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Analysis of institutional authors

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New Business Models on Artificial Intelligence-The Case of the Optimization of a Blast Furnace in the Steel Industry by a Machine Learning Solution

Publicated to:Applied System Innovation. 5 (1): 6- - 2022-02-01 5(1), DOI: 10.3390/asi5010006

Authors: Redchuk, Andres; Walas Mateo, Federico

Affiliations

Univ Nacl Arturo Jauretche, Inst Ingn & Agron, B1888, Florencio Varela, Argentina - Author
Univ Rey Juan Carlos, Escuela Tecn Super Ingn Informat, Madrid 28933, Spain - Author

Abstract

This article took the case of the adoption of a Machine Learning (ML) solution in a steel manufacturing process through a platform provided by a Canadian startup, Canvass Analytics. The content of the paper includes a study around the state of the art of AI/ML adoption in steel manufacturing industries to optimize processes. The work aimed to highlight the opportunities that bring new business models based on AI/ML to improve processes in traditional industries. Methodologically, bibliographic research in the Scopus database was performed to establish the conceptual framework and the state of the art in the steel industry, then the case was presented and analyzed, to finally evaluate the impact of the new business model on the operation of the steel mill. The results of the case highlighted the way the innovative business model, based on a No-Code/Low-Code solution, achieved results in less time than conventional approaches of analytics solutions, and the way it is possible to democratize artificial intelligence and machine learning in traditional industrial environments. This work was focused on opportunities that arise around new business models linked to AI. In addition, the study looked into the framework of the adoption of AI/ML in a traditional industrial environment toward a smart manufacturing approach. The contribution of this article was the proposal of an innovative methodology to put AI/ML in the hands of process operators. It aimed to show how it was possible to achieve better results in a less complex and time-consuming adoption process. The work also highlighted the need for an important quantity of data from the process to approach this kind of solution.

Keywords
0Artificial intelligenceBusiness modelIndustry 4Industry 4.0Low-code solutionMachine learningNo-codeNo-code/low-code solutionSmart manufacturing

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Applied System Innovation due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2022, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Industrial and Manufacturing Engineering.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.06, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions May 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-05-14, the following number of citations:

  • WoS: 7
  • Scopus: 12
  • OpenCitations: 7
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-05-14:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 114 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Argentina.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Redchuk Cisterna, Andrés) .

    the author responsible for correspondence tasks has been Redchuk Cisterna, Andrés.