Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning
The task of rational organization of auxiliary processes at the enterprise is to reduce their cost by deep integration into the main production process. The purpose of the article is to develop a classification analysis algorithm for assessing the dependencies between the main and auxiliary units...
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| Format: | Article |
| Language: | English |
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Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education
2024-02-01
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| Series: | Омский научный вестник |
| Subjects: | |
| Online Access: | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%E2%84%961%20(189)%20(%D0%9E%D0%9D%D0%92)/12-19%20%D0%9C%D0%B0%D0%BB%D1%8B%D1%88%D0%B5%D0%B2%D0%B0%20%D0%A2.%20%D0%92..pdf |
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| _version_ | 1832573343875727360 |
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| author | T. V. Malysheva |
| author_facet | T. V. Malysheva |
| author_sort | T. V. Malysheva |
| collection | DOAJ |
| description | The task of rational organization of auxiliary processes at the enterprise is to reduce
their cost by deep integration into the main production process. The purpose
of the article is to develop a classification analysis algorithm for assessing the
dependencies between the main and auxiliary units and the typology of production
processes according to the level of intra-factory cooperation. As a method for
determining the type of production, the Random Forest machine learning method
using the bagging machine learning meta-algorithm is proposed. Parameters have
been developed that describe the costs of auxiliary operations, the costs of repair
facilities and equipment maintenance, the level of technical efficiency of production.
Approbation of the algorithm on the example of chemical enterprises made it
possible to distinguish three types of production according to the nature of intraplant cooperation of processes according to the most informative parameters. To
assess the usefulness and performance of the models, cumulative lift diagrams are
constructed, where the most productive type is determined with an average level of
intra-factory cooperation. The results are the primary diagnostics of the organization
of auxiliary facilities, decision-making on the reengineering of processes in order to
strengthen intra-factory cooperation and reduce costs. |
| format | Article |
| id | doaj-art-353b7144d8664d79b35ecd43596296e9 |
| institution | Kabale University |
| issn | 1813-8225 2541-7541 |
| language | English |
| publishDate | 2024-02-01 |
| publisher | Omsk State Technical University, Federal State Autonoumos Educational Institution of Higher Education |
| record_format | Article |
| series | Омский научный вестник |
| spelling | doaj-art-353b7144d8664d79b35ecd43596296e92025-02-02T04:33:21ZengOmsk State Technical University, Federal State Autonoumos Educational Institution of Higher EducationОмский научный вестник1813-82252541-75412024-02-011 (189)121910.25206/1813-8225-2024-189-12-19Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learningT. V. Malysheva0https://orcid.org/0000-0003-4306-0188Kazan National Research Technological UniversityThe task of rational organization of auxiliary processes at the enterprise is to reduce their cost by deep integration into the main production process. The purpose of the article is to develop a classification analysis algorithm for assessing the dependencies between the main and auxiliary units and the typology of production processes according to the level of intra-factory cooperation. As a method for determining the type of production, the Random Forest machine learning method using the bagging machine learning meta-algorithm is proposed. Parameters have been developed that describe the costs of auxiliary operations, the costs of repair facilities and equipment maintenance, the level of technical efficiency of production. Approbation of the algorithm on the example of chemical enterprises made it possible to distinguish three types of production according to the nature of intraplant cooperation of processes according to the most informative parameters. To assess the usefulness and performance of the models, cumulative lift diagrams are constructed, where the most productive type is determined with an average level of intra-factory cooperation. The results are the primary diagnostics of the organization of auxiliary facilities, decision-making on the reengineering of processes in order to strengthen intra-factory cooperation and reduce costs.https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%E2%84%961%20(189)%20(%D0%9E%D0%9D%D0%92)/12-19%20%D0%9C%D0%B0%D0%BB%D1%8B%D1%88%D0%B5%D0%B2%D0%B0%20%D0%A2.%20%D0%92..pdfauxiliary productionproduction processesintra-factory cooperationalgorithmclassification analysismachine learningrandom forest |
| spellingShingle | T. V. Malysheva Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning Омский научный вестник auxiliary production production processes intra-factory cooperation algorithm classification analysis machine learning random forest |
| title | Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning |
| title_full | Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning |
| title_fullStr | Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning |
| title_full_unstemmed | Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning |
| title_short | Development of the algorithm for classifying industries according to the type of intra-factory cooperation of main and auxiliary processes using machine learning |
| title_sort | development of the algorithm for classifying industries according to the type of intra factory cooperation of main and auxiliary processes using machine learning |
| topic | auxiliary production production processes intra-factory cooperation algorithm classification analysis machine learning random forest |
| url | https://www.omgtu.ru/general_information/media_omgtu/journal_of_omsk_research_journal/files/arhiv/2024/%E2%84%961%20(189)%20(%D0%9E%D0%9D%D0%92)/12-19%20%D0%9C%D0%B0%D0%BB%D1%8B%D1%88%D0%B5%D0%B2%D0%B0%20%D0%A2.%20%D0%92..pdf |
| work_keys_str_mv | AT tvmalysheva developmentofthealgorithmforclassifyingindustriesaccordingtothetypeofintrafactorycooperationofmainandauxiliaryprocessesusingmachinelearning |