Research on Data Management Capability Evaluation of Manufacturing Enterprises Based on Fermatean Fuzzy TOPSIS
- DOI
- 10.2991/978-94-6463-368-9_30How to use a DOI?
- Keywords
- Data management ability; Fermatean Fuzzy Set; TOPSIS method; Relative entropy; Combination weighting method
- Abstract
In order to promote the effective improvement of data management ability of Chinese manufacturing enterprises. This paper refers to the data of the 2018 China Enterprise-Labor Matching survey and selects the relevant data of a total of 10 manufacturing enterprises distributed in Guangdong, Hubei, Jiangsu, Sichuan, Jilin and other five provinces as sample data. This paper constructs an evaluation index system of manufacturing enterprise data management ability from six dimensions, adopts Fermatean Fuzzy TOPSIS method to evaluate the data management ability of ten manufacturing enterprises, and analyzes the data management ability of the enterprises with the highest and lowest ranking. The results show that: There are some problems in the data management of manufacturing enterprises in China, such as lack of awareness of data management and weak data management ability in the diversity of enterprise data collection subjects.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenjun Li AU - Mu Zhang PY - 2024 DA - 2024/02/14 TI - Research on Data Management Capability Evaluation of Manufacturing Enterprises Based on Fermatean Fuzzy TOPSIS BT - Proceedings of the 2023 5th International Conference on Economic Management and Cultural Industry (ICEMCI 2023) PB - Atlantis Press SP - 254 EP - 263 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-368-9_30 DO - 10.2991/978-94-6463-368-9_30 ID - Li2024 ER -