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Analysis of institutional authors

Redchuk, AndresAuthor

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April 10, 2023
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Adoption Case of IIoT and Machine Learning to Improve Energy Consumption at a Process Manufacturing Firm, under Industry 5.0 Model

Publicated to:Big Data And Cognitive Computing. 7 (1): 42- - 2023-03-01 7(1), DOI: 10.3390/bdcc7010042

Authors: Redchuk, Andres; Walas Mateo, Federico; Pascal, Guadalupe; Tornillo, Julian Eloy

Affiliations

Univ Nacl Arturo Jauretche, Engn & Agron Inst, RA-1888 Florencio Varela, Argentina - Author
Univ Nacl Lomas Zamora, Engn Fac, RA-8659 Lomas De Zamora, Argentina - Author
Univ Rey Juan Carlos, Comp Sci & Engn Dept, Madrid 28933, Spain - Author

Abstract

Considering the novel concept of Industry 5.0 model, where sustainability is aimed together with integration in the value chain and centrality of people in the production environment, this article focuses on a case where energy efficiency is achieved. The work presents a food industry case where a low-code AI platform was adopted to improve the efficiency and lower environmental footprint impact of its operations. The paper describes the adoption process of the solution integrated with an IIoT architecture that generates data to achieve process optimization. The case shows how a low-code AI platform can ease energy efficiency, considering people in the process, empowering them, and giving a central role in the improvement opportunity. The paper includes a conceptual framework on issues related to Industry 5.0 model, the food industry, IIoT, and machine learning. The adoption case's relevancy is marked by how the business model looks to democratize artificial intelligence in industrial firms. The proposed model delivers value to ease traditional industries to obtain better operational results and contribute to a better use of resources. Finally, the work intends to go through opportunities that arise around artificial intelligence as a driver for new business and operating models considering the role of people in the process. By empowering industrial engineers with data driven solutions, organizations can ensure that their domain expertise can be applied to data insights to achieve better outcomes.

Keywords

0Artificial-intelligenceCodes (symbols)Energy efficiencyEnergy utilizationEnergy-consumptionEnvironmental impactFood industriesIiotIndustry 5Industry 5.0Learning algorithmsLow-code platformMachine learningMachine-learningManufacturing firmsOptimizationProcess manufacturingSustainable developmentUnited nation sustainable development goalUnited nationsUnited nations sustainable development goals

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Big Data And Cognitive Computing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2023, it was in position 25/144, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Theory & Methods.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.14. This 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: ESI Nov 14, 2024)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2 (source consulted: FECYT Feb 2024)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-08-06, the following number of citations:

  • WoS: 8
  • Scopus: 17

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 2025-08-06:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 145.
  • 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: 175 (PlumX).

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

  • The Total Score from Altmetric: 2.35.
  • The number of mentions on the social network X (formerly Twitter): 3 (Altmetric).

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) .