Application of Data Mining in Effect Evaluation of Lean Management
Author(s) -
Song Ding,
Jun Li,
Jiye Li
Publication year - 2022
Publication title -
scientific programming
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.269
H-Index - 36
eISSN - 1875-919X
pISSN - 1058-9244
DOI - 10.1155/2022/3101614
Subject(s) - computer science , data mining , fuzzy logic , lean manufacturing , index (typography) , enterprise data management , set (abstract data type) , process (computing) , knowledge management , enterprise software , operations management , artificial intelligence , engineering , world wide web , programming language , operating system
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.
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