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Efficiency Evaluation of Operation Analysis Systems Based on Dynamic Data Envelope Analysis Models from a Big Data Perspective

机译:大数据视角下基于动态数据包络分析模型的运营分析系统效率评估

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The operating environment of power grid enterprises is complex with a range of monitoring indicators. To grasp the overall operational status in time and find the key affecting factors, Balanced Scorecard Card (BSC), Interpretive Structural Model (ISM), Principal component analysis (PCA) should be applied. This paper proposed several grid enterprise operators and monitoring indicator systems (which include achievement indicators and driver indicators), and applied PCA for verification and evaluation. The achievement indicators mainly reflected the effectiveness of business operations, which included corporate value, social value, customer value, and so on. Driver indicators mainly reflected the core resources and operations process management of business operations, which have a direct impact on the achievement indicators. The driver and achievement indicators were used as input and output indicators for the provinces to assess the efficiency of operations, and appropriate measures were proposed for improvement. The results showed that the dynamic data envelopment analysis (DEA) model could reflect the time lag of the grid enterprises operating investment and income much better than the other two methods, and the static changes compared to assess efficiency had an average around 4%.
机译:电网企业的运行环境复杂,具有一系列监控指标。为了及时掌握总体运营状况并找到关键的影响因素,应使用平衡计分卡(BSC),解释性结构模型(ISM),主成分分析(PCA)。本文提出了几种网格企业运营商和监控指标体系(包括绩效指标和驱动指标),并将PCA应用于验证和评估。绩效指标主要反映企业运营的有效性,包括企业价值,社会价值,客户价值等。动因指标主要反映业务运营的核心资源和运营流程管理,对绩效指标有直接影响。动因和绩效指标被用作各省的投入和产出指标,以评估业务效率,并提出了适当的改进措施。结果表明,动态数据包络分析(DEA)模型可以比其他两种方法更好地反映电网企业的运营投资和收益的时滞,静态评估效率的平均变化约为4%。

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