Logistics management for the future: the IJLRA framework

Chan, Hau-Ling; Choi, Tsan-Ming

doi:10.1080/13675567.2023.2286352

Cited by 3 publications

(1 citation statement)

References 72 publications

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“…They mainly approached the medicine domain, looking for ML tools to predict lung cancer disease [53,54]. The last two groups contain two authors for each group, with a very small impact: discovering technologies and operations in Industry 4.0, proposing a framework for logistic management, and material characterization using Industry 5.0 [55][56][57][58]. The full list of authors is listed in Figure 9.…”

Section: Authors' Explanationmentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

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Section: Authors' Explanationmentioning

confidence: 99%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Metaheuristic Tabu Search for Vehicle Scheduling: A Case Study of Healthcare Logistics

Ng,

Kerk,

Lim

et al. 2024

Intelligent Systems Reference Library

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Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis

Ciucu-Durnoi,

Delcea,

Stănescu

et al. 2024

Sustainability

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The rapid advancement of technology has led to significant milestones in industrial progress, surpassing previous capabilities and presenting new challenges for adaptation. The current phase of industrial revolutions is characterized by accelerated technological development, particularly in automation and digitalization. For instance, the global industrial robotics market was valued at approximately USD 43.0 billion in 2022 and is projected to reach USD 70.6 billion by 2028. The integration of human labor alongside robotic machinery, though a tangible reality, may still seem abstract in certain regions. Despite the recent announcement of the fourth industrial revolution, Industry 5.0 has quickly emerged as the new standard toward which industries aspire. This study performs a bibliometric analysis of articles published between 2020 and 2023 that explores the implications of these two industrial revolutions and the transition between them. Using the Clarivate Analytics’ Web of Science Core Collection, the study identifies 154 articles using the Biblioshiny package in R, which simultaneously discuss Industry 4.0 and Industry 5.0 within their titles, abstracts, or keywords. An impressive annual growth rate of 119.47% among the published papers included in the dataset underlines the interest of the research community in this field. Additionally, key findings include the identification of prominent sources, prolific authors, highly cited articles and their content, as well as common research themes explored across the analyzed papers. Among the most relevant sources in terms of the number of publications, the journal Sustainability plays a key role, holding the first position, followed by Applied Sciences, and Sensors. In terms of motor themes, digital transformation, artificial intelligence, the Internet of Things, and smart manufacturing have been found to play a key role. As a result, the present research contributes to understanding the rapid evolution from Industry 4.0 to Industry 5.0, highlighting key trends, influential research, and emerging themes that are shaping the future of industrial advancements.

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Logistics management for the future: the IJLRA framework

Cited by 3 publications

References 72 publications

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Metaheuristic Tabu Search for Vehicle Scheduling: A Case Study of Healthcare Logistics

Beyond Industry 4.0: Tracing the Path to Industry 5.0 through Bibliometric Analysis

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