数据挖掘在精益管理效果评价中的应用

Sci. Program. Pub Date : 2022-01-10 DOI:10.1155/2022/3101614
Song Ding, Jun Li, Jiye Li
{"title":"数据挖掘在精益管理效果评价中的应用","authors":"Song Ding, Jun Li, Jiye Li","doi":"10.1155/2022/3101614","DOIUrl":null,"url":null,"abstract":"Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.","PeriodicalId":21628,"journal":{"name":"Sci. Program.","volume":"38 1","pages":"3101614:1-3101614:11"},"PeriodicalIF":0.0000,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Application of Data Mining in Effect Evaluation of Lean Management\",\"authors\":\"Song Ding, Jun Li, Jiye Li\",\"doi\":\"10.1155/2022/3101614\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.\",\"PeriodicalId\":21628,\"journal\":{\"name\":\"Sci. Program.\",\"volume\":\"38 1\",\"pages\":\"3101614:1-3101614:11\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sci. Program.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1155/2022/3101614\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sci. Program.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2022/3101614","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

定量评价是企业诊断的重要组成部分,促进了企业管理的科学化、现代化。目前,现有的企业管理评价方法无法完成对企业指标数据的挖掘,导致企业管理评价误差大,显著性系数低。因此,提出了数据挖掘在企业精益管理效果评价中的应用。分析了数据挖掘的过程和主要功能;采用数据挖掘算法建立精益管理效果评价指标体系,并计算指标权重。利用数据挖掘中的关联规则方法,根据企业精益管理水平评价指标和权重值的参数,通过模糊集变换思想,用隶属度来描述各指标和因素的模糊边界,构造模糊判断矩阵,通过多层复合计算得到最终评价结果。实验结果表明,本研究具有较高的显著性系数,提出的企业精益管理效果评价方法具有理想的准确性和较短的耗时。在实际应用中,累积贡献率较高,具有较高的稳定性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Application of Data Mining in Effect Evaluation of Lean Management
Quantitative evaluation is an important part of enterprise diagnosis, which promotes the scientific and modern management of enterprises. At present, the existing enterprise management evaluation methods cannot complete the mining of enterprise index data, which leads to large error and low significance coefficient in enterprise management evaluation. Therefore, the application of data mining in enterprise lean management effect evaluation is put forward. The process and main functions of data mining are analyzed; data mining algorithm is used to establish the evaluation index system of lean management effect and calculate the index weight. Using the association rules method in data mining, according to the parameters of enterprise lean management level evaluation index and weight value, through the fuzzy set transformation idea, the fuzzy boundary of each index and factor is described by the membership degree, the fuzzy judgment matrix is constructed, and the final evaluation result is obtained by multilayer compound calculation. Experimental results show that this study has a high significance coefficient, and the proposed evaluation method of enterprise lean management effect has ideal accuracy and short time consumption. In practical application, the cumulative contribution rate is higher and has higher stability.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
A Liquid Democracy Enabled Blockchain-Based Electronic Voting System Bike-Sharing Fleet Allocation Optimization Based on Demand Gap and Cycle Rebalancing Strategies Research on the Intelligent Assignment Model of Urban Traffic Planning Based on Optimal Path Optimization Algorithm Online Teaching Wireless Video Stream Resource Dynamic Allocation Method considering Node Ability The Path of Film and Television Animation Creation Using Virtual Reality Technology under the Artificial Intelligence
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1