The main objective of this research is to identify current trends in Industry 4.0 within the manufacturing sector through bibliometrics. A dataset of 1069 documents from 2020 to 2024 obtained from the Web of Science is processed. Using the R-Bibliometrix package, research trends, leading authors, and institutional contributions are identified. The accelerated growth rate of 30.77% in publications confirms research interest. Thematic exploration reveals the convergence of Industry 4.0 with sustainability, AI, the Internet of Things, smart manufacturing, and digitalization as dominant themes. The transition towards smarter and more efficient systems is evident, with an emphasis on integrating sustainability into Industry 4.0 practices. Challenges persist in management adjustment, technological integration, and strategy for digital transformation. The study identifies sustainability and machine learning as critical enabling factors for Industry 4.0, while security and collaboration have emerged as key focus areas in recent years. A wide geographic distribution of research contributions with substantial international cooperation is observed, highlighting India, Italy, and China. Major journals like Sustainability and Journal of Manufacturing Systems emerge as influential platforms for disseminating research on the topic. The analysis of citation networks, co-occurrence, and thematic evolution underscores the multidimensional impact of Industry 4.0 technologies on manufacturing.

This study conducts an Academic Literature Analysis of 75 maturity models to clarify how Industry 4.0 and Industry 5.0 are being conceptualized and assessed. We map model scope, level structures, evaluated dimensions, and enabling technologies and complement descriptive statistics with exploratory non-parametric tests on the relationship between level structure and dimensional breadth. Results show a persistent dominance of Industry 4.0 models (≈92%), alongside a recent but steady emergence of Industry 5.0 and hybrid approaches in the latest models. Structurally, five-level schemes prevail, balancing diagnostic granularity and comparability. Content-wise, Technology and Digitalization, Processes and Operations, and Management and Strategy remain core, while People and Competencies and Innovation gain relevance; Sustainability and Social Responsibility and Human–Machine Interaction appear with the rise of Industry 5.0. We contribute (i) an operational definition of “hybrid” maturity models to make the I4.0→I5.0 transition measurable, (ii) a meta-typology of maturity levels explaining the five-level preference, and (iii) an evidence-based technology cartography across models. The findings suggest that future designs should retain the digital backbone of I4.0 while integrating explicit indicators for human-centricity, sustainability, and resilience with transparent weighting and scenario-based validation.

This article presents an academic literature analysis of 75 Industry 4.0 (I4.0) and Industry 5.0 (I5.0) maturity models published between 2020 and 2024, examining their empirical validation, sectoral scope, geographical origin, and stated applicability to developing-country contexts. The study combines descriptive profiling, contingency-table analyses with exact tests and effect sizes, and a large-scale synthesis of 562 research gaps reported by model authors. Knowledge production is highly concentrated in single-country studies (77.3%) and in developed economies, while most models do not explicitly or implicitly document applicability to developing-country settings (approximately 83%). Empirical validation practices are uneven, with multiple-case studies (33.3%) and surveys (24.0%) dominating, and sectoral coverage is strongly skewed toward manufacturing, limiting transferability to other sectors relevant for emerging economies. A statistically detectable association is observed between the development level of the model’s country of origin and the presence of applicability statements (${\chi }^2$ = 17.13, $p<0.05$, moderate effect size), whereas authorship configuration shows no substantive association. Thematic analysis of reported gaps highlights persistent deficits in empirical rigor, sectoral breadth, SME orientation, operationalization of human-centric and sustainability dimensions associated with Industry 5.0, availability of implementation tools, and longitudinal or predictive evidence. The article concludes by outlining a research agenda focused on context-aware validation designs, broader sectoral grounding, and greater transparency and reproducibility, supported by open access to all underlying data, codebooks, and taxonomies.