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A systematic knowledge graph-based smart management method for operations: A case study of standardized management

Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources grea...

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Detalles Bibliográficos
Autores principales: Wen, Peihan, Zhao, Yiming, Liu, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539963/
https://www.ncbi.nlm.nih.gov/pubmed/37780784
http://dx.doi.org/10.1016/j.heliyon.2023.e20390
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author Wen, Peihan
Zhao, Yiming
Liu, Jin
author_facet Wen, Peihan
Zhao, Yiming
Liu, Jin
author_sort Wen, Peihan
collection PubMed
description Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on.
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spelling pubmed-105399632023-09-30 A systematic knowledge graph-based smart management method for operations: A case study of standardized management Wen, Peihan Zhao, Yiming Liu, Jin Heliyon Research Article Standardized routine operation management (SROM) has been widely accepted and applied by kinds of enterprises and played a key supporting role. With full use of the emerging knowledge-based smart management technology, SROM will further increase comprehensive efficiency and save human resources greatly at the same time, especially for small and medium enterprises (SMEs). Hence, we propose a systematic knowledge-based smart management method to transfer SROM activities from human operations to automatic response by means of knowledge explicitation, organization, sharing and reusing, which can be further achieved by employing knowledge graph. We took a typical SROM instance, ISO 9000 implementation management, as an example to validate the transformation from human activities to knowledge graph-based automatic operation. We firstly analyzed characteristics of domain knowledge and constructed an ontology model according to the knowledge stability. Secondly, a hybrid knowledge graph construction and dynamic updating framework together with related algorithms were designed by deliberately integrating semantic similarity calculation and natural language processing. Thirdly, we developed a question-answering mechanism and reasoning system based on the ISO 9000 implementation knowledge graph to support automatic decision and feedback for ISO 9000 routine operation management including knowledge learning and processes auditing. Finally, the practicability and effectiveness of SROM knowledge graph has been validated in a SME in China, realizing the application of question-answering, job responsibility recommendation, conflict detection, semantic detection, multidimensional statistical analysis. The proposed method can also be generalized to support auxiliary optimization decision, vertical risk control, operation mode analysis, optimization model improvement experience and so on. Elsevier 2023-09-22 /pmc/articles/PMC10539963/ /pubmed/37780784 http://dx.doi.org/10.1016/j.heliyon.2023.e20390 Text en © 2023 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Wen, Peihan
Zhao, Yiming
Liu, Jin
A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title_full A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title_fullStr A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title_full_unstemmed A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title_short A systematic knowledge graph-based smart management method for operations: A case study of standardized management
title_sort systematic knowledge graph-based smart management method for operations: a case study of standardized management
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539963/
https://www.ncbi.nlm.nih.gov/pubmed/37780784
http://dx.doi.org/10.1016/j.heliyon.2023.e20390
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