Industry 4.0 adoption in the textile sector: Global scientific trends and mapping (2013–2025)

Abstract

In a context of rapid industrial transformation and growing sustainability demands, Industry 4.0 has emerged as a key technological paradigm for the textile sector, historically characterized by labor intensity and low digitalization. Despite its potential for automation, flexibility, and traceability, adoption remains partial and uneven. This study addresses a major gap in the literature by providing a systematic and longitudinal mapping of the barriers to Industry 4.0 adoption in the global textile industry.

Using a quantitative bibliometric approach, 150 Scopus-indexed articles (2013–2025) were analyzed with Bibliometrix (R), Biblioshiny, and VOSviewer. The study combines performance analyses (output, citations, H-index) with structural analyses (co-occurrence, co-citation, and bibliographic coupling) to identify the field’s main themes, key contributors, and persistent barriers.

The results indicate an average annual growth rate of 24.6% and reveal four thematic clusters: (1) intelligent production technologies, (2) sustainability and circularity, (3) organizational adoption models, and (4) innovations in smart and functional textiles. The main obstacles are interconnected: high investment costs, lack of data science skills, limited interoperability, cybersecurity risks, and socio-technical resistance. The analysis also highlights geographic asymmetries, as Southern countries often produce knowledge that remains under-cited. Artificial intelligence emerges as a transversal enabler applied to predictive maintenance, simulation, automation, and adaptive management.

Overall, this study calls for a systemic reassessment of the barriers to adoption, integrating human, technical, and institutional dimensions. It offers a structured framework to guide the textile industry’s transition toward a smarter, more sustainable, and resilient model.

Classification JEL : O33, L67, M11, Q56

Paper type : Theoretical Research