West Science Information System and Technology Vol.
No.
April 2025, pp.
Artificial Intelligence in Industry 4.
0: A Bibliometric Analysis of Research Trends Loso Judijanto
IPOSS Jakarta
Article Info
ABSTRACT
Article history:
The integration of Artificial Intelligence (AI) into Industry 4.
0 has revolutionized industrial processes through the implementation of intelligent automation, predictive analytics, and interconnected This study conducts a comprehensive bibliometric analysis to map the research trends, thematic evolution, influential authors, and international collaboration networks within the domain of AI and Industry 4.
Data were retrieved from the Scopus database, focusing on peer-reviewed journal articles published between 2013 and 2024.
Using VOSviewer for data visualization, the analysis reveals five major thematic clusters, with AuIndustry 4.
0,Ay Aumachine learning,Ay AuInternet of Things,Ay and Ausmart manufacturingAy as dominant keywords.
The temporal mapping indicates a shift from core technical research toward more strategic themes such as sustainability, digital transformation, and Industry 5.
Author collaboration networks show regional clusters with limited interdisciplinary integration, while country-level analysis highlights India.
Germany.
China, and Italy as major The findings emphasize the field's dynamic growth and underscore the need for more inclusive, cross-disciplinary, and globally connected research agendas to fully realize the potential of AI in the context of Industry 4.
Received April, 2025 Revised April, 2025 Accepted April, 2025 Keywords:
Artificial Intelligence.
Industry 4.
Bibliometric Analysis.
Machine Learning.
Smart Manufacturing This is an open access article under the CC BY-SA license.
Corresponding Author:
Name: Loso Judijanto Institution: IPOSS Jakarta Email: losojudijantobumn@gmail.
INTRODUCTION
The Fourth Industrial Revolution, widely known as Industry 4.
0, marks a paradigm shift in the way industries operate by leveraging emerging technologies to Characterized by the integration of cyberphysical systems, the Internet of Things (IoT), cloud computing, and big data analytics.
Industry manufacturing into smart and interconnected systems .
Among these enabling technologies.
Artificial Intelligence (AI) plays a central role due to its ability to simulate human cognitive functions such as learning, decision-making.
The convergence of AI and Industry 4.
0 has led to a substantial transformation across sectors healthcare, and finance .
The application of AI in Industry 4.
0 is extensive, ranging from predictive maintenance and quality control to supply chain optimization and customer Machine algorithms, natural language processing.
Journal homepage: https://wsj.
westscience-press.
com/index.
php/wsist West Science Information System and Technology A 76 computer vision, and robotics are now integral to smart manufacturing and intelligent decision-making .
For example, predictive analytics powered by AI enables factories to reduce downtime and anticipate failures, while intelligent robotics facilitate real-time adaptation to production demands.
This evolution not only enhances operational efficiency but also fosters innovation, agility, and resilience in industrial systems.
Over the past decade, the academic and industrial interest in AI applications within Industry 4.
0 has grown exponentially.
Research in this area has become more multidisciplinary, involving fields such as engineering, computer science, data science, and business management.
Numerous studies have explored technical, strategic, and ethical dimensions of AI integration into industrial ecosystems .
As a result, the body of literature continues to expand rapidly, reflecting both the maturity of certain applications and the emergence of novel AI techniques tailored for industrial use cases.
Despite the rapid growth in publications, there is a pressing need to systematically analyze and synthesize the research landscape to identify prevailing trends, knowledge gaps, and future directions.
Bibliometric analysis serves as a powerful method to uncover the intellectual structure and evolution of a research field by analyzing patterns in academic publications .
Through quantitative techniques, bibliometric studies can reveal influential authors, institutions, countries, keywords, and thematic clusters, thereby offering a macrolevel understanding of the research In the context of AI and Industry 0, such analysis can illuminate how the field has progressed, what areas dominate current research, and which topics warrant further Previous bibliometric studies have addressed AI broadly or in relation to specific sectors, but a focused bibliometric analysis on AI in Industry 4.
0 remains limited.
While some studies have examined AI adoption in smart manufacturing or its impact on supply chain management, they often lack a holistic and comprehensive perspective of the research trends across disciplines and geographies .
As the field continues to evolve dynamically, a detailed bibliometric review can provide critical insights for scholars, practitioners, and policymakers seeking to navigate the complexities and opportunities at the intersection of AI and Industry 4.
Although the literature on Artificial Intelligence and its integration within Industry 4.
0 has experienced significant growth, the current body of research remains fragmented and lacks a cohesive understanding of its development The absence of a comprehensive bibliometric analysis hinders the ability of scholars and stakeholders to recognize emerging themes, influential contributors, and research gaps in the domain.
This limitation poses challenges in aligning academic research with industrial practices and policy formulation.
Without a clear mapping of the intellectual landscape, efforts to advance the practical and theoretical underpinnings of AI in Industry 4.
0 risk redundancy and inefficiency.
This study aims to conduct a bibliometric analysis of scholarly publications on Artificial Intelligence in the context of Industry 4.
0 to identify research trends, thematic evolution, and influential contributors within the field.
1 Industry 4.
0: A Socio-Technical Paradigm Industry 4.
0, first popularized by the German conceptualized as the fourth industrial (Industry 1.
, mass production (Industry 2.
and automation (Industry 3.
This revolution is characterized by the fusion of physical and digital systems through the adoption of cyber-physical systems (CPS).
Internet of Things (IoT), cloud computing, and big data analytics .
The core theoretical perspective underpinning Industry 4.
0 is the socio-technical systems theory, which posits that optimal organizational performance is achieved when the social and technical subsystems are jointly optimized .
From this perspective, the implementation of Industry 4.
0 technologies must not only focus Vol.
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April 2025: pp.
West Science Information System and Technology A 77 on technical upgrades but also consider organizational culture, human capital, and change management.
AI, as an intelligent decision-making and automation tool, contributes to the technical layer of Industry 0, enabling real-time data processing, selflearning efficiency and strategic agility .
2 Artificial Intelligence: The Cognitive Automation Engine Artificial Intelligence refers to the development of systems capable of performing tasks that normally require human intelligence.
These include learning, reasoning, problem-solving, perception, and language understanding .
The theoretical framework behind AI stems from cognitive science and machine learning.
It assumes that intelligent behavior can be modeled through algorithms that mimic human cognition or through statistical and probabilistic models that optimize decision outcomes over time.
is operationalized in Industry 4.
0 through technologies such as supervised and unsupervised learning, neural networks, deep learning, natural language processing (NLP), and computer vision.
These technologies are production lines, smart supply chains, human-machine collaboration, and intelligent quality control systems .
The adaptive systems theory is also relevant here, describing AI-enabled systems as those that can evolve and self-optimize in dynamic environmentsAia key trait of Industry 4.
Technology Acceptance Organizational Readiness In analyzing AI adoption within Industry 4.
0, several models offer theoretical Among them, the Technology Acceptance Model (TAM) .
and the Technology-Organization-Environment (TOE) framework are most widely used.
TAM
posits that the perceived usefulness and ease of use of a technology influence usersAo willingness to adopt it.
This model can explain the human factors influencing the integration of AI tools in industrial environments, particularly from an employee or managerial perspective.
The TOE framework expands the view by including organizational .
, resources, size, cultur.
and environmental .
, market dynamics, regulatory factor.
elements in determining technology adoption.
In the context of Industry 4.
0, these theories help explain not only individual acceptance of AI tools but also broader strategic and systemic integration within organizations.
4 The Knowledge Management Perspective Another critical perspective lies in knowledge management theory, which is relevant to the understanding of how organizations create, share, and apply knowledge, particularly in technologyintensive settings.
AI contributes to knowledge creation by extracting patterns from large datasets, enabling real-time insights, and supporting informed decisionmaking.
As such, the Nonaka and Takeuchi knowledge spiral model becomes relevant, especially in highlighting the transformation of tacit to explicit knowledge within industrial Industry environment.
AI systems function as enablers combinationAitwo crucial phases in the SECI Machine learning, for instance, allows firms to capture and analyze unstructured data, making implicit operational patterns explicit and actionable.
This reinforces the symbiotic relationship between human underscoring the necessity of continuous learning and innovation.
METHODS
This study utilizes a quantitative bibliometric analysis to explore the development, structure, and emerging trends in the research domain of Artificial Intelligence in Industry 4.
The data were sourced from the Scopus database, chosen for its extensive coverage of peer-reviewed literature across science, technology, and engineering disciplines.
A comprehensive Vol.
No.
April 2025: pp.
West Science Information System and Technology A 78 search was conducted using combinations of keywords such as AuArtificial Intelligence,Ay AuAI,Ay AuIndustry 4.
0,Ay and AuFourth Industrial Revolution,Ay connected by Boolean operators to ensure the inclusion of relevant articles.
The search was limited to journal articles published in English between 2013 and 2024, reflecting the period during which Industry 0 and AI have gained significant academic and industrial momentum.
After retrieving the dataset, a rigorous screening and data cleaning process was carried out to eliminate duplicates and unrelated entries.
The final dataset was then imported into VOSviewer, a widely used software tool for constructing and visualizing bibliometric networks.
Using VOSviewer, the study conducted coauthorship analysis, keyword co-occurrence analysis, and citation analysis to identify influential authors, journals, countries, and thematic clusters.
serve as key enablers of digital transformation across industries.
On the right side of the map, the red cluster centers around Aumachine learning,Ay Audeep learning,Ay Aupredictive maintenance,Ay and Aulearning systems.
Ay This cluster represents the AI-focused technical core of Industry 4.
0 research.
The strong linkages between terms like Aulearning algorithms,Ay Aucomputer vision,Ay and Aufault detectionAy highlight how machine learning techniques are being applied for real-time monitoring, forecasting, and automation within smart factories.
The presence of Audecision support systemsAy and Audecision makingAy suggests an increasing emphasis on AI-driven industrial contexts.
To the upper left, the green cluster revolves around Auinternet of things (IoT),Ay Aucloud computing,Ay Auedge computing,Ay and Aurobotics.
Ay This area of the map represents the infrastructural and cyber-physical aspects of Industry 4.
0, where data collection and connectivity are foundational.
The cooccurrence of terms like Audigital storageAy and AuIoTAy with AI-related keywords reflects how real-time data from interconnected devices is increasingly used to train intelligent models.
This cluster also signifies the importance of distributed computing systems in facilitating scalable AI applications.
Located on the left side of the map, the blue Audigital transformation,Ay Audigitalization,Ay Ausustainability,Ay and Auindustry 5.
Ay This grouping reflects the broader organizational and strategic dimensions of Industry 4.
including its evolution and long-term The presence of AusustainabilityAy alongside technological terms suggests a growing discourse on how AI and digital technologies can be harnessed to achieve environmentally responsible and socially inclusive industrial practices.
Moreover.
Auindustry 5.
0Ay hints at the emerging transition toward more human-centric innovation, where collaboration between humans and machines is emphasized.
RESULTS AND DISCUSSION
1 Keyword Visualization Co-Occurrence Network Figure 1.
Network Visualization Source: Data Analysis, 2025 At the center of the visualization is the term AuIndustry 4.
0,Ay shown as the largest and most connected node, signifying its dominant presence across the research Its central location and extensive connections indicate that it serves as the technological, strategic, and analytical Closely tied to it are prominent keywords like Aumachine learning,Ay Auinternet of things,Ay Ausmart manufacturing,Ay and Audata analytics,Ay reflecting the multidisciplinary nature of Industry 4.
0, where AI technologies Vol.
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April 2025: pp.
West Science Information System and Technology A 79 At the bottom center, the purple cluster encompasses keywords such as Ausmart manufacturing,Ay Aucyber-physical systems,Ay Audigital twin,Ay and Audecision making.
Ay This thematic area illustrates the integration of digital and physical production systems, a hallmark of Industry 4.
The close proximity of Audigital twinAy and Aucyber-physical systemsAy reflects the growing use of real-time simulation and modeling to enhance production efficiency and fault prediction.
The inclusion of Audecision makingAy emphasizes AIAos role in enabling autonomous, data-driven interconnected systems.
ethical, sustainable, and human-centric perspectives in technological transformation.
For instance.
Auindustry 5.
0Ay appears as a more collaboration between humans and intelligent systems, moving beyond the automationcentric view of Industry 4.
Similarly.
AublockchainAy and Audigital transformationAy are gaining prominence in more recent years as organizations explore decentralized, secure, and scalable infrastructure to support intelligent manufacturing ecosystems.
In contrast, keywords such as Audeep learning,Ay Aulearning systems,Ay Audecision systems,Ay Aupredictive maintenanceAy tend to appear in cooler tones, indicating they were already actively studied in earlier phases .
This shows that while these technical applications remain important, scholarly attention is increasingly expanding into more strategic and systemic concerns, such as sustainability and organizational innovation.
The map reveals an evolution in research focus, from core technical implementation toward broader interdisciplinary and societal considerations, signaling the maturation and diversification of the AI and Industry 4.
0 research field.
Figure 2.
Overlay Visualization Source: Data Analysis, 2025 This temporal analysis of keyword co-occurrence in the literature on Artificial Intelligence in Industry 4.
0, using color gradients to represent the average publication year of each Darker tones .
lue/purpl.
indicate older research .
round 2021Ae2.
, while lighter tones .
reen to yello.
represent more recent research trends .
pproaching 2023Ae The term Auindustry 4.
0Ay remains central and relatively mature, shown in green-blue, indicating it has been a consistent focal point over time.
Other foundational keywords like Aumachine learning,Ay Auinternet of things,Ay and Audata analyticsAy also appear in shades of green and teal, reflecting their establishment as core topics since the early development of AI in industrial contexts.
The yellowish areas, such as Ausustainability,Ay Auindustry 5.
0,Ay Aublockchain,Ay and Audigital transformation,Ay indicate newer or emerging themes.
These keywords suggest a shift in scholarly focus toward nextgeneration topics, especially the integration of Figure 3.
Density Visualization Source: Data Analysis, 2025 This density visualization map highlights the concentration and frequency of co-occurring keywords in the literature on Artificial Intelligence in Industry 4.
The areas shown in bright yellow and green indicate high-density regions, representing keywords that appear most frequently and are most strongly interconnected in the analyzed publications.
At the core of this heatmap is Auindustry 4.
0,Ay which has the highest intensity, reaffirming its role as the Vol.
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April 2025: pp.
West Science Information System and Technology A 80 central and most extensively studied concept.
Surrounding it are other high-density terms like Auinternet of things,Ay Aumachine learning,Ay Audata analytics,Ay and Ausmart manufacturing,Ay suggesting these topics are critical pillars within the research field and often co-occur in In contrast, keywords located toward the edges of the map, such as Ausustainability,Ay Aublockchain,Ay Auindustry 5.
0,Ay and Audecision support systems,Ay appear in cooler shades of blue and green, indicating they are less frequent but still present in the discourse.
This suggests these topics may be emerging or niche within the broader context.
While they are not yet dominant, their presence shows a gradual broadening of research focus to human-centric approaches .
s seen in "industry 5.
0"), and secure data infrastructures like blockchain.
2 Co-Authorship Visualization blue clusterAiincluding authors like Haleem .
Gunasekaran A.
Ivanov D.
, and Kumar S.
represents a more dispersed network, with collaborations likely stemming from Europe and South Asia, focused on broader strategic, organizational, or supply chain dimensions of Industry 4.
Figure 4.
Author Visualization Source: Data Analysis, 2025 This co-authorship visualization illustrates the collaboration patterns among prolific authors in the field of Artificial Intelligence in Industry 4.
The map is divided into distinct clusters, each representing groups of authors who frequently co-publish.
The red cluster, which is the most densely populated, features a tightly-knit group of researchersAisuch as Wang Y.
Zhang Y.
Liu Y.
, and Chen Y.
Aiwho appear to be leading contributors, often working with colleagues within the same regional or institutional networks, likely centered in East Asia.
The green cluster includes notable figures like Lee J.
Tao F.
, and Zhong R.
, suggesting strong collaborations in smart manufacturing and digital twin research, possibly spanning institutions in China.
Singapore, or Korea.
Meanwhile, the Figure 6.
Country Visualization Source: Data Analysis, 2025 This VOSviewer collaboration network visualizes the global distribution and cooperative relationships in the field of Artificial Intelligence in Industry 0 research.
The size of each node indicates the publication volume from that country, while the lines represent co-authorship or research collaboration.
India appears as the most prominent and central node, reflecting its leading role in publication output and particularly with countries like Spain.
Saudi Arabia, and South Africa.
Other major contributors include Germany.
China.
Italy, and the Russian Federation, each forming dense collaborative sub-networks often reflecting regional or linguistic ties.
The map also highlights increasing contributions from emerging economies such as Poland.
Portugal, and Iran, suggesting a broadening of research participation beyond traditional Western and East Asian powerhouses.
DISCUSSION