Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing: A Systematic Literature Review

Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing: A Systematic Literature Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing: A Systematic Literature Review El Mahdi BOUYAHROUZI, Racha BENMIMOUN, Yousra EL KIHEL, Mohamed Saab BAJJOU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7184682/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The integration of Industry 4.0 (I4.0) technologies with Maintenance 4.0 (M4.0) practices holds strong potential for advancing sustainable manufacturing (SM). While these technologies promise improvements in resource efficiency, waste reduction, and alignment with sustainability objectives, research on their synergistic implementation remains limited. This study addresses this gap through a Systematic Literature Review (SLR) of 75 peer-reviewed papers published between 2015 and 2024, conducted in accordance with PRISMA guidelines. The review explores how I4.0–M4.0 synergy contributes to sustainability across four interconnected dimensions: economic, environmental, social, and technological. Findings show that integrating technologies such as the Internet of Things and Artificial Intelligence into maintenance operations can reduce downtime by 20–50% and enhance efficiency and system resilience by 10–25%, particularly in industries like automotive and aerospace. Digital twin technologies extend equipment lifespan by 10–25%, thereby deferring capital expenditures. Furthermore, blockchain and augmented reality improve operational transparency by 30–40%, while big data analytics and cyber-physical systems contribute to energy savings of 12–18% and reduce material waste by 20–25% through real-time quality monitoring. Despite these benefits, several challenges hinder integration, including technical barriers (e.g., legacy systems, cybersecurity risks), organizational resistance (e.g., high costs, cultural inertia), and human-related issues (e.g., skills shortages, workforce restructuring). To address these barriers, the paper proposes a holistic architecture that aligns I4.0–M4.0 integration with sustainability goals, bridging technological innovation with responsible resource management. This framework offers actionable insights for stakeholders, policymakers, and industry leaders aiming to foster resilient, efficient, and socially responsible manufacturing ecosystems. Industrial Engineering Industry 4.0 I4.0 Technologies Maintenance 4.0 Sustainable Manufacturing Systematic Literature Review Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Contrary to the principles of a linear economy, which mainly relies on intense resource extraction and consumption, sustainable manufacturing concerns have arisen as a primary focus of global objective [ 1 ]. With rising issues such as depletion of natural resources, ecosystem degradation and depletion of non-renewable resources like rare earth metals, and ecosystem degradation from industrial waste, it has become important to reinvent production and consumption patterns by embracing sustainable and regenerative techniques [ 2 ], [ 3 ]. It is within this environment that novel techniques, such as Industry 4.0 Technologies (I4.0), are emerging as possible solutions [ 4 ],[ 5 ]. On the one hand, the growing adoption of I4.0 is profoundly transforming business environments [ 6 ]. The cumulative effect of these disruptions is profoundly transforming the global manufacturing [ 7 ]. In this context, the sustainability of these changes-in connection with social, economic, and environmental factors-is of paramount importance. On the other hand, optimized maintenance management plays a key role in enhancing companies' adaptability to these disruptions [ 8 ]. They aim not only to reduce the impact of human activities on the environment, but also to ensure economic development in harmony with the preservation of natural resources [ 9 ]. The Industrial Revolution has radically transformed production activities, as shown in Fig. 1 . It began with steam-powered mechanization (Industry 1.0), progressed to the use of electricity for mass production (Industry 2.0), and moved toward automated production processes (Industry 3.0). More recently, Industry 4.0 has emerged, characterized by I4.0 technologies such as the Internet of Things, cyber-physical systems, cloud computing, and big data [ 10 ], [ 11 ]. This new paradigm enables the analysis of data extracted from sensors characterized by high velocity, variability, veracity, volume, and value. Through in-depth analysis, they generate production forecasts, optimize maintenance management, provide insights into equipment, and create resilient, sustainable, and digital manufacturing processes [ 12 ]. In this context, both the sustainability of changes and the optimized maintenance management becomes crucial to ensure organizational adaptation to such disturbances [ 13 ],[ 14 ]. By integrating sustainable practices and innovative technologies, companies may not only prevent disruptions in production processes but also save costs, prolong equipment lifespan, and lessen their ecological imprint. These efforts eventually boost their long-term competitiveness. According to AFNOR FD X 60 − 000 (2016), maintenance serves as a cornerstone of sustainability. By enhancing equipment availability and extending its lifespan, maintenance reduces the need for raw materials, energy, and resources required to manufacture new goods, while simultaneously limiting the waste generated by their disposal [ 3 ]. Furthermore, maintenance stimulates local job creation, driving its positive impact across economic, environmental, social, and technological dimensions are all affected. However, the transition to sustainable manufacturing also poses challenges; for example, if not properly managed, it can disrupt production workflows, degrade product quality, and undermine a company's competitive advantage [ 15 ]. Additionally, risks such as security threats, integration issues with existing systems, and increased complexity in operations must be addressed [ 16 ]. In response to these challenges, the adoption of technological and technical solutions supporting sustainable development has gained momentum in recent years [ 17 ], [ 18 ]. Advanced tools such as predictive maintenance systems, IoT-enabled sensors, and AI-driven analytics enable companies to anticipate failures, optimize resource utilization, and foster resilient production processes. These innovations not only minimize operational disruptions but also strengthen long-term sustainability by reducing waste, lowering costs, and supporting a Circular Economy model [ 19 ]. Previous research has yet to provide comprehensive, systematic, and quantitative answers regarding the integration of I4.0 technologies and M4.0 practices to achieve SM, limiting a deeper understanding of the enabling mechanisms and potential synergies. While several studies have explored the intricate relationships between I4.0 and SM, particularly the impacts of I4.0 technologies on economic, environmental, social, and technological dimensions [ 20 ], [ 21 ], qualitative approaches have largely dominated the field. These studies offer valuable insights into interpretations and conceptual frameworks but fall short in providing structured, scientific, and quantitative analyses of how specific I4.0 technologies directly influence SM practices [ 22 ], [ 23 ]. Additionally, the mechanisms for integrating I4.0 technologies—such as IoT, artificial intelligence, and additive manufacturing—into SM processes remain insufficiently understood. The lack of quantitative data hinders the development of systematic models that illustrate the concrete contributions of these technologies to sustainability initiatives [ 7 ]. The I4.0 and SM can be considered complementary paradigms with a shared vision of enhancing the sustainability and efficiency of operational systems [ 24 ]. Industry 4.0, by leveraging advanced technologies such as IoT, AI, and data analytics, enables optimized resource management, waste minimization, and the reduction of inefficiencies in industrial workflows. Concurrently, sustainable manufacturing—the practical realization of SD within the manufacturing domain—advocates for circular economy principles by promoting material reuse, extending product life cycles, and reducing waste generation [ 25 ]. Together, these two approaches (I4.0 et M4.0) pave the way for a transformative shift toward sustainable industrial practices. They maximize resource efficiency and resilience while mitigating environmental impacts. Table 1 summarizes prior studies that investigated the intersection between I4.0 and Maintenance, especially in the context of Corporate Social Responsibility (CSR) and Sustainable Manufacturing (SM). Table 1 Past Literature Reviews. Author Title Paper reviewed Findings Murtaza and al. (2024) [ 26 ] “Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study” 100 The transition from Industry 4.0 to Industry 5.0, in the context of predictive maintenance and condition monitoring, aims to foster Sustainable Manufacturing Mallioris and al. (2024) [ 27 ] “Predictive maintenance in Industry 4.0: A systematic multi-sector mapping” 78 The paper provides a systematic mapping of predictive maintenance (PdM) within the framework of Industry 4.0 across multiple sectors while highlighting the benefits of predictive maintenance in improving operational efficiency, reducing downtime, and supporting sustainable manufacturing processes. Stana and al. (2024) [ 28 ] “Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review” 20 The paper identifies and analyzes the key criteria that facilitate the adoption of sustainable maintenance practices through an umbrella review approach. It consolidates findings from multiple studies to highlight critical factors such as environmental impact, cost efficiency, technological integration, and policy frameworks that enable sustainable maintenance. Psarommatis and al. (2023) [ 29 ] “Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework” 344 The paper envisions Maintenance 5.0 as a human-centered, sustainable evolution of maintenance systems beyond Industry 4.0. It emphasizes the need for collaboration between advanced technologies and human intelligence to create more resilient, efficient, and environmentally responsible maintenance strategies. Hallioui and al. (2023) [ 30 ] “A Review of Sustainable Total Productive Maintenance (STPM)” 94 The paper explores the concept of Sustainable Total Productive Maintenance (STPM), which integrates sustainability principles into traditional Total Productive Maintenance (TPM) practices. It highlights How STPM contributes to improving equipment efficiency, reducing resource consumption, and minimizing environmental impacts while maintaining operational performance. Wang and al. (2020) [ 31 ] “Smart remanufacturing and maintenance for machinery in Industry 4.0” 154 The paper explores the integration of smart remanufacturing and maintenance strategies for machinery within the framework of Industry 4.0 to achieve maximum efficiency, reliability and sustainability of machine operations in manufacturing Our study “Paving the Way to Sustainability: Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing Goals” 75 The study presents an SLR study to discuss the connections between I4.0 Technologies and M4.0 with Sustainable Manufacturing A thorough analysis of the current application of I4.0-M4.0 in the context of SM is essential to build a solid foundation for deepening and broadening the integration of these technologies. Maintenance, by harnessing advanced tools such as IoT, artificial intelligence and data analytics, plays a central role in implementing sustainable manufacturing principles. By optimizing equipment management, it guarantees the efficiency, reliability and sustainability of industrial systems, thus contributing directly to SM objectives. Better mastery of current I4.0 technologies and M4.0 approaches can not only reinforce these benefits but also unlock new opportunities to extend the application of sustainable manufacturing. To achieve these results, it is essential to answer the following key research questions: ● RQ1 : How can the integration of Industry 4.0 and Maintenance 4.0 be leveraged to enhance both sustainability and competitiveness, and what are the key managerial and technological levers involved? ● RQ2 : What are the main barriers to the integration of I4.0 and M4.0, and what roadmap can organizations follow to implement these solutions effectively? Answering these questions requires interdisciplinary research combining technological, organizational, and strategic perspectives. A conceptual framework could also be developed to guide industries in leveraging I4.0-M4.0 effectively. Additionally, showcasing successful implementations and case studies can provide actionable insights for companies aiming to align their practices with sustainability goals. By systematically synthesizing the dispersed body of knowledge, this article makes three main contributions. First, it maps the technology–maintenance–sustainability nexus and highlights twelve integration mechanisms that recur across empirical studies (e.g., condition‑based energy management, digital twin‑aided scheduling). Second, it proposes a multi‑layered conceptual framework that aligns asset‑level predictive maintenance data with plant‑level sustainability dashboards and corporate sustainability strategy. Finally, it outlines a diagnose–design–deploy roadmap that practitioners can adapt to accelerate their transition towards sustainable, resilient, and digitally empowered factories. The remainder of the paper is organized as follows. Section 2 describes the SLR protocol, including database selection, inclusion/exclusion criteria, and the PRISMA flow diagram. Section 3 presents descriptive statistics of the selected literature. Section 4 offers a critical analysis of seminal studies and synthesizes integration mechanisms and sustainability outcomes. Section 5 discusses theoretical and managerial implications, while Section 6 introduces the implementation roadmap. Finally, Section 7 concludes and suggests avenues for future research. 2. Research methodology This Systematic Literature Review (SLR) aims to investigate how the combined implementation of Industry 4.0 (I4.0) technologies and Maintenance 4.0 (M4.0) practices contributes to advancing the economic, environmental, social, and technological pillars of sustainable manufacturing. The review was conducted following a preregistered protocol and the PRISMA 2020 reporting framework. We predefined the objectives, search strategy, and inclusion/exclusion criteria to ensure transparency and reproducibility [ 32 ]. Our study employs a mixed-methods approach, integrating qualitative thematic analysis with quantitative profiling to provide decision-oriented insights. 2.1. Search Strategy Phase 1: Selection of studies In line with the research questions, the papers in this review investigate the contributions of integrating I4.0 technologies and M4.0 practices in promoting sustainable manufacturing and enhancing competitiveness. For the purposes of this review, only peer-reviewed articles written in English were considered. Given that each topic examined in this study is novel, it is important to specify that a strict temporal limit was established. Thus, only articles published between 2015 and 2024, inclusive, were retained. This temporal delimitation aims to ensure the relevance and timeliness of the data examined, while providing an overview of recent advances in the field. Phase 2: Paper Collection To build up an exhaustive corpus of scientific articles, a structured search strategy was developed. On the one hand, keywords belonging to the same thematic group "Intra-group", as well as intra-group combinations, were associated using the Boolean operator “OR”, in order to broaden the scope of the search and capture all relevant terminological variations. On the other hand, the main groups of keywords were defined to cover three major axes: technologies and concepts related to M4.0-I4.0 and the four dimensions of SM, namely environmental, economic, social and technological. These thematic groups "Inter-group" were then crossed using the Boolean operator “AND” to focus the search on relevant intersections between these domains. The two group terms are: Group A: (“Industry 4.0”, “I4.0”, ”Digitalization”, ”Intelligent Manufacturing“, ”Digital Factory“, ”Smart Factory“, “Fourth Industrial Revolution”, “Smart Manufacturing”, “Industrial Automation”, “Integrated Systems”, “I4.0 Technologies”, “Cloud Manufacturing”, “Internet of Things”, “Artificial Intelligence”, “Big Data”, “Blockchain”, “Cloud Computing”, and “Cyber-Physical System”, “Augmented Reality”, “Virtual Reality”, “Digital Twin”, “Additive Manufacturing”, “Edge Computing”). Group B: (“Predictive Maintenance”, “PdM”, “Maintenance 4.0”, “M4.0”, “remaining useful life”, “Condition-Based Maintenance”, “Asset Management”, “Reliability-Centered Maintenance”, “Digital Maintenance”, “Maintenance Digital”). Group C: (“Sustainable Manufacturing”, “Sustainable Development”, “Environmental Performance”, “Economic Performance”, “Social Performance”, “Technological Performance”, “Sustainable”, “Sustainability”, “Industrial Practice”, ”Durability”, “Environmental”, “Economic”, “Social”, “Technological”, “Social Sustainability”, “Green Manufacturing”, “Resource Efficiency” ). Phase 3: Paper Select The articles collected from the SCOPUS, WOS, and IEEE databases include the most peer-reviewed publications. Additionally, the three databases contain most of the main publishers, such as Springer, Taylor & Francis, Wiley, Elsevier, ScienceDirect, and Emerald Insights. Phase 4: Paper Processing In the first stage, duplicates and non-journal items—such as conference papers, book chapters and articles from distant fields like materials science, chemistry or pure mathematics—were removed, leaving 390 peer-reviewed journal articles. The second stage involved a keyword screen: papers whose titles or keywords revolved around peripheral topics (for instance “battery”, “PV” or general “energy” issues) were excluded because they did not address our core research questions. This reduced the pool to 318 articles. During the third stage, all remaining records underwent title-and-abstract scrutiny followed by full-text reading when necessary, using an explicit two-step rubric. Articles were rejected if the full text was unavailable, if they were non-academic or misused the term sustainability, or if they mentioned I4.0–M4.0 concepts only in passing. Conversely, studies that touched on at least one of the domains (Industry 4.0, maintenance or sustainability) without fully linking them were kept for contextual background, while papers that explicitly integrated the three themes formed the core evidence base. The final stage consisted of an independent, detailed appraisal by all authors; disagreements were resolved through discussion. Here, 46 additional texts were discarded for inadequate methodological detail or weak thematic alignment. Ultimately, 75 studies—comprising 26 review articles and 49 empirical or conceptual investigations—met every criterion and now underpin our analysis of how I4.0 technologies and M4.0 practices advance sustainable manufacturing. Figure 2 illustrates the process of selecting studies from various sources. Initially, studies were identified through additional queries in the IEEE (82), Scopus (43), and Web of Science (28) databases. This was followed by the extraction of 963 additional studies from the databases (IEEE: 529, Scopus: 169, Web of Science: 265), resulting in a total of 1,116 studies. After applying the exclusion criteria described above, 26 studies were retained for the review, along with 49 relevant study reports, yielding a final total of 75 included items. 2.2. Descriptive Analysis The final portfolio of selected studies undergoes a thorough examination, incorporating both quantitative and qualitative analyses. Drawing notably on previous research, as referenced by [ 33 ], [ 34 ], we approach the data from several key dimensions to highlight specific trends and distributions. Our study therefore considers four main categories of distributions: Distribution of publications over time : Identifying the progression and growth of research in the context of I4.0, maintenance, and their impact on sustainable manufacturing. Distribution of publications by journals : Highlighting the concentration of relevant research in specific journals to identify the main publication channels. Distribution of publications by technologies deployed : Categorizing studies based on the specific I4.0 technologies and tools implemented. This analysis reveals which technologies dominate the research landscape, their application within Maintenance 4.0 frameworks, and their contributions to sustainable manufacturing practices. Distribution of publications by research focus : Categorizing studies based on their objectives, such as exploratory research, theoretical development, or empirical testing. Distribution of publications by methodology : Examining the methodological approaches used in the portfolio, including conceptual papers, literature reviews, empirical studies, secondary data analyses, expert interviews, surveys, and experiments. Distribution of keywords through Co‑occurrence analysis : illustrating the relationships between the keywords or research topics concepts through connections reflect their co-occurrence in scholarly literature. The objective of this analysis is to identify the main themes and research areas. This step provides an overview of the relationship between I4.0 technologies and their impact on sustainability within the framework of M4.0. Within this context, I4.0 technologies, M4.0, and sustainability principles are identified, selected, and categorized. The main objectives achieved through the combination of I4.0 and M4.0 processes to achieve sustainable manufacturing are then determined based on the content of the portfolio. Through this analysis, a comprehensive understanding of the research landscape is obtained, allowing us to identify existing gaps in the field. 3. Descriptive Analysis Following the thorough documentary analysis, 75 studies were selected. These sources form the foundation of our analysis, which aims to explore the interactions between I4.0 and M4.0 technologies, as well as their impact on sustainable practices. Upon completing the content analysis, a diagram was developed to illustrate the links between specific I4.0 technologies and M4.0, and their role in achieving sustainable manufacturing. 3.1. Distribution of publication over time Figure 3 illustrates the annual distribution of the selected publications from 2015 to 2024. A notable increase in publications emerged starting in 2020, reflecting heightened awareness and growing adoption of I4.0 principles and advanced maintenance strategies in the manufacturing sector. The number of studies rose from five publications in 2020 to a peak of 14 in 2021 and 2022, suggesting that these years were particularly productive for developing foundational knowledge, theoretical frameworks, and practical solutions. In 2024, there was a substantial surge, reaching 27 publications. This marked growth can be attributed to several factors: increasing industrial interest in these topics, stronger political support for sustainable manufacturing, and the growing maturity of enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Cyber-Physical Systems (CPS). Overall, this upward trend highlights the increasing importance for the industrial sector and the manufacturing ecosystem to adopt sustainable practices supported by advanced digital technologies. The expanding body of literature serves as a clear indicator of the interest, dynamism, and evolving nature of this field. 3.2. Distribution of publication by methodology Figure 4 provides a more granular view by categorizing the selected publications into four types: conceptual Studies, systematic reviews and critical analyses, quantitative or qualitative analyses, and empirical and case study. The evaluations indicate the diversity of research approaches employed to address the themes of I4.0 and M4.0 within the context of sustainable manufacturing. The results reveal a predominance of empirical and case studies, followed by systematic reviews, conceptual studies, and finally quantitative or qualitative analyses. Empirical and case studies have garnered the most attention, representing a significant portion of the selected publications. These studies aim to: i) Validate proposed solutions, frameworks, or models through real-world applications; ii) Provide tangible results via case studies, experiments, and prototypes; iii) Demonstrate the effectiveness of I4.0 technologies and M4.0 in improving maintenance strategies, optimizing manufacturing processes, and achieving sustainability goals. In parallel, systematic reviews and critical analyses represent a substantial proportion of the literature. These papers synthesize and evaluate the existing body of knowledge, serving three primary purposes: i) Providing a comprehensive overview of the current state of research; ii) Identifying trends, challenges, and research gaps; iii) Comparing and analyzing existing methods, models, or applications. Conceptual studies contribute by proposing theoretical frameworks or new models that lay the foundation for future research. These studies explore innovative solutions and offer strategic directions for integrating advanced technologies into maintenance practices. Finally, quantitative or qualitative analyses focus on applying analytical approaches, such as machine learning models, simulations, or optimization techniques, to address specific challenges related to predictive maintenance and sustainability. 3.3. Distribution of publication by journal As illustrated in Fig. 5 , the distribution of sources for the 75 selected articles is spread across various journals, with a total of 51 different journals. The analysis reveals that the largest proportion, approximately 17%, consists of articles published in the "Sustainability" journal by MDPI. The "Applied Sciences" journal follows with 7%, highlighting its relevance to interdisciplinary and applied studies. The journal "IEEE Access" contributes 5%, emphasizing the technological aspects of the research. The "Journal of Cleaner Production" accounts for 4%, and so forth. Approximately 58% of the articles are published in various other journals, each represented by a single article. This diversification of publications highlights both the interdisciplinary nature of the research field and the significance of specialized journals in shaping discussions on I4.0 technologies, Maintenance 4.0, and three axes of sustainability. It further demonstrates the broad dissemination of research across numerous platforms, facilitated by the innovative solutions brought about by this transformation. 3.4. Distribution of publication by technologies deployed The bar chart in Fig. 6 illustrates the frequency of different technologies referenced in the reviewed publications between 2015 and 2024. The analysis reveals significant variations in the focus on various M4.0 and I4.0 Technologies. The industrial sector's growing interest in Industry 4.0 technologies is reflected in the different technologies used over time. Ten essential technologies have arisen, significantly influencing the current Industry 4.0 scene and fostering improvements in sustainability. 3.5. Distribution of keywords through Co‑occurrence analysis The network map, as illustrated in Fig. 7 , offers a visual depiction of the interconnections between keywords or key research topics. The keywords that are represented by larger font sizes are indicated as the most frequently used in existing publications. The network map reveals three distinct clusters, designated by red, green, and blue, respectively, that are associated with Industry 4.0, Maintenance 4.0, and Sustainable Manufacturing. At the core of the network, "Industry 4.0", "Predictive Maintenance", and "Sustainability" appear as dominant nodes, suggesting their central role in the research landscape. Surrounding these key concepts are closely related terms such as "Artificial Intelligence", "Internet of Things" (IoT), "Big Data", and "Cyber-Physical Systems", which serve as critical enablers of sustainable manufacturing in industrial environments. The prominence of AI-related terms like "Machine Learning", "Deep Learning", and "Neural Networks" further indicates that intelligent algorithms are at the heart of automation, predictive analytics, and decision-making processes in modern manufacturing. The map also reveals a strong link between Industry 4.0 and Maintenance 4.0. Technologies such as Digital Twins, Edge Computing, and IoT facilitate real-time monitoring, allowing industries to transition from reactive to predictive maintenance strategies. Concepts like "Remaining Useful Life" (RUL), "Condition-Based Maintenance", and "Asset Management" are deeply interconnected, demonstrating a clear trend toward optimizing equipment performance and reducing downtime through smart technologies. Another significant observation is the integration of sustainability principles into industrial practices. Terms such as "Energy Efficiency", "Resource Efficiency", "Circular Economy", and "Climate Change" are connected to both Industry 4.0 and Maintenance 4.0, highlighting that these technological advances are not only about automation and operational efficiency but also aim to achieve environmental and economic sustainability. Additionally, the presence of terms like "Green Manufacturing" and "Social Sustainability" reflects a growing awareness of the importance of responsible and eco-friendly production methods. The analysis of the literature reveals an evolution and maturation of research at the intersection of I4.0, M4.0, and SM. The continually expanding body of work now includes both cutting-edge empirical studies and critical analyses, contributing to a dynamic and thoughtfully engaged research ecosystem. This analytical framework provides valuable insights for practitioners, policymakers, and researchers, enabling a comprehensive understanding of the scientific landscape and helping to identify current gaps in the literature on this subject. 4. Content analysis Building on the descriptive statistics presented in Section 3, this section synthesizes the findings from the final portfolio of seventy-five studies through a combined quantitative–qualitative lens. Following the methodological guidance of [ 35 ], [ 36 ], we examined each paper across several dimensions—publication year, research method, focal technology, maintenance function and sustainability pillar—to surface dominant patterns and emerging gaps. The analysis confirms that the joint deployment of Maintenance 4.0 (M4.0) and Industry 4.0 (I4.0) technologies strengthens manufacturing capability, sharpens competitive advantage and supports all four sustainability pillars (economic, environmental, social and technological). To present these insights coherently, the discussion is organized around two interrelated themes: (i) Integration mechanisms between M4.0 and I4.0 technologies: Here we map the technical and organizational linkages—such as IIoT-enabled data pipelines, digital-twin-driven diagnostics and AI-supported decision loops—that embed maintenance intelligence within broader smart-factory architectures. (ii) Contribution of M4.0–I4.0 integration to sustainable manufacturing: This theme explains how the identified mechanisms translate into tangible sustainability outcomes: resource-efficiency gains, waste and emission reductions, extended asset lifecycles and enhanced workforce well-being. 4.1. Integration Mechanisms between M4.0 and I4.0 Technologies The transition from conventional maintenance paradigms to Maintenance 4.0, aligned with Industry 4.0 standards, necessitates a fundamental transformation driven by the integration of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems (CPS) [ 37 ]. This evolution requires a systematic evaluation of each technology’s contribution to core M4.0 processes. To build comprehensive analytical depth, we conducted a content analysis of 75 academic publications, examining the specific usefulness and implementation modalities of these technologies in maintenance environments. The aim is to optimize maintenance operations, enhance decision-making capabilities, and maximize operational efficiency, while strategically leveraging technological advances to address current and future competitiveness challenges [ 38 ]. Despite significant progress, a gap remains between theoretical promises and practical industrial applications, primarily due to technical challenges (e.g., interoperability) and organizational barriers (e.g., resistance to change). In the literature, several studies [ 39 ], [ 40 ], [ 41 ] agree that technological progress is centered around nine fundamental pillars that have the potential to transform production systems, whether adopted individually or in combination. For instance, in the study [ 42 ], the author investigated workforce competency transformation in Industry 4.0 environments through a modified Delphi method involving 38 industry experts across twelve manufacturing sectors. The research identified nine core competency domains, with predictive analytics literacy showing the strongest correlation with maintenance efficiency (β = 0.72, p < 0.01). The study concluded that augmented reality-based training reduced skill acquisition time by 40% compared to traditional methods, and proposed a hierarchical upskilling framework. Another study [ 43 ], mapped technology adoption trajectories in SMEs using a sequential mixed-methods approach (n = 127 survey responses + 12 case studies). The study quantified the maturity progression of Industry 4.0 technological pillars, finding that asynchronous adoption creates “integration debt”—for example, early IoT adopters experienced 18% lower ROI than synchronized adopters (F = 6.34, p = 0.013). The authors emphasized that phased implementation must prioritize interoperable architectures to avoid technical fragmentation. In [ 44 ], the author conducted a five-year longitudinal action research study across three Nordic manufacturers to investigate the coevolution of technology and maintenance practices. The study revealed self-reinforcing loops between IIoT deployment and predictive maintenance maturity, leading to a 32 ± 7% reduction in Mean Time to Repair (MTTR). It further demonstrated that data liquidity accelerates M4.0 adoption 2.4 times faster than isolated, technology-centric initiatives. In the paper [ 45 ], the authors explored IIoT–CPS interoperability through controlled experiments using industrial controllers under varying network conditions. By quantifying latency thresholds for predictive maintenance, they showed that 5G-enabled edge computing reduces diagnostic latency to under 8 ms—compared to 142 ms with 4G—achieving 99.2% fault detection accuracy. The study concluded that maintaining sub-10 ms latency is essential for high-urgency maintenance scenarios. In this context, the nine core technological pillars—Industrial Internet of Things (IIoT), Big Data and analytics, horizontal and vertical system integration, 3D simulation, cloud computing, augmented/virtual reality (AR/VR), autonomous robots, additive manufacturing, and cybersecurity—function as interdependent components within a dynamic capability framework [ 40 ], [ 41 ], [ 46 ]. These pillars constitute the foundation of “smart factories”, characterized by advanced automation, holistic systems integration, and optimized production processes. Furthermore, they enhance efficiency, foster innovative collaboration among suppliers, manufacturers, and customers, and strengthen the human–machine interface [ 47 ]. This analysis focuses on the full spectrum of Industry 4.0 technologies, drawing from an extensive body of literature to provide a holistic, comparative perspective on their impact. The objective is to identify synergies between technologies and assess their contributions to both industrial performance and organizational transformation. To establish a clear connection between I4.0 technologies and M4.0 processes, Table 2 presents a synthesis of the synergies across these two domains. It highlights the contributions of various Industry 4.0 tools—including IoT, AR/VR, BDA, cloud computing, AI, RFID, M2M, 3D printing, cybersecurity, Power BI, digital twins, cobots, 5G/6G, blockchain, and CPS—to the primary components of M4.0 (data acquisition, monitoring, diagnosis, prognosis, decision making, maintenance planning, process automation, and self-healing). In doing so, it underscores their collective role in optimizing maintenance strategies and enhancing industrial performance. This analysis reveals critical interactions among these technologies, with most scientific articles emphasizing the combined use of AI, IoT, and Big Data across the majority of Maintenance 4.0 dimensions. Data Acquisition and Monitoring —the initial pillars of Maintenance 4.0—are widely viewed as foundational to this innovative maintenance approach, relying on interconnected sensors to continuously collect, compile, and analyze key equipment parameters (e.g., temperature, vibration, pressure), thereby reflecting each machine’s real-time condition. In this context, the literature points to the Industrial Internet of Things (IIoT) concept, which adapts IoT principles for the industrial domain, thus enabling machine-to-machine (M2M) communication without human intervention [ 48 ]. This technology interlinks physical devices through sensors, RFID, and dedicated Internet protocols, creating an interconnected ecosystem that spans the entire production environment. According to author [ 49 ], IIoT is regarded as a core technology underlying CPS, which integrates the physical (real-world) and digital (cyberspace) realms through advanced computing, communication, and control functions. As noted by [ 50 ], CPSs enable seamless convergence between physical and virtual spheres, dissolving the boundaries between these two environments. Such systems are characterized by the close intertwinement of natural and artificial processes with digital technologies. Moreover, technologies such as blockchain and cybersecurity facilitate automatic and secure data transmission—originating from RFID and M2M—to central control systems. The consolidation and safeguarding of data flows constitute essential prerequisites for more advanced analyses. Subsequently, Big Data techniques become indispensable for aggregating and exploiting the massive volume of collected information. Several studies [ 51 ], [ 52 ] have demonstrated that AI plays a pivotal role in monitoring, early fault detection, and establishing alert thresholds and standards [ 53 ], [ 54 ]. By analyzing high-dimensional data at scale and identifying non-linear correlations undetectable by humans [ 55 ], AI systems improve failure prediction accuracy by 20–50%, reduce maintenance costs by 18–25%, and strengthen operational resilience. These outcomes underscore AI’s transformative capacity for preventing and predicting malfunctions in Maintenance 4.0. Furthermore, the integration of IoT, Big Data, and AI paves the way for maintenance that is both proactive and reactive, aligning with contemporary demands for reliability and cost-effectiveness. In parallel, leveraging digital twins, AR/VR, and cloud computing enables a comprehensive, immersive, and interactive perspective on equipment status—even when accessed remotely [ 56 ]. Finally, cybersecurity remains fundamental to preserving the reliability and integrity of data flows, thereby completing the broader M4.0 ecosystem. At the same time, advanced visualization tools, such as Power BI, synthesize and display information in real time, providing a solid foundation for in-depth analyses. The Diagnosis and Prognosis phases of Maintenance 4.0 rely on a coherent integration of advanced technologies—particularly Big Data and AI—which serve as key drivers in the evolution of algorithms [ 57 ]. It is noteworthy that most diagnostic and prognostic solutions use data-driven models and algorithms, as they can handle terabytes of data (Big Data) from thousands of IoT devices, detecting deviations with over 90% accuracy [ 58 ], [ 59 ], this performance surpasses traditional physics-based, expert-based, or rule-based diagnostic models [ 60 ], [ 61 ]. Diagnostic processes leverage the ability to reliably analyze and interpret data collected in the previous stage from a multitude of connected sensors and RFID systems, while machine learning or deep learning algorithms identify weak signals indicative of anomalies or potential failures. Furthermore, immersive technologies such as augmented reality (AR) and virtual reality (VR)—applied in approximately 20% of cases—facilitate more detailed and interactive inspections, thereby improving the understanding of complex anomalies [ 62 ]. Prognosis, in turn, relies on advanced algorithms, chiefly deep learning, to anticipate failures and estimate each asset’s remaining useful life (RUL) by considering various factors (operating intensity, service conditions, maintenance history, etc.). In this context, interoperability supported by M2M and IoT, which constitute roughly 60–70% of the architecture in connected industrial environments, enables continuous updates of predictive models [ 62 ]. Combined with Big Data—employed in about 80% of use cases—this interoperability further strengthens model robustness by consolidating extensive contextual data from heterogeneous sensors and external sources (failure histories, production conditions, spare-part inventories) [ 63 ]. This large-scale analytical capacity significantly improves predictive accuracy and facilitates proactive decision-making, reducing costs associated with unplanned production stoppages. Decision-Making and Maintenance Planning , the subsequent Maintenance 4.0 stages, benefit from unprecedented automation and optimization through strategic integration of solutions such as IoT/IIoT, AI, digital twins, and even 5G/6G. Deployed to varying degrees across different sectors, these technologies generate tangible gains in productivity, cost reduction, and operational resilience [ 64 ], [ 65 ]. The adoption of Power BI—utilized by 70–80% of industrial firms—plays a pivotal role in centralizing performance indicators (KPIs) and generating interactive dashboards [ 66 ]. These tools enable intuitive data visualization from IoT sensors, implemented in 65% of connected factories [ 67 ], or cloud databases, cutting analysis time by 30–40% [ 68 ]. Multicriteria algorithms, coupled with predictive AI, automate intervention prioritization by accounting for multiple factors, including asset criticality (assessed via digital twin models), intervention costs, and availability of both human and material resources. This integrated approach provides rapid insights for effective decision-making and optimal intervention planning. Concurrently, blockchain technology—adopted by 10–15% of firms for decision-making —reinforces transparency by securing maintenance history and corporate data, thus reducing human error by 20% [ 69 ], [ 70 ]. Intervention planning also depends on close coordination among human operators and autonomous systems, enabling optimized lead times and costs. Cobots (collaborative robots) and 5G/6G networks (with latency as low as 1 ms) enable instant communication among technicians, engineers, and suppliers, accelerating problem resolution by 40% [ 71 ], [ 72 ]. AR/VR usage—ranging from 10–20% in certain high-risk sectors—allows the simulation of complex maintenance tasks before execution, significantly mitigating unforeseen issues and achieving a 99% accuracy rate while reducing error risk. AI, RFID, IoT, and M2M automate task prioritization and resource allocation, thereby streamlining process optimization [ 73 ]. For example, RFID simplifies spare-part inventory management by enabling real-time tracking, ensuring the availability of necessary components. Lastly, while 3D printing remains a niche technology (not yet widely adopted), it supports local on-demand fabrication of critical parts, shortening lead times by 50–70% in urgent cases [ 74 ]. Taken together, these technologies automate and optimize decision-making, rendering maintenance planning and management more streamlined, faster, and more precise. Finally, Process Automation and Self-Healing technologies are fundamentally transforming industrial operations through three measurable shifts: (i) Autonomous responsiveness to production variables (ii) Predictive adaptability to system anomalies (iii) Closed-loop optimization of resource utilization [ 75 ]. Empirical evidence demonstrates that IoT/IIoT systems drive approximately 30% of efficiency gains by enabling real-time equipment monitoring and machine-to-machine coordination, facilitating instantaneous adjustments to throughput requirements [ 76 ]. Complementary analysis reveals that Big Data analytics combined with RFID technologies contribute an additional 25% of process optimization through granular data collection and pattern recognition, enhancing automated decision-making [ 77 ]. Cloud computing provides the architectural backbone for these capabilities, enabling centralized management and distributed execution of maintenance protocols across geographically dispersed facilities [ 78 ]. Moreover, Artificial Intelligence and cobots have been cited in recent literature as being responsible for more than 60% of the qualitative and quantitative improvements in intelligent automation systems, so they represent the most significant technologies in terms of transformation [ 79 ]. These technologies allow machines to learn from data, adapt autonomously to changing conditions, and collaborate safely and efficiently with human operators in hybrid work environments. These integrated systems yield quantifiable improvements in manufacturing resilience: 41–58% reduction in unplanned downtime [ 80 ] 22–37% improvement in operational agility [ 81 ] 31 ± 5% increase in resource utilization efficiency [ 82 ]. Taken together, these technologies are not only improving operational efficiency, but are also driving a transformation marked by greater agility, scalability, and self-adaptability, thereby laying the foundation for resilient and autonomous production systems [ 28 ], [ 83 ]. Ultimately, the synergy across these technological building blocks not only optimizes maintenance from an operational standpoint but also reimagines industrial asset lifecycle management in a more predictive and proactive manner. As illustrated in Table 2 , Maintenance 4.0—through the strategic orchestration of data acquisition, diagnosis, automation, and planning—creates a virtuous cycle wherein data quality and intervention responsiveness mutually reinforce one another. This evolution positions industrial maintenance as a true strategic pillar for competitiveness and profitability, fully aligned with the demands of the future industrial landscape. Table 2 Contribution of the I4.0 technologies in Maintenance 4.0 process (Sources: literature analysis) I4.0 Technologies Data Acquisition Monitoring Diagnosis Prognosis Decision making Maintenance planning Process automation Self-Healing Ref IoT/IIoT 4 4 2 2 3 3 3 0 [ 84 ], [ 85 ], [ 86 ], [ 87 ], [ 88 ], [ 89 ], [ 90 ], [ 91 ] AR/VR 2 3 3 0 2 3 0 0 [ 92 ], [ 93 ], [ 94 ] Big Data 4 4 4 4 4 4 4 0 [ 89 ], [ 90 ], [ 93 ], [ 95 ], [ 96 ], [ 97 ], [ 98 ] Cloud computing 0 3 2 2 3 3 3 0 [ 86 ], [ 90 ], [ 95 ], [ 99 ] AI 2 4 4 4 4 4 4 4 [ 85 ], [ 88 ], [ 89 ], [ 90 ], [ 93 ], [ 96 ], [ 100 ], [ 101 ], [ 102 ], [ 103 ] RFID 4 4 0 0 2 2 2 0 [ 104 ], [ 105 ] M2M 4 0 0 0 4 4 4 4 [ 106 ], [ 107 ] 3d printing 0 0 0 0 0 2 2 0 [ 86 ], [ 108 ], [ 109 ] cybersecurity 4 4 4 4 4 4 4 4 [ 91 ], [ 110 ], [ 111 ] Power BI 0 2 0 0 4 4 0 0 [ 98 ], [ 101 ], [ 112 ] Digital Twin 4 4 4 4 4 4 4 4 [ 84 ], [ 85 ], [ 87 ], [ 113 ], [ 114 ] Cobot 2 0 0 0 3 2 4 4 [ 93 ], [ 115 ], [ 116 ] 5G/6G 4 4 4 4 4 4 4 4 [ 103 ], [ 117 ] Blockchain 4 4 0 0 4 4 0 0 [ 118 ], [ 119 ], [ 120 ] CPS 4 4 0 0 0 4 4 4 [ 50 ], [ 121 ], [ 122 ] 0 = Absent · 1 = Emerging · 2 = Moderate · 3 = High · 4 = Leading / Best-in-class 4.2. Role of M4.0 and I4.0 Technologies in Sustainable Manufacturing Technological advancements have reshaped traditional maintenance paradigms by introducing innovative tools and methods capable of meeting end users’ growing demands while respecting environmental, social, economic, and technological constraints in manufacturing. From a maintenance perspective, this transformational shift significantly affects several key aspects. The author underscores the importance of Maintenance 4.0 as a lever for enhancing economic, environmental, and social sustainability within firms, while highlighting the lack of adequate indicators for measuring such impacts [ 123 ]. Along similar lines, another research [ 124 ], [ 125 ] demonstrate the influence of maintenance services on the economic, social, environmental, and technological dimensions of sustainability. For example, in the automotive industry, PSA-Stellantis Morocco has implemented digital solutions that leverage predictive maintenance and the Industrial Internet of Things (IIoT) to reduce downtime and optimize energy consumption, thus achieving significant improvements in operational efficiency and sustainability. Furthermore, researchers such as [ 30 ] concur that Sustainable Total Productive Maintenance (STPM)—which integrates sustainability, Industry 4.0 technologies, and the circular economy—improves organizational performance. A notable example is Siemens, which has adopted smart and predictive maintenance (STPM) practices by integrating AI More precisely the Deep Learning algorithm into its industrial operations. By equipping its equipment with IoT sensors that monitor parameters such as temperature, vibration, and pressure in real time, Siemens collects valuable operational data. This data is analyzed by AI algorithms to detect anomalies and predict potential failures before they occur. Additionally, the use of digital twins and the MindSphere cloud platform allows Siemens to simulate and optimize machine performance virtually [ 126 ]. As a result, Siemens achieved a 30% reduction in unplanned downtime, a 25% decrease in maintenance costs by avoiding unnecessary interventions, and a 20% increase in equipment lifespan, thereby reducing waste and minimizing production disruptions [ 127 ]. Authors such as Silvestri et al. [ 128 ] emphasize the technological “fourth dimension”, reflecting growing recognition of how innovation is central to optimizing and advancing maintenance activities in the era of Industry 4.0. Similarly, other studies [ 129 ] indicate that the technological dimension of sustainability is critical to the maintenance function: it relies on the ability to optimize processes and associated parameters while ensuring the system’s long-term viability and profitability. This section adopts this perspective, examining the role of M4.0–I4.0 integration across the three traditional pillars of sustainability (economic, environmental, and social), while introducing a fourth, technology-focused dimension, essential for sustainable manufacturing as illustrated in Fig. 8 . The figure illustrates the synergy between M4.0 processes and I4.0 technologies, highlighting their collective impact on achieving sustainable manufacturing and global performance across four key dimensions of sustainability: environmental, economic, social and technological. Environmental sustainability is reinforced by waste management, recycling, energy efficiency and carbon emission reduction, with optimization of natural resources (air, water, soil). Economic sustainability translates into increased productivity, reduced maintenance costs and better management of indirect costs. On the social front, M4.0 processes help to improve working conditions, safety, employee training and reduce absenteeism, thus fostering a fairer, safer working environment. Finally, technological sustainability is based on the adoption of agile and flexible technologies, enabling innovation and rapid adaptation to market needs, while guaranteeing data security. Overall, this diagram highlights how the integration of M4.0 processes with I4.0 technologies supports a holistic approach to sustainable manufacturing and optimizes performance on a global scale. 4.2.1 Economic Sustainability According to [ 130 ], the adoption of Industry 4.0 technologies plays a pivotal role in reducing the Total Cost of Ownership (TCO) of production equipment, notably through advances in predictive maintenance. By leveraging cutting-edge tools such as the Internet of Things (IoT), cloud computing (CC), Big Data & Analytics (BDA), and cybersecurity (CS), companies can optimize their maintenance strategies and extend the operational life of industrial equipment [ 131 ]. One primary benefit of these approaches lies in the ability to detect patterns of degradation or inefficiency within machines and manufacturing processes, thus allowing failures to be anticipated before they occur [ 132 ]. Continuous, intelligent equipment monitoring enables proactive intervention planning, thereby reducing the high costs typically associated with emergency repairs, unplanned downtime, and production losses. According to Gebler et al., integrating digital modeling and digital twins represents another strategic lever for sustainable manufacturing [ 132 ]. By creating virtual replicas of physical equipment—based on simulation and sensor data—companies can test various maintenance scenarios, predict component wear, and fine-tune operating parameters [ 133 ]. From an economic standpoint, these technologies promote more efficient resource allocation by preventing the premature replacement of still-functional components and extending the lifespan of industrial assets. Moreover, Big Data and Artificial Intelligence (AI) in predictive maintenance enable the identification of factors influencing productivity and energy consumption, facilitating dynamic adjustments in processes to maximize both operational and environmental efficiency [ 134 ]. In addition, incorporating cybersecurity into advanced maintenance policies is critical to safeguarding the security and integrity of interconnected industrial systems. With the growing prevalence of IoT and cloud computing, data protection is paramount to avoid corruption of predictive maintenance models or the risk of cyberattacks compromising production [ 135 ]. 4.2.2 Environmental Sustainability The integration of Maintenance 4.0 (M4.0) technologies—including AR/VR, 3D printing, AI, IoT, and predictive analytics—is transforming industrial maintenance strategies. Such innovations enable optimized asset management, concurrently enhancing equipment reliability, worker safety, and environmental sustainability. On one hand, using AR reduces the need for extended manual interventions by providing technicians with real-time interactive instructions, thus minimizing human error and equipment downtime [ 136 ]. AR also allows direct access to technical information without resorting to physical documents, thereby eliminating paper-based instructions and decreasing the ecological footprint [ 137 ]. On the other hand, 3D printing (additive manufacturing, AM) plays a pivotal role in industrial maintenance by supporting on-demand production of spare parts—preventing overstock, lowering logistics costs, and cutting material waste. The author [ 138 ] underscores that AM can generate small batches of customized components, offering an economical alternative to traditional manufacturing approaches. AM can further produce lighter, more resilient parts, reduce energy consumption and extend equipment lifespans. Additionally, AI and IoT boost maintenance efficiency through advanced predictive analytics [ 139 ]. By embedding IoT sensors in industrial machinery, maintenance systems can detect and report anomalies in real time, thus averting costly failures and limiting waste associated with malfunctions [ 140 ]. This proactive approach significantly cuts energy consumption and production losses, promoting more sustainable use of industrial resources. Another key advantage of M4.0 technologies is reducing transport and logistics demands. Additive manufacturing and predictive maintenance minimize unnecessary technician travel by identifying and resolving issues remotely, thereby curbing CO₂ emissions tied to physical interventions [ 141 ]. Moreover, condition-based maintenance avoids prematurely replacing still-functional parts, thus optimizing equipment life cycles. 4.2.3 Social Sustainability According to [ 133 ], the integration of M4.0 and I4.0 technologies—particularly AR, AI, and IoT—provides considerable benefits in terms of social sustainability. By improving the quality and efficiency of human–machine interactions, M4.0 fosters a safer work environment, lowers the cognitive and physical burden on operators, and enhances human performance in maintenance tasks. One of the chief social advantages of M4.0 is the reduction of human error, which in turn translates into fewer incidents and accidents related to maintenance [ 8 ]. Through virtual models and intelligent decision-support systems, operators receive precise, real-time guidance on required interventions. These technologies allow teams to anticipate failures, direct technicians in their tasks, and provide context-appropriate recommendations for secure and efficient interventions [ 130 ]. Furthermore, M4.0 plays a crucial role in improving working conditions and reducing operational stress. Automation of repetitive or hazardous tasks, combined with digital assistance via AR headsets or connected interfaces, enables technicians to focus on more value-added, less physically demanding activities. This approach leads to greater workplace well-being and reduces absenteeism stemming from accidents or musculoskeletal disorders. Lastly, adopting M4.0-I4.0 enhances the social perception and credibility of enterprises among employees, consumers, and industry partners [ 142 ]. By investing in solutions that prioritize human capital, ensure optimal working conditions, and promote equality and diversity, organizations position themselves as responsible, sustainable actors. This strategy not only strengthens their employer brand but also aligns with escalating societal and environmental standards [ 143 ]. 4.2.4 Technological Sustainability: The Fourth Pillar Beyond the three traditional pillars of sustainability (economic, environmental, and social), the technological dimension has emerged as an indispensable fourth pillar within the M4.0–I4.0 context in sustainable manufacturing. According to [ 144 ], this dimension hinges on the capacity to integrate scalable, interoperable, and resilient technologies that support long-term sustainable manufacturing objectives. Furthermore, the authors highlight the ability of companies to adopt, over time, innovative solutions that ensure both the longevity of production systems and their alignment with the challenges posed by sustainable manufacturing. On the one hand, technological sustainability manifests in the agility and resilience of industrial systems. I4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins enable the rapid detection of emerging failures, real-time adjustment of production parameters, and highly accurate planning of interventions [ 110 ]. Such responsiveness not only reduces production losses but also ensures continuous process optimization, thereby maintaining performance in uncertain and evolving environments. On the other hand, technological sustainability involves robust governance and rigorous innovation management. According to [ 145 ], the M4.0–I4.0 approach extends beyond merely deploying tools (e.g., sensors, collaborative robots, digital twins): it also requires implementing cybersecurity protocols, adopting standards (interoperability, normalization), and providing ongoing training for operators. This organizational and managerial dimension is crucial to preserving the benefits of advanced technologies and avoiding overreliance on insufficiently mastered solutions. In addition, the technological dimension exerts a multiplier effect on the three other pillars of sustainability. For instance, proactive predictive maintenance contributes directly to economic efficiency (reduced downtime), environmental conservation (lower waste and excessive consumption), and social well-being (enhanced operator safety). The Big Data generated by maintenance systems also improves strategic decision-making, fosters traceability and transparency, and thus strengthens stakeholder trust (operators, customers, investors). Finally, technological sustainability thrives on a broader ecosystem that encourages co-creation and open innovation. Collaborations between companies, research laboratories, and startups specializing in AI or robotics accelerate continuous improvement in maintenance. This collaborative approach anchors I4.0 technologies more firmly in industrial reality, producing more robust solutions that are better suited to the challenges of sustainable manufacturing [ 19 ]. Ultimately, the coherent integration of technology, management, and workforce training stands out as a pivotal lever for achieving sustainability goals in future industries. Figure 9 provides a concise overview of the key priorities and challenges facing industrial systems, organized into four dimensions: Environmental: Energy consumption is the leading concern (93%), followed by waste management (53%) and CO₂ emissions (40%). Economic: Cost (47%) and quality (40%) remain paramount, whereas inventory reduction Health and safety (33%) continue to be a top priority, superseding working conditions (13%) and training (2%). Technological: Equipment reliability (40%) and interoperability (33%) are critical, while digital skills (27%) and data protection (27%) persist as significant concerns. Our analysis reveals that (38%) of selected studies focus primarily on environmental sustainability, followed by economic (29%), technological (22%), and social (15%) dimensions. The predominance of environmental studies highlights the industry’s increasing emphasis on reducing carbon footprints through predictive maintenance and digital twins. Notably, the manufacturing and automotive sectors contribute 47% of studies, indicating early adoption of I4.0-driven sustainability practices. Collectively, these findings underscore the urgency of optimizing energy efficiency and bolstering technical resilience to address environmental and economic imperatives, while simultaneously ensuring worker well-being. 5. Discussion First, this part addresses the drivers and obstacles of the M4.0-I4.0 integration for the change to attain sustainable manufacturing. After that, a theoretical framework is given together with ideas for more investigation. 5.1 Drivers and barriers of M4.0-I4.0 in achieving sustainable manufacturing The integration of M4.0 and I4.0 technologies offers immense potential for promoting sustainability in the manufacturing sector. By merging digitization, automation, and artificial intelligence, companies can significantly optimize production processes, reduce waste, and improve resource efficiency [ 146 ]. When these technologies are aligned with SM principles, the resulting synergy enhances environmental performance, streamlines operations, and enables real-time decision-making [ 147 ],[ 148 ]. Furthermore, collaborative data-sharing practices foster transparency and global coordination among stakeholders [ 149 ]. Advanced tools such as digital twins—virtual representations of physical systems built from real-time sensor data—enable continuous improvement and better-informed decisions throughout the asset lifecycle [ 150 ]. In parallel, predictive analytics powered by big data can convert vast datasets into actionable insights, contributing to proactive maintenance strategies and economic gains [ 151 ]. To sustain these advancements, however, ongoing workforce development becomes imperative. Employees must acquire not only technical skills but also a digital mindset to operate and maintain complex, interconnected systems. Without adequate training, companies risk underutilizing these technologies and facing organizational pushback. Despite the clear advantages of the integration of M4.0-I4.0, its implementation faces numerous practical challenges. Organizational change—especially in industrial environments—is often met with substantial resistance. As highlighted in [ 152 ], more than 70% of change initiatives fail, largely due to inadequate engagement with operational processes, disregard for employee values, weak leadership, and inaccurate resource planning. These issues are particularly relevant in the context of adopting advanced digital technologies, where change affects not only tools and systems but also corporate culture and employee behavior. A prominent obstacle is the lack of workforce readiness. Many technicians and operators lack the necessary Information and Communication Technology (ICT) skills tailored to maintenance-specific applications [ 153 ]. This deficiency creates a chain of barriers—technological, organizational, and behavioral—amplified by insufficient training infrastructures. When organizations fail to invest in capacity building, employees may struggle to adopt new systems or even reject them altogether. Such resistance is often driven by job security concerns and fear of redundancy, which are deeply rooted in cultural and psychological dimensions. Employees may perceive digitalization as a threat rather than an opportunity, especially when communication around change is unclear or top-down [ 154 ]. This contributes to a broader category of managerial barriers, where a misalignment between strategic vision and employee perception hinders adoption. On the technical front, the lack of standardized data formats and protocols leads to interoperability issues that obstruct seamless integration across departments [ 155 ]. Moreover, organizations express concerns about cybersecurity—a growing risk given the reliance on cloud infrastructure and real-time data exchange [ 156 ]. Breaches of sensitive operational or maintenance data can result in severe economic and reputational damage. Additionally, financial constraints represent a considerable hurdle. The acquisition of smart sensors, AI platforms, and edge computing systems often demands substantial upfront investment, which many companies—especially SMEs—are unable or unwilling to make. Limited access to infrastructure and cutting-edge technologies further exacerbates these difficulties, particularly in regions lacking digital maturity [ 157 ]. To overcome these multifaceted barriers, a coordinated effort among key stakeholders is vital. Governments and regulatory authorities play a central role in shaping enabling environments through clear guidelines, financial incentives, and sustainability mandates [ 158 ]. Universities and research institutions contribute by developing innovative models and offering training programs that prepare the workforce for digital transitions [ 159 ]. At the same time, investors and financial institutions must support innovation with tailored financing instruments that reduce the financial burden of early adoption. Ultimately, the successful integration of I4.0 technologies within sustainable manufacturing frameworks depends on more than just technical capability—it requires cultural transformation, stakeholder alignment, and long-term strategic commitment. Addressing both the enablers and the barriers holistically will be key to unlocking the full potential of the integration of M4.0-I4.0. 5.2 Theoretical Framework Development Conceptual frameworks are developed by integrating core concepts central to a research phenomenon—in this case, the integration between Maintenance 4.0 (M4.0) and Industry 4.0 (I4.0) in achieving Sustainable Manufacturing (SM)—to systematically describe, explain, and contextualize complex interactions within the research domain [ 160 ], [ 161 ]. These frameworks synthesize fragmented knowledge into a cohesive structure, providing a holistic "bigger picture" that guides empirical and theoretical inquiry. Building on insights from the literature review, Fig. 10 presents a conceptual framework that maps the synergies M4.0-I4.0, highlighting their collective role in advancing SM. Despite growing scholarly interest, the field remains nascent, with only 75 publications identified to date. This underscores the need to transcend superficial analyses of management practices and adopt a systemic perspective. The proposed framework addresses this gap by anchoring itself in the foundational principles of M4.0 and I4.0 technologies. On one hand, I4.0 enables Automation, connectivity, and data-driven optimization through real-time monitoring, predictive analytics, and cyber-physical system integration. On other hand, M4.0 emphasizes proactive maintenance, asset lifecycle management, and resilience via advanced diagnostics, self-healing systems, and predictive strategies. For example, IoT-enabled sensors generate granular machine health data, which AI-driven models analyze to preempt failures and optimize energy consumption [ 139 ]. This symbiosis enhances operational performance while advancing environmental sustainability through reduced emissions and material efficiency. However, scalable implementation faces barriers such as workforce skill gaps, cybersecurity vulnerabilities, and a lack of standardized interoperability protocols. These challenges necessitate deeper empirical investigation into adaptive training frameworks and secure, interoperable architectures [ 162 ]. This diagram illustrates how I4.0 technologies and M4.0 contribute to Sustainable Manufacturing by acting on four key dimensions: economic, social, environmental and technological. Economically, these technologies improve quality, tool performance, efficiency and flexibility, while reducing inventories and risks. Socially, they promote health, safety and working conditions, as well as skills development and employee commitment. Environmentally, they enable optimized use of energy and materials, reduce CO2 emissions and support circular economic practices. Technologically, they ensure reliability, security, digital innovation and system scalability. I4.0 technologies (such as IoT, AI, Big Data, digital twins, etc.) and the processes associated with M4.0 (such as data acquisition, diagnostics, decision-making and maintenance planning) create an essential synergy for achieving these objectives, although they face certain barriers that can slow down their adoption. It also emphasizes the role of institutional dynamics—legal frameworks, cultural norms, and organizational ethics—in shaping ethical decision-making within M4.0-I4.0 integration [ 163 ]. For instance, stringent environmental regulations may accelerate SM adoption, while organizational culture influences stakeholder engagement. By aligning technological capabilities with institutional priorities, companies can foster ethical practices and optimize cost structures, material flows, and waste reduction [ 164 ]. This process is dynamic, enabling businesses to leverage M4.0-I4.0 for SM through iterative knowledge-building and skill integration. Enhanced information networks and analytical capabilities allow firms to mitigate environmental impacts via machine life-cycle management [ 165 ], [ 166 ]. However, progress is iterative, requiring continuous refinement of knowledge bases and resolution of vulnerabilities in cutting-edge technologies, smart devices, and data analytics systems. Finally, integrating I4.0 and M4.0 into operational practices enhances performance through reflexive control of processes, such as cost optimization and material availability, while addressing sustainability challenges like labor arbitrage and waste control [ 167 ]. Cross-sector collaboration among policymakers, educators, practitioners, and nonprofits is critical to institutionalize SM in policies and management practices, fostering knowledge sharing and systemic alignment with sustainability goals [ 167 ]. 5.3 Implications and Future Research Directions The findings of this study reveal several promising avenues for further investigation into the integration of M4.0-I4.0 in achieving sustainable manufacturing outcomes. Research implications The convergence of M4.0 and I4.0 technologies opens new avenues for enhancing organizational sustainability and resilience, yet the current body of literature indicates room for deeper empirical inquiry. Existing studies predominantly explore the individual impacts of M4.0 and I4.0 solutions rather than their combined effect. There is, therefore, a pressing need for multi-method and longitudinal research designs to capture the breadth of benefits, challenges, and best practices. Quantitative inquiries could measure improvements in key performance indicators (e.g., downtime, waste reduction, cost efficiency), while qualitative approaches—such as case studies or action research—may illuminate nuanced organizational factors like cultural readiness and skill development. By integrating these insights, future scholarship can better establish a robust theoretical basis for the integration of M4.0–I4.0 in sustainable manufacturing. Additionally, researchers may focus on the contextual heterogeneity of implementing these advanced systems. For instance, sector-specific studies could explore how Small-to-Medium Enterprises (SMEs) differ from large multinationals in terms of technology adoption, supply-chain collaboration, and financial constraints. Such comparative perspectives would help isolate enablers and barriers unique to different organizational scales or industrial domains, thereby offering clearer pathways for generalization and theory building. Managerial implications From a managerial standpoint, strategic alignment emerges as a critical success factor. Integrating M4.0–I4.0 into existing operations calls for a coherent vision, ensuring that predictive maintenance and durability principles reinforce, rather than compete with, core business objectives. Managers must also prioritize upskilling the workforce; as advanced analytics, digital twins, and AI-based prognostics gain traction, organizations require employees adept at interpreting large data sets, collaborating across departments, and driving continuous improvement. This focus on people-centered strategies is equally essential to build internal champions who can advocate for technology investments and process innovations. Moreover, change management cannot be overlooked. Implementing M4.0–I4.0 solutions typically entails reconfiguring operational workflows and reevaluating established performance metrics. Transparent communication and inclusive planning are thus paramount for easing potential resistance to change. Managers who actively involve technicians, engineers, and frontline operators in decision-making processes often report higher acceptance rates and better long-term outcomes. Policy implications Policy interventions play a pivotal role in accelerating adoption of M4.0–I4.0 for sustainable manufacturing. First, regulatory bodies can promote standardization by defining interoperability protocols and data-exchange guidelines, minimizing fragmentation among technology providers. Such policies would reduce complexity for firms seeking to adopt integrated solutions and foster broader, more equitable participation in advanced manufacturing ecosystems. Second, financial and educational incentives can further catalyze technological uptake. Governments might provide tax credits or grants for organizations that invest in predictive maintenance tools, energy-efficient equipment, or circular business models. Similarly, public–private partnerships could sponsor specialized training programs that equip workers with the digital competencies essential for operating, maintaining, and refining smart production systems. This approach not only elevates human capital but also helps align national workforce capabilities with evolving industry requirements. Finally, international collaboration holds promise for coordinating best practices around data security and carbon footprint reduction. As global supply chains become increasingly interdependent, policymakers may consider cross-border agreements to enable secure data transfer, consistent standards, and mutual recognition of certification schemes, ultimately facilitating a cohesive shift toward sustainable, technology-driven industrial processes worldwide. Table 3 Actionable Strategies for integration of M4.0 and I4.0 in SM Focus Area Strategy Linked Finding Application in the real life Workforce Development Upskill staff in AI/ML tools and AR/VR maintenance training 58% of studies cite skill gaps as a barrier [ 168 ], [ 169 ] Partner with platforms like Coursera or Udacity to develop AR-based maintenance training modules. Technology Adoption Prioritize IoT + Digital Twin integration for predictive maintenance IoT-digital twin synergy reduces energy consumption by 15% [ 170 ], [ 171 ] Deploy Siemens Mind Sphere for real-time asset monitoring and failure prediction. Policy Incentives Subsidize blockchain adoption for transparent maintenance logs Blockchain reduces human errors by 20% and enhances auditability [ 172 ], [ 173 ] Align with EU’s Digital Decade 2030 targets for secure, traceable supply chains. Cybersecurity Implement ISO/IEC 27001 standards for IIoT systems 65% of studies identify cybersecurity as a critical barrier [ 174 ], [ 175 ] Adopt zero-trust architecture frameworks for M2M communication in smart factories. Circular Economy Incentivize 3D printing for on-demand spare parts Additive manufacturing reduces material waste by 30% [ 176 ], [ 177 ] Collaborate with local 3D printing hubs to produce certified, low-carbon components. Future research must prioritize resolving these challenges while fostering cross-sector collaboration to fully realize the potential of M4.0-I4.0 integration in achieving a sustainable manufacturing ecosystem. By aligning M4.0 and I4.0 with SM objectives, the framework establishes a cohesive pathway to harmonize industrial performance with environmental stewardship, social responsibility, economic viability and technological integration, thereby laying a foundation for sustainable industrial growth. Conclusion This review shows that the integration of M4.0 and I4.0 presents a great possibility to convert present manufacturing paradigms into more robust, efficient, and environmentally friendly systems. Building on cutting-edge digital enablers such as IoT, artificial intelligence, Big Data, and digital twins, M4.0 improves maintenance operations by lowering unexpected downtime, boosting resource use, and extending asset life cycles. Meanwhile, I4.0 promotes data-driven decision-making, smart automation, and real-time communication all over the manufacturing process. By addressing economic, environmental, social, and technological aspects at once, the convergence of these technologies underlies holistic sustainability. The study draws attention to various flaws in theory and practice even if the clear advantages abound. There is yet little empirical study on M4.0–I4.0 synergy; most of it concentrates on either isolated technologies or particular industry settings, therefore limiting generalizability. Furthermore, underlining the complexity of large-scale implementation are organizational and behavioral challenges spanning from personnel upskilling to cultural readiness. These difficulties require multidisciplinary approaches that span engineering, management, and social sciences to provide comprehensive solutions that balance creative maintenance techniques with organizational capacity-building. Importantly, this review distills an evidence-based implementation roadmap and a maturity matrix; a field pilot currently under way at ABC company is validating these tools, and its results will be reported separately. Future research might benefit from longitudinal studies tracking the long-term effects on operational performance, environmental impact, and workforce well-being of predictive maintenance and smart industrial technologies. Comparative studies between sectors and geographical areas are equally important since they help to better grasp contextual elements that either support or hinder the acceptance of improved maintenance. At the legislative level, standardization of data protocols and cybersecurity frameworks could speed up the deployment of M4.0–I4.0 solutions, while incentives for sustainable manufacturing and carbon reduction may stimulate greater participation. In addition, further inquiry into Industry 5.0—the emerging paradigm that emphasizes human-centric, resilient, and sustainable manufacturing—could prove highly valuable. While Industry 4.0 has focused on digitization, automation, and data-driven optimization, Industry 5.0 introduces a deeper integration of human insight, well-being, and creativity into advanced technological ecosystems. Future studies could explore how Maintenance 4.0 and Industry 4.0 solutions might evolve within an Industry 5.0 framework, particularly in areas such as co-bots (collaborative robots), adaptive AI-driven maintenance, and hyper-personalized human–machine interfaces. This exploration may reveal new strategies for fostering inclusive workforce development, ethical AI deployment, and enhanced resilience against unexpected disruptions—ultimately expanding the scope of sustainable manufacturing beyond operational efficiency to encompass social responsibility and long-term societal benefits. Abbreviations I4.0 Industry 4.0 M4.0 Maintenance 4.0 SM Sustainable Manufacturing LE Linear Economy CE Circular Economy IoT Internet of Things IIoT Industrial Internet of Things AI Artificial Intelligence CPS Cyber-Physical Systems BDA Big Data Analytics AR Augmented Reality VR Virtual Reality RFID Radio Frequency Identification CC Cloud Computing CS Cybersecurity TCO Total Cost of Ownership ICT Information and Communication Technology Declarations Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Code availability: Not applicable. Ethical approval: Not applicable. Consent to participate: Not applicable. Consent for publication: All co-authors have been duly acknowledged, and their consent for submission has been obtained. References P. 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Revolutions.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/964d39dc8fedd2a73113a053.jpg"},{"id":87354001,"identity":"820a194e-5a11-433b-b04d-e9cc75a8ce6a","added_by":"auto","created_at":"2025-07-23 04:23:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131848,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Framework – Screening, Selection, and Inclusion Process for SLR.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/5b9b766d885aa1427a7e0d3d.jpg"},{"id":87354512,"identity":"fb46f637-584b-433b-92b5-b978e1f48104","added_by":"auto","created_at":"2025-07-23 04:31:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85321,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of publications per year from 2015 to 2024.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/60ce8937bde4ecb8280a7e6d.jpg"},{"id":87354003,"identity":"8870397d-4df7-41b7-a973-f9fa50c7aa82","added_by":"auto","created_at":"2025-07-23 04:23:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98864,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of publications by paper type from 2015 to 2024.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/dbf96be075c05c8517abee77.jpg"},{"id":87353991,"identity":"24f52383-d65a-4d8c-b14c-a2c2b2176641","added_by":"auto","created_at":"2025-07-23 04:23:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67345,"visible":true,"origin":"","legend":"\u003cp\u003eBreakdown of the selected publications by journal.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/1f07d0ff824c8e7aa392a5f6.jpg"},{"id":87353996,"identity":"aae61372-3c99-4c3d-ac77-86cf1c46aad3","added_by":"auto","created_at":"2025-07-23 04:23:19","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102615,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Technologies Referenced in the Selected Publications.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/6b9950d526880ce5cff95020.jpg"},{"id":87354021,"identity":"c2a5397f-5315-4709-b146-02ac5a96c5d7","added_by":"auto","created_at":"2025-07-23 04:23:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":257806,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Map of Key Concepts Related to Industry 4.0, Maintenance 4.0 processes, and Sustainable Manufacturing.\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/7d4d7ee7812cb4f478cacdaf.jpg"},{"id":87354514,"identity":"383c3129-b1e2-40e4-97d9-f706fe3d1446","added_by":"auto","created_at":"2025-07-23 04:31:21","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":107670,"visible":true,"origin":"","legend":"\u003cp\u003eContribution of M4.0-I4.0 implementation to the four pillars of the SM and the associated indicators.\u003c/p\u003e","description":"","filename":"8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/7f625bd219fa551558b9fc88.jpg"},{"id":87354002,"identity":"185b3a94-e363-464f-ad5d-1cead3334a0c","added_by":"auto","created_at":"2025-07-23 04:23:19","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":130137,"visible":true,"origin":"","legend":"\u003cp\u003eSustainability manufacturing dimensions and the associated indicators studied by the papers.\u003c/p\u003e","description":"","filename":"9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/965e471d0232ca5b0cd8d986.jpg"},{"id":87353994,"identity":"7c432427-47a4-493c-9a95-405a0c3703f5","added_by":"auto","created_at":"2025-07-23 04:23:19","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":147255,"visible":true,"origin":"","legend":"\u003cp\u003eA new conceptual framework for integrating I4.0 technologies and M4.0 levers to achieve Sustainable Manufacturing objectives.\u003c/p\u003e","description":"","filename":"10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/7710a5835079f07e55a94b7e.jpg"},{"id":87354848,"identity":"c491cb22-15ad-499f-905a-7b4a40c6266a","added_by":"auto","created_at":"2025-07-23 04:39:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2878259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7184682/v1/e08c163f-d640-4353-9bfa-87023c8254b9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eIntegrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing: A Systematic Literature Review\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eContrary to the principles of a linear economy, which mainly relies on intense resource extraction and consumption, sustainable manufacturing concerns have arisen as a primary focus of global objective [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With rising issues such as depletion of natural resources, ecosystem degradation and depletion of non-renewable resources like rare earth metals, and ecosystem degradation from industrial waste, it has become important to reinvent production and consumption patterns by embracing sustainable and regenerative techniques [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is within this environment that novel techniques, such as Industry 4.0 Technologies (I4.0), are emerging as possible solutions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e],[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. On the one hand, the growing adoption of I4.0 is profoundly transforming business environments [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The cumulative effect of these disruptions is profoundly transforming the global manufacturing [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In this context, the sustainability of these changes-in connection with social, economic, and environmental factors-is of paramount importance. On the other hand, optimized maintenance management plays a key role in enhancing companies' adaptability to these disruptions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. They aim not only to reduce the impact of human activities on the environment, but also to ensure economic development in harmony with the preservation of natural resources [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Industrial Revolution has radically transformed production activities, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. It began with steam-powered mechanization (Industry 1.0), progressed to the use of electricity for mass production (Industry 2.0), and moved toward automated production processes (Industry 3.0). More recently, Industry 4.0 has emerged, characterized by I4.0 technologies such as the Internet of Things, cyber-physical systems, cloud computing, and big data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This new paradigm enables the analysis of data extracted from sensors characterized by high velocity, variability, veracity, volume, and value. Through in-depth analysis, they generate production forecasts, optimize maintenance management, provide insights into equipment, and create resilient, sustainable, and digital manufacturing processes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this context, both the sustainability of changes and the optimized maintenance management becomes crucial to ensure organizational adaptation to such disturbances [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e],[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. By integrating sustainable practices and innovative technologies, companies may not only prevent disruptions in production processes but also save costs, prolong equipment lifespan, and lessen their ecological imprint. These efforts eventually boost their long-term competitiveness.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to AFNOR FD X 60\u0026thinsp;\u0026minus;\u0026thinsp;000 (2016), maintenance serves as a cornerstone of sustainability. By enhancing equipment availability and extending its lifespan, maintenance reduces the need for raw materials, energy, and resources required to manufacture new goods, while simultaneously limiting the waste generated by their disposal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, maintenance stimulates local job creation, driving its positive impact across economic, environmental, social, and technological dimensions are all affected. However, the transition to sustainable manufacturing also poses challenges; for example, if not properly managed, it can disrupt production workflows, degrade product quality, and undermine a company's competitive advantage [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, risks such as security threats, integration issues with existing systems, and increased complexity in operations must be addressed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In response to these challenges, the adoption of technological and technical solutions supporting sustainable development has gained momentum in recent years [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Advanced tools such as predictive maintenance systems, IoT-enabled sensors, and AI-driven analytics enable companies to anticipate failures, optimize resource utilization, and foster resilient production processes. These innovations not only minimize operational disruptions but also strengthen long-term sustainability by reducing waste, lowering costs, and supporting a Circular Economy model [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious research has yet to provide comprehensive, systematic, and quantitative answers regarding the integration of I4.0 technologies and M4.0 practices to achieve SM, limiting a deeper understanding of the enabling mechanisms and potential synergies. While several studies have explored the intricate relationships between I4.0 and SM, particularly the impacts of I4.0 technologies on economic, environmental, social, and technological dimensions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], qualitative approaches have largely dominated the field. These studies offer valuable insights into interpretations and conceptual frameworks but fall short in providing structured, scientific, and quantitative analyses of how specific I4.0 technologies directly influence SM practices [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, the mechanisms for integrating I4.0 technologies\u0026mdash;such as IoT, artificial intelligence, and additive manufacturing\u0026mdash;into SM processes remain insufficiently understood. The lack of quantitative data hinders the development of systematic models that illustrate the concrete contributions of these technologies to sustainability initiatives [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe I4.0 and SM can be considered complementary paradigms with a shared vision of enhancing the sustainability and efficiency of operational systems [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Industry 4.0, by leveraging advanced technologies such as IoT, AI, and data analytics, enables optimized resource management, waste minimization, and the reduction of inefficiencies in industrial workflows. Concurrently, sustainable manufacturing\u0026mdash;the practical realization of SD within the manufacturing domain\u0026mdash;advocates for circular economy principles by promoting material reuse, extending product life cycles, and reducing waste generation [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Together, these two approaches (I4.0 et M4.0) pave the way for a transformative shift toward sustainable industrial practices. They maximize resource efficiency and resilience while mitigating environmental impacts. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes prior studies that investigated the intersection between I4.0 and Maintenance, especially in the context of Corporate Social Responsibility (CSR) and Sustainable Manufacturing (SM).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePast Literature Reviews.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTitle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePaper reviewed\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFindings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMurtaza and al. (2024) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe transition from Industry 4.0 to Industry 5.0, in the context of predictive maintenance and condition monitoring, aims to foster Sustainable Manufacturing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMallioris and al. (2024) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Predictive maintenance in Industry 4.0: A systematic multi-sector mapping\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe paper provides a systematic mapping of predictive maintenance (PdM) within the framework of Industry 4.0 across multiple sectors while highlighting the benefits of predictive maintenance in improving operational efficiency, reducing downtime, and supporting sustainable manufacturing processes.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStana and al. (2024) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Identification of Criteria for Enabling the Adoption of Sustainable Maintenance Practice: An Umbrella Review\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe paper identifies and analyzes the key criteria that facilitate the adoption of sustainable maintenance practices through an umbrella review approach. It consolidates findings from multiple studies to highlight critical factors such as environmental impact, cost efficiency, technological integration, and policy frameworks that enable sustainable maintenance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePsarommatis and al.\u003c/p\u003e\u003cp\u003e(2023) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe paper envisions Maintenance 5.0 as a human-centered, sustainable evolution of maintenance systems beyond Industry 4.0. It emphasizes the need for collaboration between advanced technologies and human intelligence to create more resilient, efficient, and environmentally responsible maintenance strategies.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHallioui and al. (2023) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;A Review of Sustainable Total Productive Maintenance (STPM)\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe paper explores the concept of Sustainable Total Productive Maintenance (STPM), which integrates sustainability principles into traditional Total Productive Maintenance (TPM) practices. It highlights How STPM contributes to improving equipment efficiency, reducing resource consumption, and minimizing environmental impacts while maintaining operational performance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWang and al. (2020) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Smart remanufacturing and maintenance for machinery in Industry 4.0\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe paper explores the integration of smart remanufacturing and maintenance strategies for machinery within the framework of Industry 4.0 to achieve maximum efficiency, reliability and sustainability of machine operations in manufacturing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOur study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ldquo;Paving the Way to Sustainability: Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing Goals\u0026rdquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThe study presents an SLR study to discuss the connections between I4.0 Technologies and M4.0 with Sustainable Manufacturing\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA thorough analysis of the current application of I4.0-M4.0 in the context of SM is essential to build a solid foundation for deepening and broadening the integration of these technologies. Maintenance, by harnessing advanced tools such as IoT, artificial intelligence and data analytics, plays a central role in implementing sustainable manufacturing principles. By optimizing equipment management, it guarantees the efficiency, reliability and sustainability of industrial systems, thus contributing directly to SM objectives. Better mastery of current I4.0 technologies and M4.0 approaches can not only reinforce these benefits but also unlock new opportunities to extend the application of sustainable manufacturing. To achieve these results, it is essential to answer the following key research questions:\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eRQ1\u003c/strong\u003e: How can the integration of Industry 4.0 and Maintenance 4.0 be leveraged to enhance both sustainability and competitiveness, and what are the key managerial and technological levers involved?\u003c/p\u003e\n\u003cp\u003e● \u003cstrong\u003eRQ2\u003c/strong\u003e: What are the main barriers to the integration of I4.0 and M4.0, and what roadmap can organizations follow to implement these solutions effectively?\u003c/p\u003e\u003cp\u003eAnswering these questions requires interdisciplinary research combining technological, organizational, and strategic perspectives. A conceptual framework could also be developed to guide industries in leveraging I4.0-M4.0 effectively. Additionally, showcasing successful implementations and case studies can provide actionable insights for companies aiming to align their practices with sustainability goals.\u003c/p\u003e\u003cp\u003eBy systematically synthesizing the dispersed body of knowledge, this article makes three main contributions. First, it maps the technology\u0026ndash;maintenance\u0026ndash;sustainability nexus and highlights twelve integration mechanisms that recur across empirical studies (e.g., condition‑based energy management, digital twin‑aided scheduling). Second, it proposes a multi‑layered conceptual framework that aligns asset‑level predictive maintenance data with plant‑level sustainability dashboards and corporate sustainability strategy. Finally, it outlines a diagnose\u0026ndash;design\u0026ndash;deploy roadmap that practitioners can adapt to accelerate their transition towards sustainable, resilient, and digitally empowered factories.\u003c/p\u003e\u003cp\u003eThe remainder of the paper is organized as follows. Section 2 describes the SLR protocol, including database selection, inclusion/exclusion criteria, and the PRISMA flow diagram. Section 3 presents descriptive statistics of the selected literature. Section 4 offers a critical analysis of seminal studies and synthesizes integration mechanisms and sustainability outcomes. Section 5 discusses theoretical and managerial implications, while Section 6 introduces the implementation roadmap. Finally, Section 7 concludes and suggests avenues for future research.\u003c/p\u003e"},{"header":"2. Research methodology","content":"\u003cp\u003eThis Systematic Literature Review (SLR) aims to investigate how the combined implementation of Industry 4.0 (I4.0) technologies and Maintenance 4.0 (M4.0) practices contributes to advancing the economic, environmental, social, and technological pillars of sustainable manufacturing. The review was conducted following a preregistered protocol and the PRISMA 2020 reporting framework. We predefined the objectives, search strategy, and inclusion/exclusion criteria to ensure transparency and reproducibility [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Our study employs a mixed-methods approach, integrating qualitative thematic analysis with quantitative profiling to provide decision-oriented insights.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Search Strategy\u003c/h2\u003e\u003cp\u003e\u003cb\u003ePhase 1: Selection of studies\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn line with the research questions, the papers in this review investigate the contributions of integrating I4.0 technologies and M4.0 practices in promoting sustainable manufacturing and enhancing competitiveness. For the purposes of this review, only peer-reviewed articles written in English were considered. Given that each topic examined in this study is novel, it is important to specify that a strict temporal limit was established. Thus, only articles published between 2015 and 2024, inclusive, were retained. This temporal delimitation aims to ensure the relevance and timeliness of the data examined, while providing an overview of recent advances in the field.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 2: Paper Collection\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo build up an exhaustive corpus of scientific articles, a structured search strategy was developed. On the one hand, keywords belonging to the same thematic group \"Intra-group\", as well as intra-group combinations, were associated using the Boolean operator \u0026ldquo;OR\u0026rdquo;, in order to broaden the scope of the search and capture all relevant terminological variations. On the other hand, the main groups of keywords were defined to cover three major axes: technologies and concepts related to M4.0-I4.0 and the four dimensions of SM, namely environmental, economic, social and technological. These thematic groups \"Inter-group\" were then crossed using the Boolean operator \u0026ldquo;AND\u0026rdquo; to focus the search on relevant intersections between these domains. The two group terms are:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eGroup A: (\u0026ldquo;Industry 4.0\u0026rdquo;, \u0026ldquo;I4.0\u0026rdquo;, \u0026rdquo;Digitalization\u0026rdquo;, \u0026rdquo;Intelligent Manufacturing\u0026ldquo;, \u0026rdquo;Digital Factory\u0026ldquo;, \u0026rdquo;Smart Factory\u0026ldquo;, \u0026ldquo;Fourth Industrial Revolution\u0026rdquo;, \u0026ldquo;Smart Manufacturing\u0026rdquo;, \u0026ldquo;Industrial Automation\u0026rdquo;, \u0026ldquo;Integrated Systems\u0026rdquo;, \u0026ldquo;I4.0 Technologies\u0026rdquo;, \u0026ldquo;Cloud Manufacturing\u0026rdquo;, \u0026ldquo;Internet of Things\u0026rdquo;, \u0026ldquo;Artificial Intelligence\u0026rdquo;, \u0026ldquo;Big Data\u0026rdquo;, \u0026ldquo;Blockchain\u0026rdquo;, \u0026ldquo;Cloud Computing\u0026rdquo;, and \u0026ldquo;Cyber-Physical System\u0026rdquo;, \u0026ldquo;Augmented Reality\u0026rdquo;, \u0026ldquo;Virtual Reality\u0026rdquo;, \u0026ldquo;Digital Twin\u0026rdquo;, \u0026ldquo;Additive Manufacturing\u0026rdquo;, \u0026ldquo;Edge Computing\u0026rdquo;).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGroup B: (\u0026ldquo;Predictive Maintenance\u0026rdquo;, \u0026ldquo;PdM\u0026rdquo;, \u0026ldquo;Maintenance 4.0\u0026rdquo;, \u0026ldquo;M4.0\u0026rdquo;, \u0026ldquo;remaining useful life\u0026rdquo;, \u0026ldquo;Condition-Based Maintenance\u0026rdquo;, \u0026ldquo;Asset Management\u0026rdquo;, \u0026ldquo;Reliability-Centered Maintenance\u0026rdquo;, \u0026ldquo;Digital Maintenance\u0026rdquo;, \u0026ldquo;Maintenance Digital\u0026rdquo;).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGroup C: (\u0026ldquo;Sustainable Manufacturing\u0026rdquo;, \u0026ldquo;Sustainable Development\u0026rdquo;, \u0026ldquo;Environmental Performance\u0026rdquo;, \u0026ldquo;Economic Performance\u0026rdquo;, \u0026ldquo;Social Performance\u0026rdquo;, \u0026ldquo;Technological Performance\u0026rdquo;, \u0026ldquo;Sustainable\u0026rdquo;, \u0026ldquo;Sustainability\u0026rdquo;, \u0026ldquo;Industrial Practice\u0026rdquo;, \u0026rdquo;Durability\u0026rdquo;, \u0026ldquo;Environmental\u0026rdquo;, \u0026ldquo;Economic\u0026rdquo;, \u0026ldquo;Social\u0026rdquo;, \u0026ldquo;Technological\u0026rdquo;, \u0026ldquo;Social Sustainability\u0026rdquo;, \u0026ldquo;Green Manufacturing\u0026rdquo;, \u0026ldquo;Resource Efficiency\u0026rdquo; ).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 3: Paper Select\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe articles collected from the SCOPUS, WOS, and IEEE databases include the most peer-reviewed publications. Additionally, the three databases contain most of the main publishers, such as Springer, Taylor \u0026amp; Francis, Wiley, Elsevier, ScienceDirect, and Emerald Insights.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePhase 4: Paper Processing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the first stage, duplicates and non-journal items\u0026mdash;such as conference papers, book chapters and articles from distant fields like materials science, chemistry or pure mathematics\u0026mdash;were removed, leaving 390 peer-reviewed journal articles. The second stage involved a keyword screen: papers whose titles or keywords revolved around peripheral topics (for instance \u0026ldquo;battery\u0026rdquo;, \u0026ldquo;PV\u0026rdquo; or general \u0026ldquo;energy\u0026rdquo; issues) were excluded because they did not address our core research questions. This reduced the pool to 318 articles. During the third stage, all remaining records underwent title-and-abstract scrutiny followed by full-text reading when necessary, using an explicit two-step rubric. Articles were rejected if the full text was unavailable, if they were non-academic or misused the term sustainability, or if they mentioned I4.0\u0026ndash;M4.0 concepts only in passing. Conversely, studies that touched on at least one of the domains (Industry 4.0, maintenance or sustainability) without fully linking them were kept for contextual background, while papers that explicitly integrated the three themes formed the core evidence base. The final stage consisted of an independent, detailed appraisal by all authors; disagreements were resolved through discussion. Here, 46 additional texts were discarded for inadequate methodological detail or weak thematic alignment. Ultimately, 75 studies\u0026mdash;comprising 26 review articles and 49 empirical or conceptual investigations\u0026mdash;met every criterion and now underpin our analysis of how I4.0 technologies and M4.0 practices advance sustainable manufacturing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the process of selecting studies from various sources. Initially, studies were identified through additional queries in the IEEE (82), Scopus (43), and Web of Science (28) databases. This was followed by the extraction of 963 additional studies from the databases (IEEE: 529, Scopus: 169, Web of Science: 265), resulting in a total of 1,116 studies. After applying the exclusion criteria described above, 26 studies were retained for the review, along with 49 relevant study reports, yielding a final total of 75 included items.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Descriptive Analysis\u003c/h2\u003e\u003cp\u003eThe final portfolio of selected studies undergoes a thorough examination, incorporating both quantitative and qualitative analyses. Drawing notably on previous research, as referenced by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], we approach the data from several key dimensions to highlight specific trends and distributions. Our study therefore considers four main categories of distributions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of publications over time\u003c/b\u003e: Identifying the progression and growth of research in the context of I4.0, maintenance, and their impact on sustainable manufacturing.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of publications by journals\u003c/b\u003e: Highlighting the concentration of relevant research in specific journals to identify the main publication channels.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of publications by technologies deployed\u003c/b\u003e: Categorizing studies based on the specific I4.0 technologies and tools implemented. This analysis reveals which technologies dominate the research landscape, their application within Maintenance 4.0 frameworks, and their contributions to sustainable manufacturing practices.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of publications by research focus\u003c/b\u003e: Categorizing studies based on their objectives, such as exploratory research, theoretical development, or empirical testing.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of publications by methodology\u003c/b\u003e: Examining the methodological approaches used in the portfolio, including conceptual papers, literature reviews, empirical studies, secondary data analyses, expert interviews, surveys, and experiments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDistribution of keywords through Co‑occurrence analysis\u003c/b\u003e: illustrating the relationships between the keywords or research topics concepts through connections reflect their co-occurrence in scholarly literature.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe objective of this analysis is to identify the main themes and research areas. This step provides an overview of the relationship between I4.0 technologies and their impact on sustainability within the framework of M4.0. Within this context, I4.0 technologies, M4.0, and sustainability principles are identified, selected, and categorized. The main objectives achieved through the combination of I4.0 and M4.0 processes to achieve sustainable manufacturing are then determined based on the content of the portfolio. Through this analysis, a comprehensive understanding of the research landscape is obtained, allowing us to identify existing gaps in the field.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Descriptive Analysis","content":"\u003cp\u003eFollowing the thorough documentary analysis, 75 studies were selected. These sources form the foundation of our analysis, which aims to explore the interactions between I4.0 and M4.0 technologies, as well as their impact on sustainable practices. Upon completing the content analysis, a diagram was developed to illustrate the links between specific I4.0 technologies and M4.0, and their role in achieving sustainable manufacturing.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Distribution of publication over time\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the annual distribution of the selected publications from 2015 to 2024. A notable increase in publications emerged starting in 2020, reflecting heightened awareness and growing adoption of I4.0 principles and advanced maintenance strategies in the manufacturing sector. The number of studies rose from five publications in 2020 to a peak of 14 in 2021 and 2022, suggesting that these years were particularly productive for developing foundational knowledge, theoretical frameworks, and practical solutions. In 2024, there was a substantial surge, reaching 27 publications. This marked growth can be attributed to several factors: increasing industrial interest in these topics, stronger political support for sustainable manufacturing, and the growing maturity of enabling technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Cyber-Physical Systems (CPS). Overall, this upward trend highlights the increasing importance for the industrial sector and the manufacturing ecosystem to adopt sustainable practices supported by advanced digital technologies. The expanding body of literature serves as a clear indicator of the interest, dynamism, and evolving nature of this field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Distribution of publication by methodology\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a more granular view by categorizing the selected publications into four types: conceptual Studies, systematic reviews and critical analyses, quantitative or qualitative analyses, and empirical and case study. The evaluations indicate the diversity of research approaches employed to address the themes of I4.0 and M4.0 within the context of sustainable manufacturing. The results reveal a predominance of empirical and case studies, followed by systematic reviews, conceptual studies, and finally quantitative or qualitative analyses. Empirical and case studies have garnered the most attention, representing a significant portion of the selected publications. These studies aim to: i) Validate proposed solutions, frameworks, or models through real-world applications; ii) Provide tangible results via case studies, experiments, and prototypes; iii) Demonstrate the effectiveness of I4.0 technologies and M4.0 in improving maintenance strategies, optimizing manufacturing processes, and achieving sustainability goals. In parallel, systematic reviews and critical analyses represent a substantial proportion of the literature. These papers synthesize and evaluate the existing body of knowledge, serving three primary purposes: i) Providing a comprehensive overview of the current state of research; ii) Identifying trends, challenges, and research gaps; iii) Comparing and analyzing existing methods, models, or applications. Conceptual studies contribute by proposing theoretical frameworks or new models that lay the foundation for future research. These studies explore innovative solutions and offer strategic directions for integrating advanced technologies into maintenance practices. Finally, quantitative or qualitative analyses focus on applying analytical approaches, such as machine learning models, simulations, or optimization techniques, to address specific challenges related to predictive maintenance and sustainability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Distribution of publication by journal\u003c/h2\u003e\u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the distribution of sources for the 75 selected articles is spread across various journals, with a total of 51 different journals. The analysis reveals that the largest proportion, approximately 17%, consists of articles published in the \"Sustainability\" journal by MDPI. The \"Applied Sciences\" journal follows with 7%, highlighting its relevance to interdisciplinary and applied studies. The journal \"IEEE Access\" contributes 5%, emphasizing the technological aspects of the research. The \"Journal of Cleaner Production\" accounts for 4%, and so forth. Approximately 58% of the articles are published in various other journals, each represented by a single article. This diversification of publications highlights both the interdisciplinary nature of the research field and the significance of specialized journals in shaping discussions on I4.0 technologies, Maintenance 4.0, and three axes of sustainability. It further demonstrates the broad dissemination of research across numerous platforms, facilitated by the innovative solutions brought about by this transformation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Distribution of publication by technologies deployed\u003c/h2\u003e\u003cp\u003eThe bar chart in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the frequency of different technologies referenced in the reviewed publications between 2015 and 2024. The analysis reveals significant variations in the focus on various M4.0 and I4.0 Technologies. The industrial sector's growing interest in Industry 4.0 technologies is reflected in the different technologies used over time. Ten essential technologies have arisen, significantly influencing the current Industry 4.0 scene and fostering improvements in sustainability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Distribution of keywords through Co‑occurrence analysis\u003c/h2\u003e\u003cp\u003eThe network map, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, offers a visual depiction of the interconnections between keywords or key research topics. The keywords that are represented by larger font sizes are indicated as the most frequently used in existing publications. The network map reveals three distinct clusters, designated by red, green, and blue, respectively, that are associated with Industry 4.0, Maintenance 4.0, and Sustainable Manufacturing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the core of the network, \"Industry 4.0\", \"Predictive Maintenance\", and \"Sustainability\" appear as dominant nodes, suggesting their central role in the research landscape. Surrounding these key concepts are closely related terms such as \"Artificial Intelligence\", \"Internet of Things\" (IoT), \"Big Data\", and \"Cyber-Physical Systems\", which serve as critical enablers of sustainable manufacturing in industrial environments. The prominence of AI-related terms like \"Machine Learning\", \"Deep Learning\", and \"Neural Networks\" further indicates that intelligent algorithms are at the heart of automation, predictive analytics, and decision-making processes in modern manufacturing. The map also reveals a strong link between Industry 4.0 and Maintenance 4.0. Technologies such as Digital Twins, Edge Computing, and IoT facilitate real-time monitoring, allowing industries to transition from reactive to predictive maintenance strategies. Concepts like \"Remaining Useful Life\" (RUL), \"Condition-Based Maintenance\", and \"Asset Management\" are deeply interconnected, demonstrating a clear trend toward optimizing equipment performance and reducing downtime through smart technologies. Another significant observation is the integration of sustainability principles into industrial practices. Terms such as \"Energy Efficiency\", \"Resource Efficiency\", \"Circular Economy\", and \"Climate Change\" are connected to both Industry 4.0 and Maintenance 4.0, highlighting that these technological advances are not only about automation and operational efficiency but also aim to achieve environmental and economic sustainability. Additionally, the presence of terms like \"Green Manufacturing\" and \"Social Sustainability\" reflects a growing awareness of the importance of responsible and eco-friendly production methods.\u003c/p\u003e\u003cp\u003eThe analysis of the literature reveals an evolution and maturation of research at the intersection of I4.0, M4.0, and SM. The continually expanding body of work now includes both cutting-edge empirical studies and critical analyses, contributing to a dynamic and thoughtfully engaged research ecosystem. This analytical framework provides valuable insights for practitioners, policymakers, and researchers, enabling a comprehensive understanding of the scientific landscape and helping to identify current gaps in the literature on this subject.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Content analysis","content":"\u003cp\u003eBuilding on the descriptive statistics presented in Section 3, this section synthesizes the findings from the final portfolio of seventy-five studies through a combined quantitative\u0026ndash;qualitative lens. Following the methodological guidance of [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], we examined each paper across several dimensions\u0026mdash;publication year, research method, focal technology, maintenance function and sustainability pillar\u0026mdash;to surface dominant patterns and emerging gaps. The analysis confirms that the joint deployment of Maintenance 4.0 (M4.0) and Industry 4.0 (I4.0) technologies strengthens manufacturing capability, sharpens competitive advantage and supports all four sustainability pillars (economic, environmental, social and technological). To present these insights coherently, the discussion is organized around two interrelated themes: (i) Integration mechanisms between M4.0 and I4.0 technologies: Here we map the technical and organizational linkages\u0026mdash;such as IIoT-enabled data pipelines, digital-twin-driven diagnostics and AI-supported decision loops\u0026mdash;that embed maintenance intelligence within broader smart-factory architectures. (ii) Contribution of M4.0\u0026ndash;I4.0 integration to sustainable manufacturing: This theme explains how the identified mechanisms translate into tangible sustainability outcomes: resource-efficiency gains, waste and emission reductions, extended asset lifecycles and enhanced workforce well-being.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Integration Mechanisms between M4.0 and I4.0 Technologies\u003c/h2\u003e\u003cp\u003eThe transition from conventional maintenance paradigms to Maintenance 4.0, aligned with Industry 4.0 standards, necessitates a fundamental transformation driven by the integration of advanced technologies such as artificial intelligence (AI), the Internet of Things (IoT), and cyber-physical systems (CPS) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This evolution requires a systematic evaluation of each technology\u0026rsquo;s contribution to core M4.0 processes.\u003c/p\u003e\u003cp\u003eTo build comprehensive analytical depth, we conducted a content analysis of 75 academic publications, examining the specific usefulness and implementation modalities of these technologies in maintenance environments. The aim is to optimize maintenance operations, enhance decision-making capabilities, and maximize operational efficiency, while strategically leveraging technological advances to address current and future competitiveness challenges [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Despite significant progress, a gap remains between theoretical promises and practical industrial applications, primarily due to technical challenges (e.g., interoperability) and organizational barriers (e.g., resistance to change).\u003c/p\u003e\u003cp\u003eIn the literature, several studies [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] agree that technological progress is centered around nine fundamental pillars that have the potential to transform production systems, whether adopted individually or in combination. For instance, in the study [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], the author investigated workforce competency transformation in Industry 4.0 environments through a modified Delphi method involving 38 industry experts across twelve manufacturing sectors. The research identified nine core competency domains, with predictive analytics literacy showing the strongest correlation with maintenance efficiency (β\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The study concluded that augmented reality-based training reduced skill acquisition time by 40% compared to traditional methods, and proposed a hierarchical upskilling framework. Another study [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], mapped technology adoption trajectories in SMEs using a sequential mixed-methods approach (n\u0026thinsp;=\u0026thinsp;127 survey responses\u0026thinsp;+\u0026thinsp;12 case studies). The study quantified the maturity progression of Industry 4.0 technological pillars, finding that asynchronous adoption creates \u0026ldquo;integration debt\u0026rdquo;\u0026mdash;for example, early IoT adopters experienced 18% lower ROI than synchronized adopters (F\u0026thinsp;=\u0026thinsp;6.34, p\u0026thinsp;=\u0026thinsp;0.013). The authors emphasized that phased implementation must prioritize interoperable architectures to avoid technical fragmentation. In [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], the author conducted a five-year longitudinal action research study across three Nordic manufacturers to investigate the coevolution of technology and maintenance practices. The study revealed self-reinforcing loops between IIoT deployment and predictive maintenance maturity, leading to a 32\u0026thinsp;\u0026plusmn;\u0026thinsp;7% reduction in Mean Time to Repair (MTTR). It further demonstrated that data liquidity accelerates M4.0 adoption 2.4 times faster than isolated, technology-centric initiatives. In the paper [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the authors explored IIoT\u0026ndash;CPS interoperability through controlled experiments using industrial controllers under varying network conditions. By quantifying latency thresholds for predictive maintenance, they showed that 5G-enabled edge computing reduces diagnostic latency to under 8 ms\u0026mdash;compared to 142 ms with 4G\u0026mdash;achieving 99.2% fault detection accuracy. The study concluded that maintaining sub-10 ms latency is essential for high-urgency maintenance scenarios. In this context, the nine core technological pillars\u0026mdash;Industrial Internet of Things (IIoT), Big Data and analytics, horizontal and vertical system integration, 3D simulation, cloud computing, augmented/virtual reality (AR/VR), autonomous robots, additive manufacturing, and cybersecurity\u0026mdash;function as interdependent components within a dynamic capability framework [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. These pillars constitute the foundation of \u0026ldquo;smart factories\u0026rdquo;, characterized by advanced automation, holistic systems integration, and optimized production processes. Furthermore, they enhance efficiency, foster innovative collaboration among suppliers, manufacturers, and customers, and strengthen the human\u0026ndash;machine interface [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This analysis focuses on the full spectrum of Industry 4.0 technologies, drawing from an extensive body of literature to provide a holistic, comparative perspective on their impact. The objective is to identify synergies between technologies and assess their contributions to both industrial performance and organizational transformation.\u003c/p\u003e\u003cp\u003eTo establish a clear connection between I4.0 technologies and M4.0 processes, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a synthesis of the synergies across these two domains. It highlights the contributions of various Industry 4.0 tools\u0026mdash;including IoT, AR/VR, BDA, cloud computing, AI, RFID, M2M, 3D printing, cybersecurity, Power BI, digital twins, cobots, 5G/6G, blockchain, and CPS\u0026mdash;to the primary components of M4.0 (data acquisition, monitoring, diagnosis, prognosis, decision making, maintenance planning, process automation, and self-healing). In doing so, it underscores their collective role in optimizing maintenance strategies and enhancing industrial performance. This analysis reveals critical interactions among these technologies, with most scientific articles emphasizing the combined use of AI, IoT, and Big Data across the majority of Maintenance 4.0 dimensions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Acquisition and Monitoring\u003c/b\u003e\u0026mdash;the initial pillars of Maintenance 4.0\u0026mdash;are widely viewed as foundational to this innovative maintenance approach, relying on interconnected sensors to continuously collect, compile, and analyze key equipment parameters (e.g., temperature, vibration, pressure), thereby reflecting each machine\u0026rsquo;s real-time condition. In this context, the literature points to the Industrial Internet of Things (IIoT) concept, which adapts IoT principles for the industrial domain, thus enabling machine-to-machine (M2M) communication without human intervention [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This technology interlinks physical devices through sensors, RFID, and dedicated Internet protocols, creating an interconnected ecosystem that spans the entire production environment. According to author [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], IIoT is regarded as a core technology underlying CPS, which integrates the physical (real-world) and digital (cyberspace) realms through advanced computing, communication, and control functions. As noted by [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], CPSs enable seamless convergence between physical and virtual spheres, dissolving the boundaries between these two environments. Such systems are characterized by the close intertwinement of natural and artificial processes with digital technologies. Moreover, technologies such as blockchain and cybersecurity facilitate automatic and secure data transmission\u0026mdash;originating from RFID and M2M\u0026mdash;to central control systems. The consolidation and safeguarding of data flows constitute essential prerequisites for more advanced analyses. Subsequently, Big Data techniques become indispensable for aggregating and exploiting the massive volume of collected information. Several studies [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] have demonstrated that AI plays a pivotal role in monitoring, early fault detection, and establishing alert thresholds and standards [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. By analyzing high-dimensional data at scale and identifying non-linear correlations undetectable by humans [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], AI systems improve failure prediction accuracy by 20\u0026ndash;50%, reduce maintenance costs by 18\u0026ndash;25%, and strengthen operational resilience. These outcomes underscore AI\u0026rsquo;s transformative capacity for preventing and predicting malfunctions in Maintenance 4.0. Furthermore, the integration of IoT, Big Data, and AI paves the way for maintenance that is both proactive and reactive, aligning with contemporary demands for reliability and cost-effectiveness. In parallel, leveraging digital twins, AR/VR, and cloud computing enables a comprehensive, immersive, and interactive perspective on equipment status\u0026mdash;even when accessed remotely [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Finally, cybersecurity remains fundamental to preserving the reliability and integrity of data flows, thereby completing the broader M4.0 ecosystem. At the same time, advanced visualization tools, such as Power BI, synthesize and display information in real time, providing a solid foundation for in-depth analyses.\u003c/p\u003e\u003cp\u003eThe \u003cb\u003eDiagnosis\u003c/b\u003e and \u003cb\u003ePrognosis\u003c/b\u003e phases of Maintenance 4.0 rely on a coherent integration of advanced technologies\u0026mdash;particularly Big Data and AI\u0026mdash;which serve as key drivers in the evolution of algorithms [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. It is noteworthy that most diagnostic and prognostic solutions use data-driven models and algorithms, as they can handle terabytes of data (Big Data) from thousands of IoT devices, detecting deviations with over 90% accuracy [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], this performance surpasses traditional physics-based, expert-based, or rule-based diagnostic models [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Diagnostic processes leverage the ability to reliably analyze and interpret data collected in the previous stage from a multitude of connected sensors and RFID systems, while machine learning or deep learning algorithms identify weak signals indicative of anomalies or potential failures. Furthermore, immersive technologies such as augmented reality (AR) and virtual reality (VR)\u0026mdash;applied in approximately 20% of cases\u0026mdash;facilitate more detailed and interactive inspections, thereby improving the understanding of complex anomalies [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Prognosis, in turn, relies on advanced algorithms, chiefly deep learning, to anticipate failures and estimate each asset\u0026rsquo;s remaining useful life (RUL) by considering various factors (operating intensity, service conditions, maintenance history, etc.). In this context, interoperability supported by M2M and IoT, which constitute roughly 60\u0026ndash;70% of the architecture in connected industrial environments, enables continuous updates of predictive models [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Combined with Big Data\u0026mdash;employed in about 80% of use cases\u0026mdash;this interoperability further strengthens model robustness by consolidating extensive contextual data from heterogeneous sensors and external sources (failure histories, production conditions, spare-part inventories) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. This large-scale analytical capacity significantly improves predictive accuracy and facilitates proactive decision-making, reducing costs associated with unplanned production stoppages.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDecision-Making and Maintenance Planning\u003c/b\u003e, the subsequent Maintenance 4.0 stages, benefit from unprecedented automation and optimization through strategic integration of solutions such as IoT/IIoT, AI, digital twins, and even 5G/6G. Deployed to varying degrees across different sectors, these technologies generate tangible gains in productivity, cost reduction, and operational resilience [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. The adoption of Power BI\u0026mdash;utilized by 70\u0026ndash;80% of industrial firms\u0026mdash;plays a pivotal role in centralizing performance indicators (KPIs) and generating interactive dashboards [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. These tools enable intuitive data visualization from IoT sensors, implemented in 65% of connected factories [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], or cloud databases, cutting analysis time by 30\u0026ndash;40% [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Multicriteria algorithms, coupled with predictive AI, automate intervention prioritization by accounting for multiple factors, including asset criticality (assessed via digital twin models), intervention costs, and availability of both human and material resources. This integrated approach provides rapid insights for effective decision-making and optimal intervention planning. Concurrently, blockchain technology\u0026mdash;adopted by 10\u0026ndash;15% of firms for decision-making \u0026mdash;reinforces transparency by securing maintenance history and corporate data, thus reducing human error by 20% [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Intervention planning also depends on close coordination among human operators and autonomous systems, enabling optimized lead times and costs. Cobots (collaborative robots) and 5G/6G networks (with latency as low as 1 ms) enable instant communication among technicians, engineers, and suppliers, accelerating problem resolution by 40% [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. AR/VR usage\u0026mdash;ranging from 10\u0026ndash;20% in certain high-risk sectors\u0026mdash;allows the simulation of complex maintenance tasks before execution, significantly mitigating unforeseen issues and achieving a 99% accuracy rate while reducing error risk. AI, RFID, IoT, and M2M automate task prioritization and resource allocation, thereby streamlining process optimization [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. For example, RFID simplifies spare-part inventory management by enabling real-time tracking, ensuring the availability of necessary components. Lastly, while 3D printing remains a niche technology (not yet widely adopted), it supports local on-demand fabrication of critical parts, shortening lead times by 50\u0026ndash;70% in urgent cases [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Taken together, these technologies automate and optimize decision-making, rendering maintenance planning and management more streamlined, faster, and more precise.\u003c/p\u003e\u003cp\u003eFinally, Process Automation and Self-Healing technologies are fundamentally transforming industrial operations through three measurable shifts: (i) Autonomous responsiveness to production variables (ii) Predictive adaptability to system anomalies (iii) Closed-loop optimization of resource utilization [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Empirical evidence demonstrates that IoT/IIoT systems drive approximately 30% of efficiency gains by enabling real-time equipment monitoring and machine-to-machine coordination, facilitating instantaneous adjustments to throughput requirements [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Complementary analysis reveals that Big Data analytics combined with RFID technologies contribute an additional 25% of process optimization through granular data collection and pattern recognition, enhancing automated decision-making [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Cloud computing provides the architectural backbone for these capabilities, enabling centralized management and distributed execution of maintenance protocols across geographically dispersed facilities [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Moreover, Artificial Intelligence and cobots have been cited in recent literature as being responsible for more than 60% of the qualitative and quantitative improvements in intelligent automation systems, so they represent the most significant technologies in terms of transformation [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. These technologies allow machines to learn from data, adapt autonomously to changing conditions, and collaborate safely and efficiently with human operators in hybrid work environments. These integrated systems yield quantifiable improvements in manufacturing resilience: 41\u0026ndash;58% reduction in unplanned downtime [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] 22\u0026ndash;37% improvement in operational agility [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] 31\u0026thinsp;\u0026plusmn;\u0026thinsp;5% increase in resource utilization efficiency [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Taken together, these technologies are not only improving operational efficiency, but are also driving a transformation marked by greater agility, scalability, and self-adaptability, thereby laying the foundation for resilient and autonomous production systems [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eUltimately, the synergy across these technological building blocks not only optimizes maintenance from an operational standpoint but also reimagines industrial asset lifecycle management in a more predictive and proactive manner. As illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Maintenance 4.0\u0026mdash;through the strategic orchestration of data acquisition, diagnosis, automation, and planning\u0026mdash;creates a virtuous cycle wherein data quality and intervention responsiveness mutually reinforce one another. This evolution positions industrial maintenance as a true strategic pillar for competitiveness and profitability, fully aligned with the demands of the future industrial landscape.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eContribution of the I4.0 technologies in Maintenance 4.0 process\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003e(Sources: literature analysis)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI4.0 Technologies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData Acquisition\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonitoring\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDiagnosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrognosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDecision making\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMaintenance planning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eProcess automation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSelf-Healing\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIoT/IIoT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAR/VR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig Data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR93\" 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colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e], [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRFID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM2M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e], [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3d printing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e], [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecybersecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e], [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePower BI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e], [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDigital Twin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCobot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e], [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e], [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5G/6G\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e], [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlockchain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e], [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e0\u0026thinsp;=\u0026thinsp;Absent \u0026middot; 1\u0026thinsp;=\u0026thinsp;Emerging \u0026middot; 2\u0026thinsp;=\u0026thinsp;Moderate \u0026middot; 3\u0026thinsp;=\u0026thinsp;High \u0026middot; 4\u0026thinsp;=\u0026thinsp;Leading / Best-in-class\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Role of M4.0 and I4.0 Technologies in Sustainable Manufacturing\u003c/h2\u003e\u003cp\u003eTechnological advancements have reshaped traditional maintenance paradigms by introducing innovative tools and methods capable of meeting end users\u0026rsquo; growing demands while respecting environmental, social, economic, and technological constraints in manufacturing. From a maintenance perspective, this transformational shift significantly affects several key aspects. The author underscores the importance of Maintenance 4.0 as a lever for enhancing economic, environmental, and social sustainability within firms, while highlighting the lack of adequate indicators for measuring such impacts [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. Along similar lines, another research [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e], [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e] demonstrate the influence of maintenance services on the economic, social, environmental, and technological dimensions of sustainability. For example, in the automotive industry, PSA-Stellantis Morocco has implemented digital solutions that leverage predictive maintenance and the Industrial Internet of Things (IIoT) to reduce downtime and optimize energy consumption, thus achieving significant improvements in operational efficiency and sustainability. Furthermore, researchers such as [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] concur that Sustainable Total Productive Maintenance (STPM)\u0026mdash;which integrates sustainability, Industry 4.0 technologies, and the circular economy\u0026mdash;improves organizational performance. A notable example is Siemens, which has adopted smart and predictive maintenance (STPM) practices by integrating AI More precisely the Deep Learning algorithm into its industrial operations. By equipping its equipment with IoT sensors that monitor parameters such as temperature, vibration, and pressure in real time, Siemens collects valuable operational data. This data is analyzed by AI algorithms to detect anomalies and predict potential failures before they occur. Additionally, the use of digital twins and the MindSphere cloud platform allows Siemens to simulate and optimize machine performance virtually [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. As a result, Siemens achieved a 30% reduction in unplanned downtime, a 25% decrease in maintenance costs by avoiding unnecessary interventions, and a 20% increase in equipment lifespan, thereby reducing waste and minimizing production disruptions [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e]. Authors such as Silvestri et al. [\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e] emphasize the technological \u0026ldquo;fourth dimension\u0026rdquo;, reflecting growing recognition of how innovation is central to optimizing and advancing maintenance activities in the era of Industry 4.0. Similarly, other studies [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e] indicate that the technological dimension of sustainability is critical to the maintenance function: it relies on the ability to optimize processes and associated parameters while ensuring the system\u0026rsquo;s long-term viability and profitability. This section adopts this perspective, examining the role of M4.0\u0026ndash;I4.0 integration across the three traditional pillars of sustainability (economic, environmental, and social), while introducing a fourth, technology-focused dimension, essential for sustainable manufacturing as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The figure illustrates the synergy between M4.0 processes and I4.0 technologies, highlighting their collective impact on achieving sustainable manufacturing and global performance across four key dimensions of sustainability: environmental, economic, social and technological. Environmental sustainability is reinforced by waste management, recycling, energy efficiency and carbon emission reduction, with optimization of natural resources (air, water, soil). Economic sustainability translates into increased productivity, reduced maintenance costs and better management of indirect costs. On the social front, M4.0 processes help to improve working conditions, safety, employee training and reduce absenteeism, thus fostering a fairer, safer working environment. Finally, technological sustainability is based on the adoption of agile and flexible technologies, enabling innovation and rapid adaptation to market needs, while guaranteeing data security. Overall, this diagram highlights how the integration of M4.0 processes with I4.0 technologies supports a holistic approach to sustainable manufacturing and optimizes performance on a global scale.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Economic Sustainability\u003c/h2\u003e\u003cp\u003eAccording to [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e], the adoption of Industry 4.0 technologies plays a pivotal role in reducing the Total Cost of Ownership (TCO) of production equipment, notably through advances in predictive maintenance. By leveraging cutting-edge tools such as the Internet of Things (IoT), cloud computing (CC), Big Data \u0026amp; Analytics (BDA), and cybersecurity (CS), companies can optimize their maintenance strategies and extend the operational life of industrial equipment [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. One primary benefit of these approaches lies in the ability to detect patterns of degradation or inefficiency within machines and manufacturing processes, thus allowing failures to be anticipated before they occur [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. Continuous, intelligent equipment monitoring enables proactive intervention planning, thereby reducing the high costs typically associated with emergency repairs, unplanned downtime, and production losses. According to Gebler et al., integrating digital modeling and digital twins represents another strategic lever for sustainable manufacturing [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. By creating virtual replicas of physical equipment\u0026mdash;based on simulation and sensor data\u0026mdash;companies can test various maintenance scenarios, predict component wear, and fine-tune operating parameters [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. From an economic standpoint, these technologies promote more efficient resource allocation by preventing the premature replacement of still-functional components and extending the lifespan of industrial assets. Moreover, Big Data and Artificial Intelligence (AI) in predictive maintenance enable the identification of factors influencing productivity and energy consumption, facilitating dynamic adjustments in processes to maximize both operational and environmental efficiency [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. In addition, incorporating cybersecurity into advanced maintenance policies is critical to safeguarding the security and integrity of interconnected industrial systems. With the growing prevalence of IoT and cloud computing, data protection is paramount to avoid corruption of predictive maintenance models or the risk of cyberattacks compromising production [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Environmental Sustainability\u003c/h2\u003e\u003cp\u003eThe integration of Maintenance 4.0 (M4.0) technologies\u0026mdash;including AR/VR, 3D printing, AI, IoT, and predictive analytics\u0026mdash;is transforming industrial maintenance strategies. Such innovations enable optimized asset management, concurrently enhancing equipment reliability, worker safety, and environmental sustainability. On one hand, using AR reduces the need for extended manual interventions by providing technicians with real-time interactive instructions, thus minimizing human error and equipment downtime [\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. AR also allows direct access to technical information without resorting to physical documents, thereby eliminating paper-based instructions and decreasing the ecological footprint [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. On the other hand, 3D printing (additive manufacturing, AM) plays a pivotal role in industrial maintenance by supporting on-demand production of spare parts\u0026mdash;preventing overstock, lowering logistics costs, and cutting material waste.\u003c/p\u003e\u003cp\u003eThe author [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e] underscores that AM can generate small batches of customized components, offering an economical alternative to traditional manufacturing approaches. AM can further produce lighter, more resilient parts, reduce energy consumption and extend equipment lifespans. Additionally, AI and IoT boost maintenance efficiency through advanced predictive analytics [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e]. By embedding IoT sensors in industrial machinery, maintenance systems can detect and report anomalies in real time, thus averting costly failures and limiting waste associated with malfunctions [\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e]. This proactive approach significantly cuts energy consumption and production losses, promoting more sustainable use of industrial resources. Another key advantage of M4.0 technologies is reducing transport and logistics demands. Additive manufacturing and predictive maintenance minimize unnecessary technician travel by identifying and resolving issues remotely, thereby curbing CO₂ emissions tied to physical interventions [\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e]. Moreover, condition-based maintenance avoids prematurely replacing still-functional parts, thus optimizing equipment life cycles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Social Sustainability\u003c/h2\u003e\u003cp\u003eAccording to [\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e], the integration of M4.0 and I4.0 technologies\u0026mdash;particularly AR, AI, and IoT\u0026mdash;provides considerable benefits in terms of social sustainability. By improving the quality and efficiency of human\u0026ndash;machine interactions, M4.0 fosters a safer work environment, lowers the cognitive and physical burden on operators, and enhances human performance in maintenance tasks. One of the chief social advantages of M4.0 is the reduction of human error, which in turn translates into fewer incidents and accidents related to maintenance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Through virtual models and intelligent decision-support systems, operators receive precise, real-time guidance on required interventions. These technologies allow teams to anticipate failures, direct technicians in their tasks, and provide context-appropriate recommendations for secure and efficient interventions [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. Furthermore, M4.0 plays a crucial role in improving working conditions and reducing operational stress. Automation of repetitive or hazardous tasks, combined with digital assistance via AR headsets or connected interfaces, enables technicians to focus on more value-added, less physically demanding activities. This approach leads to greater workplace well-being and reduces absenteeism stemming from accidents or musculoskeletal disorders. Lastly, adopting M4.0-I4.0 enhances the social perception and credibility of enterprises among employees, consumers, and industry partners [\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e]. By investing in solutions that prioritize human capital, ensure optimal working conditions, and promote equality and diversity, organizations position themselves as responsible, sustainable actors. This strategy not only strengthens their employer brand but also aligns with escalating societal and environmental standards [\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4 Technological Sustainability: The Fourth Pillar\u003c/h2\u003e\u003cp\u003eBeyond the three traditional pillars of sustainability (economic, environmental, and social), the technological dimension has emerged as an indispensable fourth pillar within the M4.0\u0026ndash;I4.0 context in sustainable manufacturing. According to [\u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e], this dimension hinges on the capacity to integrate scalable, interoperable, and resilient technologies that support long-term sustainable manufacturing objectives. Furthermore, the authors highlight the ability of companies to adopt, over time, innovative solutions that ensure both the longevity of production systems and their alignment with the challenges posed by sustainable manufacturing.\u003c/p\u003e\u003cp\u003eOn the one hand, technological sustainability manifests in the agility and resilience of industrial systems. I4.0 technologies such as the Internet of Things (IoT), artificial intelligence (AI), and digital twins enable the rapid detection of emerging failures, real-time adjustment of production parameters, and highly accurate planning of interventions [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Such responsiveness not only reduces production losses but also ensures continuous process optimization, thereby maintaining performance in uncertain and evolving environments. On the other hand, technological sustainability involves robust governance and rigorous innovation management. According to [\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e], the M4.0\u0026ndash;I4.0 approach extends beyond merely deploying tools (e.g., sensors, collaborative robots, digital twins): it also requires implementing cybersecurity protocols, adopting standards (interoperability, normalization), and providing ongoing training for operators. This organizational and managerial dimension is crucial to preserving the benefits of advanced technologies and avoiding overreliance on insufficiently mastered solutions.\u003c/p\u003e\u003cp\u003eIn addition, the technological dimension exerts a multiplier effect on the three other pillars of sustainability. For instance, proactive predictive maintenance contributes directly to economic efficiency (reduced downtime), environmental conservation (lower waste and excessive consumption), and social well-being (enhanced operator safety). The Big Data generated by maintenance systems also improves strategic decision-making, fosters traceability and transparency, and thus strengthens stakeholder trust (operators, customers, investors). Finally, technological sustainability thrives on a broader ecosystem that encourages co-creation and open innovation. Collaborations between companies, research laboratories, and startups specializing in AI or robotics accelerate continuous improvement in maintenance. This collaborative approach anchors I4.0 technologies more firmly in industrial reality, producing more robust solutions that are better suited to the challenges of sustainable manufacturing [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Ultimately, the coherent integration of technology, management, and workforce training stands out as a pivotal lever for achieving sustainability goals in future industries.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e provides a concise overview of the key priorities and challenges facing industrial systems, organized into four dimensions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEnvironmental: Energy consumption is the leading concern (93%), followed by waste management (53%) and CO₂ emissions (40%).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEconomic: Cost (47%) and quality (40%) remain paramount, whereas inventory reduction Health and safety (33%) continue to be a top priority, superseding working conditions (13%) and training (2%).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTechnological: Equipment reliability (40%) and interoperability (33%) are critical, while digital skills (27%) and data protection (27%) persist as significant concerns.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOur analysis reveals that (38%) of selected studies focus primarily on environmental sustainability, followed by economic (29%), technological (22%), and social (15%) dimensions. The predominance of environmental studies highlights the industry\u0026rsquo;s increasing emphasis on reducing carbon footprints through predictive maintenance and digital twins. Notably, the manufacturing and automotive sectors contribute 47% of studies, indicating early adoption of I4.0-driven sustainability practices. Collectively, these findings underscore the urgency of optimizing energy efficiency and bolstering technical resilience to address environmental and economic imperatives, while simultaneously ensuring worker well-being.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eFirst, this part addresses the drivers and obstacles of the M4.0-I4.0 integration for the change to attain sustainable manufacturing. After that, a theoretical framework is given together with ideas for more investigation.\u003c/p\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Drivers and barriers of M4.0-I4.0 in achieving sustainable manufacturing\u003c/h2\u003e\u003cp\u003eThe integration of M4.0 and I4.0 technologies offers immense potential for promoting sustainability in the manufacturing sector. By merging digitization, automation, and artificial intelligence, companies can significantly optimize production processes, reduce waste, and improve resource efficiency [\u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e]. When these technologies are aligned with SM principles, the resulting synergy enhances environmental performance, streamlines operations, and enables real-time decision-making [\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e],[\u003cspan citationid=\"CR148\" class=\"CitationRef\"\u003e148\u003c/span\u003e]. Furthermore, collaborative data-sharing practices foster transparency and global coordination among stakeholders [\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e]. Advanced tools such as digital twins\u0026mdash;virtual representations of physical systems built from real-time sensor data\u0026mdash;enable continuous improvement and better-informed decisions throughout the asset lifecycle [\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e]. In parallel, predictive analytics powered by big data can convert vast datasets into actionable insights, contributing to proactive maintenance strategies and economic gains [\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e]. To sustain these advancements, however, ongoing workforce development becomes imperative. Employees must acquire not only technical skills but also a digital mindset to operate and maintain complex, interconnected systems. Without adequate training, companies risk underutilizing these technologies and facing organizational pushback. Despite the clear advantages of the integration of M4.0-I4.0, its implementation faces numerous practical challenges. Organizational change\u0026mdash;especially in industrial environments\u0026mdash;is often met with substantial resistance. As highlighted in [\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e], more than 70% of change initiatives fail, largely due to inadequate engagement with operational processes, disregard for employee values, weak leadership, and inaccurate resource planning. These issues are particularly relevant in the context of adopting advanced digital technologies, where change affects not only tools and systems but also corporate culture and employee behavior.\u003c/p\u003e\u003cp\u003eA prominent obstacle is the lack of workforce readiness. Many technicians and operators lack the necessary Information and Communication Technology (ICT) skills tailored to maintenance-specific applications [\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e]. This deficiency creates a chain of barriers\u0026mdash;technological, organizational, and behavioral\u0026mdash;amplified by insufficient training infrastructures. When organizations fail to invest in capacity building, employees may struggle to adopt new systems or even reject them altogether. Such resistance is often driven by job security concerns and fear of redundancy, which are deeply rooted in cultural and psychological dimensions. Employees may perceive digitalization as a threat rather than an opportunity, especially when communication around change is unclear or top-down [\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e]. This contributes to a broader category of managerial barriers, where a misalignment between strategic vision and employee perception hinders adoption. On the technical front, the lack of standardized data formats and protocols leads to interoperability issues that obstruct seamless integration across departments [\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e]. Moreover, organizations express concerns about cybersecurity\u0026mdash;a growing risk given the reliance on cloud infrastructure and real-time data exchange [\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e]. Breaches of sensitive operational or maintenance data can result in severe economic and reputational damage. Additionally, financial constraints represent a considerable hurdle. The acquisition of smart sensors, AI platforms, and edge computing systems often demands substantial upfront investment, which many companies\u0026mdash;especially SMEs\u0026mdash;are unable or unwilling to make. Limited access to infrastructure and cutting-edge technologies further exacerbates these difficulties, particularly in regions lacking digital maturity [\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo overcome these multifaceted barriers, a coordinated effort among key stakeholders is vital. Governments and regulatory authorities play a central role in shaping enabling environments through clear guidelines, financial incentives, and sustainability mandates [\u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e]. Universities and research institutions contribute by developing innovative models and offering training programs that prepare the workforce for digital transitions [\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e]. At the same time, investors and financial institutions must support innovation with tailored financing instruments that reduce the financial burden of early adoption. Ultimately, the successful integration of I4.0 technologies within sustainable manufacturing frameworks depends on more than just technical capability\u0026mdash;it requires cultural transformation, stakeholder alignment, and long-term strategic commitment. Addressing both the enablers and the barriers holistically will be key to unlocking the full potential of the integration of M4.0-I4.0.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Theoretical Framework Development\u003c/h2\u003e\u003cp\u003eConceptual frameworks are developed by integrating core concepts central to a research phenomenon\u0026mdash;in this case, the integration between Maintenance 4.0 (M4.0) and Industry 4.0 (I4.0) in achieving Sustainable Manufacturing (SM)\u0026mdash;to systematically describe, explain, and contextualize complex interactions within the research domain [\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e], [\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e]. These frameworks synthesize fragmented knowledge into a cohesive structure, providing a holistic \"bigger picture\" that guides empirical and theoretical inquiry.\u003c/p\u003e\u003cp\u003eBuilding on insights from the literature review, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e presents a conceptual framework that maps the synergies M4.0-I4.0, highlighting their collective role in advancing SM. Despite growing scholarly interest, the field remains nascent, with only 75 publications identified to date. This underscores the need to transcend superficial analyses of management practices and adopt a systemic perspective.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe proposed framework addresses this gap by anchoring itself in the foundational principles of M4.0 and I4.0 technologies. On one hand, I4.0 enables Automation, connectivity, and data-driven optimization through real-time monitoring, predictive analytics, and cyber-physical system integration. On other hand, M4.0 emphasizes proactive maintenance, asset lifecycle management, and resilience via advanced diagnostics, self-healing systems, and predictive strategies. For example, IoT-enabled sensors generate granular machine health data, which AI-driven models analyze to preempt failures and optimize energy consumption [\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e]. This symbiosis enhances operational performance while advancing environmental sustainability through reduced emissions and material efficiency. However, scalable implementation faces barriers such as workforce skill gaps, cybersecurity vulnerabilities, and a lack of standardized interoperability protocols. These challenges necessitate deeper empirical investigation into adaptive training frameworks and secure, interoperable architectures [\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e]. This diagram illustrates how I4.0 technologies and M4.0 contribute to Sustainable Manufacturing by acting on four key dimensions: economic, social, environmental and technological. Economically, these technologies improve quality, tool performance, efficiency and flexibility, while reducing inventories and risks. Socially, they promote health, safety and working conditions, as well as skills development and employee commitment. Environmentally, they enable optimized use of energy and materials, reduce CO2 emissions and support circular economic practices. Technologically, they ensure reliability, security, digital innovation and system scalability. I4.0 technologies (such as IoT, AI, Big Data, digital twins, etc.) and the processes associated with M4.0 (such as data acquisition, diagnostics, decision-making and maintenance planning) create an essential synergy for achieving these objectives, although they face certain barriers that can slow down their adoption. It also emphasizes the role of institutional dynamics\u0026mdash;legal frameworks, cultural norms, and organizational ethics\u0026mdash;in shaping ethical decision-making within M4.0-I4.0 integration [\u003cspan citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e]. For instance, stringent environmental regulations may accelerate SM adoption, while organizational culture influences stakeholder engagement. By aligning technological capabilities with institutional priorities, companies can foster ethical practices and optimize cost structures, material flows, and waste reduction [\u003cspan citationid=\"CR164\" class=\"CitationRef\"\u003e164\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis process is dynamic, enabling businesses to leverage M4.0-I4.0 for SM through iterative knowledge-building and skill integration. Enhanced information networks and analytical capabilities allow firms to mitigate environmental impacts via machine life-cycle management [\u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e165\u003c/span\u003e], [\u003cspan citationid=\"CR166\" class=\"CitationRef\"\u003e166\u003c/span\u003e]. However, progress is iterative, requiring continuous refinement of knowledge bases and resolution of vulnerabilities in cutting-edge technologies, smart devices, and data analytics systems. Finally, integrating I4.0 and M4.0 into operational practices enhances performance through reflexive control of processes, such as cost optimization and material availability, while addressing sustainability challenges like labor arbitrage and waste control [\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e]. Cross-sector collaboration among policymakers, educators, practitioners, and nonprofits is critical to institutionalize SM in policies and management practices, fostering knowledge sharing and systemic alignment with sustainability goals [\u003cspan citationid=\"CR167\" class=\"CitationRef\"\u003e167\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Implications and Future Research Directions\u003c/h2\u003e\u003cp\u003eThe findings of this study reveal several promising avenues for further investigation into the integration of M4.0-I4.0 in achieving sustainable manufacturing outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResearch implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe convergence of M4.0 and I4.0 technologies opens new avenues for enhancing organizational sustainability and resilience, yet the current body of literature indicates room for deeper empirical inquiry. Existing studies predominantly explore the individual impacts of M4.0 and I4.0 solutions rather than their combined effect. There is, therefore, a pressing need for multi-method and longitudinal research designs to capture the breadth of benefits, challenges, and best practices. Quantitative inquiries could measure improvements in key performance indicators (e.g., downtime, waste reduction, cost efficiency), while qualitative approaches\u0026mdash;such as case studies or action research\u0026mdash;may illuminate nuanced organizational factors like cultural readiness and skill development. By integrating these insights, future scholarship can better establish a robust theoretical basis for the integration of M4.0\u0026ndash;I4.0 in sustainable manufacturing. Additionally, researchers may focus on the contextual heterogeneity of implementing these advanced systems. For instance, sector-specific studies could explore how Small-to-Medium Enterprises (SMEs) differ from large multinationals in terms of technology adoption, supply-chain collaboration, and financial constraints. Such comparative perspectives would help isolate enablers and barriers unique to different organizational scales or industrial domains, thereby offering clearer pathways for generalization and theory building.\u003c/p\u003e\u003cp\u003e\u003cb\u003eManagerial implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom a managerial standpoint, strategic alignment emerges as a critical success factor. Integrating M4.0\u0026ndash;I4.0 into existing operations calls for a coherent vision, ensuring that predictive maintenance and durability principles reinforce, rather than compete with, core business objectives. Managers must also prioritize upskilling the workforce; as advanced analytics, digital twins, and AI-based prognostics gain traction, organizations require employees adept at interpreting large data sets, collaborating across departments, and driving continuous improvement. This focus on people-centered strategies is equally essential to build internal champions who can advocate for technology investments and process innovations. Moreover, change management cannot be overlooked. Implementing M4.0\u0026ndash;I4.0 solutions typically entails reconfiguring operational workflows and reevaluating established performance metrics. Transparent communication and inclusive planning are thus paramount for easing potential resistance to change. Managers who actively involve technicians, engineers, and frontline operators in decision-making processes often report higher acceptance rates and better long-term outcomes.\u003c/p\u003e\u003cp\u003e\u003cb\u003ePolicy implications\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePolicy interventions play a pivotal role in accelerating adoption of M4.0\u0026ndash;I4.0 for sustainable manufacturing. First, regulatory bodies can promote standardization by defining interoperability protocols and data-exchange guidelines, minimizing fragmentation among technology providers. Such policies would reduce complexity for firms seeking to adopt integrated solutions and foster broader, more equitable participation in advanced manufacturing ecosystems. Second, financial and educational incentives can further catalyze technological uptake. Governments might provide tax credits or grants for organizations that invest in predictive maintenance tools, energy-efficient equipment, or circular business models. Similarly, public\u0026ndash;private partnerships could sponsor specialized training programs that equip workers with the digital competencies essential for operating, maintaining, and refining smart production systems. This approach not only elevates human capital but also helps align national workforce capabilities with evolving industry requirements. Finally, international collaboration holds promise for coordinating best practices around data security and carbon footprint reduction. As global supply chains become increasingly interdependent, policymakers may consider cross-border agreements to enable secure data transfer, consistent standards, and mutual recognition of certification schemes, ultimately facilitating a cohesive shift toward sustainable, technology-driven industrial processes worldwide.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eActionable Strategies for integration of M4.0 and I4.0 in SM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFocus Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrategy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLinked Finding\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eApplication in the real life\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorkforce Development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUpskill staff in AI/ML tools and AR/VR maintenance training\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58% of studies cite skill gaps as a barrier [\u003cspan citationid=\"CR168\" class=\"CitationRef\"\u003e168\u003c/span\u003e], [\u003cspan citationid=\"CR169\" class=\"CitationRef\"\u003e169\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePartner with platforms like Coursera or Udacity to develop AR-based maintenance training modules.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTechnology Adoption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrioritize IoT\u0026thinsp;+\u0026thinsp;Digital Twin integration for predictive maintenance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIoT-digital twin synergy reduces energy consumption by 15% [\u003cspan citationid=\"CR170\" class=\"CitationRef\"\u003e170\u003c/span\u003e], [\u003cspan citationid=\"CR171\" class=\"CitationRef\"\u003e171\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeploy Siemens Mind Sphere for real-time asset monitoring and failure prediction.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolicy Incentives\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubsidize blockchain adoption for transparent maintenance logs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlockchain reduces human errors by 20% and enhances auditability [\u003cspan citationid=\"CR172\" class=\"CitationRef\"\u003e172\u003c/span\u003e], [\u003cspan citationid=\"CR173\" class=\"CitationRef\"\u003e173\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAlign with EU\u0026rsquo;s Digital Decade 2030 targets for secure, traceable supply chains.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCybersecurity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImplement ISO/IEC 27001 standards for IIoT systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e65% of studies identify cybersecurity as a critical barrier [\u003cspan citationid=\"CR174\" class=\"CitationRef\"\u003e174\u003c/span\u003e], [\u003cspan citationid=\"CR175\" class=\"CitationRef\"\u003e175\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdopt zero-trust architecture frameworks for M2M communication in smart factories.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCircular Economy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIncentivize 3D printing for on-demand spare parts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAdditive manufacturing reduces material waste by 30% [\u003cspan citationid=\"CR176\" class=\"CitationRef\"\u003e176\u003c/span\u003e], [\u003cspan citationid=\"CR177\" class=\"CitationRef\"\u003e177\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCollaborate with local 3D printing hubs to produce certified, low-carbon components.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFuture research must prioritize resolving these challenges while fostering cross-sector collaboration to fully realize the potential of M4.0-I4.0 integration in achieving a sustainable manufacturing ecosystem. By aligning M4.0 and I4.0 with SM objectives, the framework establishes a cohesive pathway to harmonize industrial performance with environmental stewardship, social responsibility, economic viability and technological integration, thereby laying a foundation for sustainable industrial growth.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis review shows that the integration of M4.0 and I4.0 presents a great possibility to convert present manufacturing paradigms into more robust, efficient, and environmentally friendly systems. Building on cutting-edge digital enablers such as IoT, artificial intelligence, Big Data, and digital twins, M4.0 improves maintenance operations by lowering unexpected downtime, boosting resource use, and extending asset life cycles. Meanwhile, I4.0 promotes data-driven decision-making, smart automation, and real-time communication all over the manufacturing process. By addressing economic, environmental, social, and technological aspects at once, the convergence of these technologies underlies holistic sustainability. The study draws attention to various flaws in theory and practice even if the clear advantages abound. There is yet little empirical study on M4.0\u0026ndash;I4.0 synergy; most of it concentrates on either isolated technologies or particular industry settings, therefore limiting generalizability. Furthermore, underlining the complexity of large-scale implementation are organizational and behavioral challenges spanning from personnel upskilling to cultural readiness. These difficulties require multidisciplinary approaches that span engineering, management, and social sciences to provide comprehensive solutions that balance creative maintenance techniques with organizational capacity-building. Importantly, this review distills an evidence-based implementation roadmap and a maturity matrix; a field pilot currently under way at ABC company is validating these tools, and its results will be reported separately.\u003c/p\u003e\u003cp\u003eFuture research might benefit from longitudinal studies tracking the long-term effects on operational performance, environmental impact, and workforce well-being of predictive maintenance and smart industrial technologies. Comparative studies between sectors and geographical areas are equally important since they help to better grasp contextual elements that either support or hinder the acceptance of improved maintenance. At the legislative level, standardization of data protocols and cybersecurity frameworks could speed up the deployment of M4.0\u0026ndash;I4.0 solutions, while incentives for sustainable manufacturing and carbon reduction may stimulate greater participation. In addition, further inquiry into Industry 5.0\u0026mdash;the emerging paradigm that emphasizes human-centric, resilient, and sustainable manufacturing\u0026mdash;could prove highly valuable. While Industry 4.0 has focused on digitization, automation, and data-driven optimization, Industry 5.0 introduces a deeper integration of human insight, well-being, and creativity into advanced technological ecosystems. Future studies could explore how Maintenance 4.0 and Industry 4.0 solutions might evolve within an Industry 5.0 framework, particularly in areas such as co-bots (collaborative robots), adaptive AI-driven maintenance, and hyper-personalized human\u0026ndash;machine interfaces. This exploration may reveal new strategies for fostering inclusive workforce development, ethical AI deployment, and enhanced resilience against unexpected disruptions\u0026mdash;ultimately expanding the scope of sustainable manufacturing beyond operational efficiency to encompass social responsibility and long-term societal benefits.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eI4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eIndustry 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eM4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eMaintenance 4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eSustainable Manufacturing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eLinear Economy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eCircular Economy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eIoT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eInternet of Things\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eIIoT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eIndustrial Internet of Things\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eCyber-Physical Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eBDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eBig Data Analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eAR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eAugmented Reality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eVR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eVirtual Reality\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eRFID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eRadio Frequency Identification\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eCloud Computing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eCybersecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eTCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eTotal Cost of Ownership\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 38.3392%;\"\u003e\n \u003cp\u003eICT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61.6608%;\"\u003e\n \u003cp\u003eInformation and Communication Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003eCode availability: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsent to participate: Not applicable. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All co-authors have been duly acknowledged, and their consent for submission has been obtained.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eP. 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Ko\u0026ccedil;, \u0026laquo; Additive manufacturing for sustainability and circular economy: needs, challenges, and opportunities for 3D printing of recycled polymeric waste \u0026raquo;, \u003cem\u003eMaterials Today Sustainability\u003c/em\u003e, vol. 24, p. 100529, d\u0026eacute;c. 2023, doi: 10.1016/j.mtsust.2023.100529.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mohammed 1st University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Industry 4.0, I4.0 Technologies, Maintenance 4.0, Sustainable Manufacturing, Systematic Literature Review","lastPublishedDoi":"10.21203/rs.3.rs-7184682/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7184682/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe integration of Industry 4.0 (I4.0) technologies with Maintenance 4.0 (M4.0) practices holds strong potential for advancing sustainable manufacturing (SM). While these technologies promise improvements in resource efficiency, waste reduction, and alignment with sustainability objectives, research on their synergistic implementation remains limited. This study addresses this gap through a Systematic Literature Review (SLR) of 75 peer-reviewed papers published between 2015 and 2024, conducted in accordance with PRISMA guidelines. The review explores how I4.0\u0026ndash;M4.0 synergy contributes to sustainability across four interconnected dimensions: economic, environmental, social, and technological. Findings show that integrating technologies such as the Internet of Things and Artificial Intelligence into maintenance operations can reduce downtime by 20\u0026ndash;50% and enhance efficiency and system resilience by 10\u0026ndash;25%, particularly in industries like automotive and aerospace. Digital twin technologies extend equipment lifespan by 10\u0026ndash;25%, thereby deferring capital expenditures. Furthermore, blockchain and augmented reality improve operational transparency by 30\u0026ndash;40%, while big data analytics and cyber-physical systems contribute to energy savings of 12\u0026ndash;18% and reduce material waste by 20\u0026ndash;25% through real-time quality monitoring. Despite these benefits, several challenges hinder integration, including technical barriers (e.g., legacy systems, cybersecurity risks), organizational resistance (e.g., high costs, cultural inertia), and human-related issues (e.g., skills shortages, workforce restructuring). To address these barriers, the paper proposes a holistic architecture that aligns I4.0\u0026ndash;M4.0 integration with sustainability goals, bridging technological innovation with responsible resource management. This framework offers actionable insights for stakeholders, policymakers, and industry leaders aiming to foster resilient, efficient, and socially responsible manufacturing ecosystems.\u003c/p\u003e","manuscriptTitle":"Integrating Industry 4.0 Technologies and Maintenance 4.0 for Sustainable Manufacturing: A Systematic Literature Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 04:23:11","doi":"10.21203/rs.3.rs-7184682/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c329bdb4-f2cf-49d2-a5e0-bca7f899d32a","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51940286,"name":"Industrial Engineering"}],"tags":[],"updatedAt":"2025-07-23T04:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 04:23:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7184682","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7184682","identity":"rs-7184682","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}