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MBA硕士论文_电网企业物资需求预测技术研究及应用DOC

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摘要
随着电力体制改革的进一步深化,国家电网公司“三集五大”建设的全面推进,物
资集约化管理理念的提出,给电网企业物资管理提出了更高的要求。电网企业物资需求
预测作为物资管理流程的信息起始点,是提升集约化管理水平的关键环节,而物资需求
预测的准确性与及时性制约着物资管理水平的发展。对电网企业物资需求进行精准预测,
一方面可以避免因多报而造成的资金积压、库存积压等现象,有效的提高物资集约化管
理水平,另一方面对于提高电网企业核心竞争力也有着重要的意义,合理、科学的进行
物资需求预测已逐步成为电网企业提升管理、谋求发展的重要课题

论文从物资需求计划的概念、内容、特点、制定方法等方面对物资需求计划进行了
详细阐述。通过对国内外管理实践和技术应用实践分析,根据国际通行的预测方法和工
具,对物资需求预测进行模型设计。在深入调研省级电网企业的基础上,针对其公司当
前生产运营状况,构建出与之相适应的物资需求预测模型,促进物资需求计划更加科学
合理高效,做到物资供应及时、成本消耗降低、仓储管理科学,促进物资管理水平的提
升,进而深化公司物力集约化管理。通过建立物资需求管理的“三模型”,即物资需求
影响因素模型、物资需求预测模型和物资类别匹配关系模型,形成一套差异化的年度物
资需求预测模型体系,提高物资需求的准确性和及时性,提高采购效率和资金利用率

依据 W 省电网企业物资类别分类,运用相应的预测模型验证历史数据,对比需求预
测的准确性,在此基础上预测未来两年的物资需求,最后得出结论并展望物资需求预测
的应用前景,为辅助省级电网企业物资需求计划工作提供参考依据

关键词:电网物资;物资需求预测;预测模型华北电力大学硕士学位论文
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Abstract
With the further deepening of the reform of electric power system, the comprehensive
promotion of the construction of the five set of China State Grid Corp, and the concept of the
management of material management, put forward higher requirements for the material
management of power grid enterprises. Power grid enterprise material demand forecast as the
material management flow to be the starting point of the information, is the key link to
promote the level of intensive management, and material demand forecast accuracy and
timeliness restricts development level of material management. Reasonable and scientific
forecast of demand of goods has gradually become important topic for power grid enterprises
to improve management, seek the development of power grid enterprise material demand for
accurate prediction, on the one hand, can be caused by more capital backlog, inventory
backlog phenomenon is avoided and the effective to improve the material level of intensive
management, on the other hand to improve the power grid enterprise core competitiveness
also has a important significance.
Paper illustrates the content, features, aspects of design flow, the development and the
forecast methods for material requirements planning. Present the condition of material
requirements planning and difficulties in power grid enterprises in details. Besides, the paper
concludes the advanced methods, practices and practical application of technical analysis
management of material requirements planning of domestic and international , based on
internationally accepted prediction methods and tools for material requirements forecast
model design. The provincial power grid enterprises in-depth research on the basis of demand
forecasting model for their company&39;s current production operating conditions, constructed
with suitable materials, material requirements planning to promote more scientific and
rational and efficient, timely supplies, reduce the cost of consumption, warehouse
management science, promote the upgrading of materials management level, thus deepening
intensive management of company resources. Demand management through the
establishment of materials three models that are the demand factors model, material demand
forecasting model and material categories match the relational model, the formation of a
differentiated annual materials demand forecasting model system to improve the accuracy and
timeliness of material needs, improve procurement efficiency and utilization of funds.
According to the material classification of power grid enterprises in W province, this
paper predicts models with historic data, compares the accuracy of demand forecasting, and
forecasts material requirements in the next two years, then makes the conclusion and outlook华北电力大学硕士学位论文
III
Material Demand prospects, provides a reference for the provincial power grid enterprises to
make material requirements planning.
Keywords: Power Grid Material, Materials Demand Forecasting, Forecasting Model华北电力大学硕士学位论文
IV
目 录
摘要.....I
AbstractII
第 1 章 绪 论..1
1.1 研究背景及意义.. 1
1.2 国内外研究现状.. 2
1.3 研究内容与目标.. 3
1.4 研究思路与方法.. 4
第 2 章 物资需求计划理论和预测模型分析..6
2.1 物资需求计划理论.......... 6
2.1.1 物资需求计划特点 ... 7
2.1.2 物资需求计划基本数据 ....... 7
2.1.3 物资需求计划的制定 ........... 7
2.2 国内外通行的物资需求预测方法.......... 8
2.2.1 定性预测 ....... 8
2.2.2 定量预测法 ... 9
2.3 物资需求预测模型研究.. 9
2.3.1 季节性预测法 ........... 9
2.3.2 灰色预测法 ... 9
2.3.3 神经网络预测法 ...........
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