Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry
In the current research, the dataset for conducting data mining calculations was generated based on a sample with 2,000 data, reports of the general manager of the textile industry of Iran's Ministry of Industry, Mine and Trade (information from 240 industrial units and 630 spinning and weaving...
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| Format: | Article |
| Language: | English |
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REA Press
2023-06-01
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| Series: | Computational Algorithms and Numerical Dimensions |
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| Online Access: | https://www.journal-cand.com/article_186205_681590a7779d7b362c9099866de80305.pdf |
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| author | Shahram Fatemi |
| author_facet | Shahram Fatemi |
| author_sort | Shahram Fatemi |
| collection | DOAJ |
| description | In the current research, the dataset for conducting data mining calculations was generated based on a sample with 2,000 data, reports of the general manager of the textile industry of Iran's Ministry of Industry, Mine and Trade (information from 240 industrial units and 630 spinning and weaving units were collected), and textile industry plants in Borujerd as the place for implementing the plan between 2015 and 2019, a period 6 month each year. Due to extensive information from the textile industry (with the help of the Ministry of Industry, Mine and Trade), the current research is unique. Using IBM SPSS Modeler 18, the most significant results of datamining calculations to extract knowledge are as follows, which are arranged based on main predictors of the research: predicting models of "strategy innovation in net with data code (A5)" with the prediction wight of 0.34; "technology innovation in net with data code (A1)" with the prediction wight of 0.30; "work environment innovation in net with data code (A3)" with the prediction wight of 0.16; Quality innovation in net with data code (A4)" with the prediction wight of 0.15; "employe innovation in net with data code (A2)" with the prediction wight of 0.10 are utilized to accurately analyze preventive maintenance in interaction with production. |
| format | Article |
| id | doaj-art-eca1ce1703204747a529189cde7d304c |
| institution | Kabale University |
| issn | 2980-7646 2980-9320 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | REA Press |
| record_format | Article |
| series | Computational Algorithms and Numerical Dimensions |
| spelling | doaj-art-eca1ce1703204747a529189cde7d304c2025-01-30T11:22:05ZengREA PressComputational Algorithms and Numerical Dimensions2980-76462980-93202023-06-0122637310.22105/cand.2023.432834.1084186205Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industryShahram Fatemi0Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.In the current research, the dataset for conducting data mining calculations was generated based on a sample with 2,000 data, reports of the general manager of the textile industry of Iran's Ministry of Industry, Mine and Trade (information from 240 industrial units and 630 spinning and weaving units were collected), and textile industry plants in Borujerd as the place for implementing the plan between 2015 and 2019, a period 6 month each year. Due to extensive information from the textile industry (with the help of the Ministry of Industry, Mine and Trade), the current research is unique. Using IBM SPSS Modeler 18, the most significant results of datamining calculations to extract knowledge are as follows, which are arranged based on main predictors of the research: predicting models of "strategy innovation in net with data code (A5)" with the prediction wight of 0.34; "technology innovation in net with data code (A1)" with the prediction wight of 0.30; "work environment innovation in net with data code (A3)" with the prediction wight of 0.16; Quality innovation in net with data code (A4)" with the prediction wight of 0.15; "employe innovation in net with data code (A2)" with the prediction wight of 0.10 are utilized to accurately analyze preventive maintenance in interaction with production.https://www.journal-cand.com/article_186205_681590a7779d7b362c9099866de80305.pdfpreventive maintenance systemsdata miningibm modelertextile industry |
| spellingShingle | Shahram Fatemi Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry Computational Algorithms and Numerical Dimensions preventive maintenance systems data mining ibm modeler textile industry |
| title | Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| title_full | Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| title_fullStr | Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| title_full_unstemmed | Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| title_short | Extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| title_sort | extracting knowledge of preventive maintenance using data mining technique in interaction with production within textile industry |
| topic | preventive maintenance systems data mining ibm modeler textile industry |
| url | https://www.journal-cand.com/article_186205_681590a7779d7b362c9099866de80305.pdf |
| work_keys_str_mv | AT shahramfatemi extractingknowledgeofpreventivemaintenanceusingdataminingtechniqueininteractionwithproductionwithintextileindustry |