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Research on Key Performance Evaluation Method Based on Fuzzy Analytic Hierarchy Process

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Advanced Hybrid Information Processing (ADHIP 2020)

Abstract

Under the traditional performance evaluation method, the financial analysis process lays too much emphasis on the integrity and the weight of performance factors is not clear, which leads to the poor aggregation degree of performance evaluation data. A key performance evaluation method based on fuzzy analytic hierarchy process (FAHP) is proposed. The data flow of performance indicators is established according to the current situation of the enterprise on the basis of the three indicators of work efficiency of financial staff, utilization of financial funds and overall financial operation efficiency. By applying data envelopment analysis and static tree analysis, a comprehensive analysis model is established. The index data flow is sampled according to the boundary performance value and the weight of each factor is calculated by using the idea of fuzzy hierarchy. After quantification, the final fuzzy evaluation is obtained and the key performance evaluation is realized. The experimental results show that compared with the traditional key performance evaluation method, the data aggregation degree of the designed key performance evaluation method is improved by 29%, and the overall scientific nature is stronger.

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Correspondence to Shi-han Zhang .

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhang, Sh., Qiu, G. (2021). Research on Key Performance Evaluation Method Based on Fuzzy Analytic Hierarchy Process. In: Liu, S., Xia, L. (eds) Advanced Hybrid Information Processing. ADHIP 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 347. Springer, Cham. https://doi.org/10.1007/978-3-030-67871-5_42

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  • DOI: https://doi.org/10.1007/978-3-030-67871-5_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67870-8

  • Online ISBN: 978-3-030-67871-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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