Industry 5.0 and its technologies: A systematic literature review upon the human place into IoT- and CPS-based industrial systems

Valette, Etienne; El-Haouzi, Hind Bril; Demesure, Guillaume

doi:10.1016/j.cie.2023.109426

Cited by 26 publications

(6 citation statements)

References 78 publications

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“…Building on this concept, Industry 5.0, as proposed by the European Commission [10] retains the technological aspect of 4.0 but emphasizes three pillars: (a) business agility and resilience supported by advanced technologies, (b) prioritizing humans in innovation and competitiveness by focusing on skills development, and (c) embracing environmental sustainability and considering the product life cycle. A recent systematic literature review by Valette et al [11] underscores the rising importance of Cyber-Physical Systems (CPS) and IoT concepts in industrial research, with a strong emphasis on human-centricity. They advocate for viewing future industrial systems as complex socio-technical entities, where human and industrial assets are seen as interconnected.…”

Section: Literature Reviewmentioning

confidence: 99%

The Rise of Passive RFID RTLS Solutions in Industry 5.0

Bendavid,

Rostampour,

Berrabah

et al. 2024

Sensors

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In today’s competitive landscape, manufacturing companies must embrace digital transformation. This study asserts that integrating Internet of Things (IoT) technologies for the deployment of real-time location systems (RTLS) is crucial for better monitoring of critical assets. Despite the challenge of selecting the right technology for specific needs from a wide range of indoor RTLS options, this study provides a solution to assist manufacturing companies in exploring and implementing IoT technologies for their RTLS needs. The current academic literature has not adequately addressed this industrial reality. This paper assesses the potential of Passive UHF RFID-RTLS in Industry 5.0, addressing the confusion caused by the emergence of new ’passive’ RFID solutions that compete with established ’active’ solutions. Our research aims to clarify the real-world performance of passive RTLS solutions and propose an updated classification of RTLS systems in the academic literature. We have thoroughly reviewed both the academic and industry literature to remain up to date with the latest market advancements. Passive UHF RFID has been proven to be a valuable addition to the RTLS domain, capable of addressing certain challenges. This has been demonstrated through the successful implementation in two industrial sites, each with different types of tagged objects.

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Section: Literature Reviewmentioning

confidence: 99%

The Rise of Passive RFID RTLS Solutions in Industry 5.0

Bendavid,

Rostampour,

Berrabah

et al. 2024

Sensors

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“…Numerous benefits were discovered when Industry 5.0 was released, enabling development in Information and Communication Technology (ICT), including the Internet of Things (IoT), edge computing (EC), big data analytics, automation, and AI. Edge computing offers a variety of AI services, making services more stable, storing and processing information in real time, and ensuring safety [5,6]. EC is an open platform, incorporating networking tools, computing, applications, storage, data optimization, real-time business, application intelligence, security, and privacy, and being applied to numerous domains such as production, transportation, and energy [7].…”

Section: Introductionmentioning

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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“…On top of the existing knowledge gap in establishing enabling mechanisms, the limited commercialization of anticipated technologies is another major barrier. Notably, most emerging technologies of Industry 5.0, like cognitive cyber-systems or adaptive robotics, are far from widespread adoption within mainstream manufacturing firms (Müller, 2020; Valette et al ., 2023). This discrepancy has further pushed the realization of Industry 5.0 into the future.…”

Section: Introductionmentioning

confidence: 99%

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

Ghobakhloo,

Fathi,

Iranmanesh

et al. 2024

JMTM

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PurposeThis study offers practical insights into how generative artificial intelligence (AI) can enhance responsible manufacturing within the context of Industry 5.0. It explores how manufacturers can strategically maximize the potential benefits of generative AI through a synergistic approach.Design/methodology/approachThe study developed a strategic roadmap by employing a mixed qualitative-quantitative research method involving case studies, interviews and interpretive structural modeling (ISM). This roadmap visualizes and elucidates the mechanisms through which generative AI can contribute to advancing the sustainability goals of Industry 5.0.FindingsGenerative AI has demonstrated the capability to promote various sustainability objectives within Industry 5.0 through ten distinct functions. These multifaceted functions address multiple facets of manufacturing, ranging from providing data-driven production insights to enhancing the resilience of manufacturing operations.Practical implicationsWhile each identified generative AI function independently contributes to responsible manufacturing under Industry 5.0, leveraging them individually is a viable strategy. However, they synergistically enhance each other when systematically employed in a specific order. Manufacturers are advised to strategically leverage these functions, drawing on their complementarities to maximize their benefits.Originality/valueThis study pioneers by providing early practical insights into how generative AI enhances the sustainability performance of manufacturers within the Industry 5.0 framework. The proposed strategic roadmap suggests prioritization orders, guiding manufacturers in decision-making processes regarding where and for what purpose to integrate generative AI.

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Industry 5.0 and its technologies: A systematic literature review upon the human place into IoT- and CPS-based industrial systems

Cited by 26 publications

References 78 publications

The Rise of Passive RFID RTLS Solutions in Industry 5.0

The Rise of Passive RFID RTLS Solutions in Industry 5.0

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

Generative artificial intelligence in manufacturing: opportunities for actualizing Industry 5.0 sustainability goals

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