Business processes digitalization in industry: assessing scrap metal contamination

Currently, ferrous metallurgy industry is experiencing growing trends in the scrap indicator, which requires a more responsible attitude and effective methods in subsequent redistribution of the production chain. Forecasts of ferrous scrap market development in the perspective up to 2030 have been given. Facts about the increasing trends of machine vision application in the industrial sector as a result of “machine vision and industry” keyword clustering based on publications in the NCBI PubMed database have been presented. The object of the study is enterprises of metallurgical industry, in particular for ferrous metal scrap harvesting. The subject of the study is the process of scrap contamination assessment. Comparison of the traditional process of scrap assessment and the digital one (based on machine vision application), which are visualized in the form of the implementation steps sequence algorithms, has been carried out. The hypothesis about more effective use of machine vision technology in the technological production process of scrap metal contamination assessment when it is accepted for further processing in comparison with the technology based on visual-expert assessment has been confirmed. The prospects for the use and commercialization of the digital service have been presented, as well as its impact on transparency and reliability of interaction among contractors. Scientific methods such as bibliometric analysis of literature devoted to the issues of applied methods and techniques for assessing the quality of unloaded scrap, comparative analysis of applied processes for assessing scrap clogging based on the presented algorithms, as well as synthesis methods that summarize the results were used.

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Journal
E-Management
Published
2024-12-17
DOI
https://doi.org/10.26425/2658-3445-2024-7-4-4-14
Primary Topic
Economic and Technological Systems Analysis
Type
article
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article

Business processes digitalization in industry: assessing scrap metal contamination

V. V. Degtyareva
E-Management
Economic and Technological Systems Analysis
article

Business processes digitalization in industry: assessing scrap metal contamination

V. V. Degtyareva
article en

Abstract

Currently, ferrous metallurgy industry is experiencing growing trends in the scrap indicator, which requires a more responsible attitude and effective methods in subsequent redistribution of the production chain. Forecasts of ferrous scrap market development in the perspective up to 2030 have been given. Facts about the increasing trends of machine vision application in the industrial sector as a result of “machine vision and industry” keyword clustering based on publications in the NCBI PubMed database have been presented. The object of the study is enterprises of metallurgical industry, in particular for ferrous metal scrap harvesting. The subject of the study is the process of scrap contamination assessment. Comparison of the traditional process of scrap assessment and the digital one (based on machine vision application), which are visualized in the form of the implementation steps sequence algorithms, has been carried out. The hypothesis about more effective use of machine vision technology in the technological production process of scrap metal contamination assessment when it is accepted for further processing in comparison with the technology based on visual-expert assessment has been confirmed. The prospects for the use and commercialization of the digital service have been presented, as well as its impact on transparency and reliability of interaction among contractors. Scientific methods such as bibliometric analysis of literature devoted to the issues of applied methods and techniques for assessing the quality of unloaded scrap, comparative analysis of applied processes for assessing scrap clogging based on the presented algorithms, as well as synthesis methods that summarize the results were used.

E-ManagementVol. 7(4)
State University of Management (RU)
Openalex Percentile: Top 60%
Economic and Technological Systems Analysis
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