Purpose The Fourth Industrial Revolution (Industry 4.0) is transforming industrial maintenance into a strategic, technology-driven function. Maintenance 4.0, particularly predictive maintenance (PdM), leverages IoT, AI and Big Data to enhance asset reliability, reduce downtime, lower costs and improve overall operational efficiency. While these innovations promise substantial competitiveness gains, challenges such as high investment costs and specialized expertise persist. This study conducts a systematic literature review to map current Industry 4.0 maintenance technologies, identify their benefits, and explore implementation barriers. By systematizing existing knowledge, it fills a critical gap, providing insights into Maintenance 4.0's potential to drive industrial modernization and sustainable performance. Design/methodology/approach This study employed a structured methodological framework combining exploratory research, a systematic literature review (SLR), and descriptive and content analyses. The exploratory stage identified key terms and concepts, guiding search strings applied to Scopus and Web of Science databases. Following the PRISMA protocol, 949 documents were screened, duplicates removed and relevance assessed, resulting in a final sample of 29 studies. Descriptive analysis mapped publication trends, countries, and maintenance types, while content analysis examined Industry 4.0 technologies, applications, benefits and challenges. A summary table synthesized findings, providing a comprehensive, accessible overview of Maintenance 4.0 practices and insights into digital transformation in industrial maintenance. Findings Analysis of 29 studies highlights predictive maintenance as the dominant focus within Industry 4.0-driven industrial maintenance. Key technologies identified include IoT, AI, Machine Learning, Big Data, Digital Twins and Cyber-Physical Systems, enabling real-time monitoring, data-driven decision-making and predictive interventions. Benefits include reduced costs, minimized downtime, enhanced reliability, extended equipment lifespan, improved sustainability, and optimized resource utilization. However, adoption is constrained by high initial investment, data quality issues, integration challenges with legacy systems, personnel shortages and cybersecurity concerns. This study provides a comprehensive synthesis of Maintenance 4.0 practices, demonstrating how digital technologies transform maintenance strategies, improve operational efficiency and foster industrial competitiveness. Research limitations/implications This study advances research on Industry 4.0 and industrial maintenance by offering an integrated framework that synthesizes technologies, benefits, and implementation challenges of Maintenance 4.0. It strengthens theoretical understanding by positioning predictive maintenance as a central value-generation mechanism and by clarifying how digital technologies – such as IoT, AI, ML and big data – translate into performance outcomes. Furthermore, the study highlights the interaction between technological adoption and organizational constraints, supporting the development of more realistic, context-sensitive models. By moving beyond fragmented and technology-centric approaches, the research contributes to theory building and provides a foundation for future empirical and conceptual investigations in digital maintenance transformation. Practical implications This study provides actionable insights for organizations implementing Industry 4.0 in maintenance. It highlights the need to prioritize investments in data infrastructure and data quality as key enablers of predictive maintenance and advanced analytics. The findings emphasize adopting an integrated and strategic approach, combining technologies such as IoT, AI and cloud computing to maximize efficiency and reliability. Additionally, the study underscores the importance of workforce development, training, and fostering a data-driven culture. By identifying barriers – including high costs, integration complexity, and cybersecurity risks – it helps managers anticipate challenges and design more effective implementation strategies, supporting better decision-making and improved operational performance in maintenance environments. Social implications This study offers relevant social implications by highlighting how the adoption of Industry 4.0 technologies in maintenance can reshape workforce dynamics and organizational practices. The emphasis on workforce development and new skill requirements underscores the need for continuous learning and professional reskilling. By promoting data-driven cultures, the study supports more transparent and informed decision-making processes. Additionally, improved maintenance efficiency and reliability can enhance operational safety and reduce environmental impacts through optimized resource use and reduced downtime. However, challenges such as technological complexity and skill gaps may create inequalities, reinforcing the importance of inclusive strategies to ensure that digital transformation benefits employees and society more broadly. Originality/value This study provides a novel, comprehensive synthesis of Maintenance 4.0, addressing a critical gap by systematically mapping the adoption, applications and impacts of Industry 4.0 technologies in industrial maintenance. It establishes predictive maintenance as the central framework and underscores the transformative potential of IoT, AI, Machine Learning, Big Data, Digital Twins and Cyber-Physical Systems in optimizing operational efficiency, reducing costs and extending asset lifespan. By integrating measurable benefits with implementation challenges, the research offers actionable insights and a strategic roadmap for practitioners and policymakers, advancing theory and practice in digital maintenance. It positions Maintenance 4.0 as a key driver of competitiveness, sustainability and industrial modernization.
Marques et al. (Tue,) studied this question.