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@article{203931,
        author = {Dr. Vikram Bansal and Dr. Deepthi. B},
        title = {A Systematic Review of Industry 4.0 Maturity Models for Assessing Industries Readiness},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1560-1570},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203931},
        abstract = {With the advent of the fourth industrial revolution, popularly known as Industry 4.0, the overall transformation of the manufacturing industry with digital integration and intelligent engineering has taken a giant leap forward in the direction of futuristic technology. However, the maturity models used to assess organisations’ readiness to implement Industry 4.0 technologies are ambiguous because enterprises’ aims, size, and scalability vary. This paper aims to examine the prevalence and trend of studies evaluating the maturity models to assess the enterprises’ readiness in Industry 4.0. Furthermore, the purpose of this article is to identify research gaps in the existing body of knowledge and outline potential new directions for investigation. A systematic review of the literature was done to discover articles that propose or evaluate maturity models for determining the enterprise’s readiness to embrace the fourth industrial revolution. The retrieved papers were then processed using a combination of descriptive and content analysis approaches to generate new conclusions, identify gaps in the literature, and identify novel research avenues. The findings revealed that the number of publications has expanded dramatically during the last three years, and the highest number of articles are published in 2021. Nonetheless, there is still potential for research in this area because existing research lacks conventional assessment frameworks, particularly for small and medium-sized firms in emerging nations.},
        keywords = {Industry 4.0, maturity models, Bibliometric Analysis, systematic review, SLR},
        month = {June},
        }