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论文编号：	16118
作者编号：	2320234214
上传时间：	2026/6/8 11:23:49
中文题目：	A钢铁企业采购管理优化研究
英文题目：	Research on Procurement Management Optimization of A Steel Enterprise
指导老师：	张建勇
中文关键字：	采购管理；供应商绩效评价；层次分析法；智能预测；钢铁企业
英文关键字：	Procurement management；Supplier performance evaluation；Analytic Hierarchy Process (AHP)；Intelligent forecasting； Iron and steel enterprises
中文摘要：	钢铁行业作为国民经济的基础性产业，在工业化进程中承担着不可替代的资源供给与产业支撑功能。然而，当前钢铁行业正同时面临产能过剩、环保约束趋严、原材料价格大幅波动与下游需求结构深刻调整等多重压力，行业整体利润空间持续收窄。在此背景下，采购管理的战略价值愈发凸显——采购成本通常占钢铁企业总成本的70%至80%，采购管理水平的高低直接决定企业在成本竞争中的相对位置，并深刻影响库存资产质量、生产计划执行稳定性与供应链整体韧性。 本研究以华东地区A钢铁企业为案例，综合运用供应链管理理论、层次分析法（AHP）与机器学习需求预测方法，通过“问卷调查在前、深度访谈在后”的递进式调研设计，系统梳理了企业采购管理的现状，识别出制约企业采购绩效的三大核心问题：采购计划偏差率长期高达13.7%、供应商绩效评价体系存在指标单一与权重失当等系统性缺陷、采购全流程平均周期达52.3天且库存周转率仅为9.8次/年。三大问题之间相互关联、相互强化，共同造成年度综合经济损失约8820万元。 针对上述问题，本研究构建了“三位一体”的系统性优化方案。在采购计划管理方面，基于物料ABC分类建立差异化智能预测体系，A类重点物料采用LSTM深度学习模型，B类物料采用ARIMA-SVR组合方法，C类物料采用随机森林集成算法，预期将综合采购计划偏差率压降至8%以内。在供应商管理方面，引入层次分析法构建涵盖5个一级指标、17个二级指标的科学评价体系，系统展示判断矩阵构建、权重计算与一致性检验的完整过程，并将评价结果与采购份额分配机制直接挂钩。在采购流程方面，通过分级授权制度改革、合同范本库建设与流程信息系统联通三项措施组合发力，目标将采购周期压缩至30天以内。综合效益测算显示，三项优化措施年度预期净效益约8425万元，静态投资回收期约2.6个月。
英文摘要：	As a fundamental industry of the national economy, the iron and steel industry undertakes an irreplaceable role in resource supply and industrial support during the process of industrialization. However, the iron and steel industry is currently confronted with multiple pressures simultaneously, including overcapacity, stricter environmental constraints, sharp fluctuations in raw material prices, and profound adjustments in downstream demand structure, leading to a continuous narrowing of the overall profit margin in the industry. Against this background, the strategic value of procurement management has become increasingly prominent. Procurement costs usually account for 70% to 80% of the total costs of iron and steel enterprises. The level of procurement management directly determines an enterprise’s relative position in cost competition and profoundly affects the quality of inventory assets, the stability of production plan execution, and the overall resilience of the supply chain. Taking Enterprise A, an iron and steel firm in East China, as a case study, this research comprehensively applies supply chain management theory, the Analytic Hierarchy Process (AHP), and machine learning-based demand forecasting methods. Through a progressive research design of "questionnaire survey first and in-depth interview later", this study systematically sorts out the current situation of the enterprise’s procurement management and identifies three core problems restricting procurement performance: the long-term deviation rate of procurement plans is as high as 13.7%, the supplier performance evaluation system suffers from systematic defects such as single indicators and inappropriate weighting, and the average full-process procurement cycle reaches 52.3 days with an inventory turnover rate of only 9.8 times per year. These three problems are interrelated and mutually reinforcing, resulting in an annual comprehensive economic loss of approximately 88.2 million yuan. In response to the above problems, this study constructs a systematic "trinity" optimization scheme. In terms of procurement plan management, a differentiated intelligent forecasting system is established based on material ABC classification: the LSTM deep learning model is adopted for Class A key materials, the ARIMA-SVR combined method for Class B materials, and the random forest ensemble algorithm for Class C materials, which is expected to reduce the comprehensive procurement plan deviation rate to less than 8%. In terms of supplier management, the Analytic Hierarchy Process is introduced to build a scientific evaluation system covering 5 first-level indicators and 17 second-level indicators. The complete process of judgment matrix construction, weight calculation and consistency test is systematically demonstrated, and the evaluation results are directly linked to the procurement share allocation mechanism. In terms of procurement processes, three combined measures are implemented: the reform of the graded authorization system, the construction of a contract template library, and the connection of process information systems, aiming to shorten the procurement cycle to less than 30 days. The comprehensive benefit calculation shows that the three optimization measures are expected to bring an annual net benefit of about 84.25 million yuan, with a static payback period of approximately 2.6 months.
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