实际情况,导致企业出现库存积压、缺货等问题。大数据技术的出现为解决此问题提供了新的思路。文章构建了一套融合多源异构数据分析、智能预测算法与实时响应能力的生产计划管理体系,系统性地提出了“数据驱动决策”的新模式。通过深挖数据价值来提升预测准确性、优化资源调度和强化扰动应对能力,最终赋能企业实现柔性、高效与精益生产。分析实际案例可得,该策略有效提高了产能利用率与订单交付率,降低了运营成本。
With the rapid development of globalization and information technology,supply chain management has become increasingly important in enterprise operations.As the supply chain environment grows more complex and market competition intensifies,traditional inventory management methods can no longer accurately reflect real-time supply chain conditions,leading to issues such as inventory overstocking and stockouts.The emergence of big data technology offers innovative solutions to these challenges.To address this issues,the paper proposes a production planning management system that integrates multi-source heterogeneous data analysis,intelligent predictive algorithms,and real-time responsiveness.By deeply mining the value of data to improve prediction accuracy,optimize resource scheduling,and strengthen disturbance response capabilities,this approach ultimately empowers enterprises to achieve flexible,efficient,and lean production.Analysis of actual cases shows that this strategy has effectively improved capacity utilization and order delivery rates while reducing operating costs.
作者
孙鹏轩
SUN Pengxuan(AVIC Xi’an Aircraft Industry Group Co.,Ltd.,Xi’an Shaanxi 710000,China)
出处
《信息与电脑》
2025年第16期55-57,共3页
Information & Computer
关键词
大数据分析
生产计划
精准制定
动态调整
big data analytics
production planning
precise formulation
dynamic adjustment
