Conventional maintenance practices have primarily focused on reactive repairs and replacements, often leading to inefficient resource utilisation and environmental degradation. However, the paradigm is shifting towards predictive, energy-based ^[1], and sustainable maintenance (SM) practices ^[2]. These practices encompass strategies that neglect the sole aim of prolonging the lifespan for the sake of profitability, instead the environmental and social priorities are at the forefront now ^[3]. Integrating sustainability principles into maintenance practice ^[4], the expected outcome considers minimised negative environmental impacts, reduced resource consumption, and enhanced resilience of physical assets.

Rhetorically, SM goes beyond the traditional understanding of maintenance as a mere technical activity of condition monitoring, maintenance activities and planning ^[5]. It recognizes the interconnectedness between human activities, asset management and the environment, placing the importance on sustainability challenges ^[6]. Such a holistic approach provides (1) a shift in the traditional perception of maintenance as a “necessary evil” to a “value-added activity” ^[7]; (2) a transition from a short-term financial benefit to a long-term perspective; and (3) a better understanding of the life cycle impacts of maintenance activities ^[8]. With the adoption of SM practices, one potentially contributes to the realisation of Sustainable Development Goals (SDG) ^[9]—imposed by the European Commission, promotes circular economy (CE) principles ^[10], and enhances social equity ^[11], which are all dimensions that SM tries to encapsulate.

Finally, SM yields economic benefits in the long run. Namely, by investing in advanced maintenance technologies, organizations can avoid costly repairs and replacements, minimise downtime, and improve overall operational efficiency ^[12]. Additionally, SM practices can create new job opportunities in energy efficiency, renewable energy, and sustainable materials sectors. Therefore, understanding and implementing SM strategies is essential for preserving the environment and achieving global sustainability goals but also for promoting economic growth and resilience ^[13].

The rise in systematic (or standalone) reviews on sustainable maintenance suggests the following. Firstly, the growth in the number of SM publications characterises scientific discourse on industrial maintenance, which has been stimulated by imposed environmental actions (e.g., Millennium Development Goals—MDGs) and initiatives (e.g., Green Deal, RePowerEU). The former, adopted by the UN during the Millennium Summit 2000, marked a historic mobilisation to achieve a set of important social and environmental priorities worldwide ^[14] and was later succeeded by the SDGs in 2015 ^[9], consequently affecting the adoption of SM conceptual propositions. Secondly, the shift from traditional maintenance practices towards sustainable ones by relying on sustainability indicators can be a valid point for maintenance evolution ^[5] by incorporating more than an economic dimension in maintenance decision-making. The bibliometric analysis encompassing reviews from 2015 to 2023 suggested the following. The growth in reviews (22.28%), averaging 4.84 citations per document year, suggests that indeed SM offers a huge potential for the upcoming Industry 5.0; however many challenges and barriers are encountered both in literature and in practice. Although extensive SLRs are conducted, from the methodological side, the reviews may be prone to bias. Namely, out of all reviews gathered, only 30% (6/20) used an explicit protocol (e.g., PRISMA), while only 10% (2/20) used an explicit QAT tool. In that sense, the validity and reliability of such studies can be considered low and questionable in terms of reproducibility. From the co-occurrence analysis of authors and sources, the publications by Franciosi, S. and Jasiulewicz-Kaczmarek M., can be considered leaders in the domain of SM, while the Journal of Cleaner Production and Sustainability, as the literature sources with the most publications, vouch for the reliability of SM results. Moreover, in the existing reviews, no primary studies were conducted that specifically assessed or compared SM strategies to traditional ones. Based on the prior evidence, only one study ^[15] contrasted EBM to traditional maintenance practice(s), resulting in poorer results, suggesting that SM practice is still in its infancy and on a theoretical basis, thus lacking substance behind arguments of benefits authors claim it provides.

3. Thematic and Conceptual Analysis

The thematic evolution analysis suggests that most of the research is on a theoretical basis (up to 2021), which considered a discussion on the suitability of and relationships between sustainability concepts (e.g., CE) and industrial maintenance. Later on, the studies started questioning the role of sustainable maintenance as a standalone function ^[16], instead of just a supporting activity for the sake of sustainability ^[17]. This was also semi-supported by the thematic evolution maps, where before 2021, emerging themes were circling economic, social and environmental dimensions and slowly adopting questions regarding barriers, challenges and performance indicators. The post-2021 mapping did not provide enough information, except in the generic terms “systematic”, “sustainability”, “production”, “manufacturing”, etc.; thus, the findings from the unigram mappings in the post-2021 split remained inconclusive. Extending the thematic analysis by abstract bigrams, the results provided insight into the motor themes of “future research”, “sustainable maintenance”, “asset management”, and “maintenance impacts”, while emerging/declining themes covered “sustainable goals”, “social aspect”, “economic dimensions” and “maintenance perspectives”, leaving the impression that although there is a huge amount of solution space to be explored, the fact that there is most research on a theoretical basis, leaves the impression that SM topic is not moving forward. Furthermore, the MCA analysis (with abstract bigrams) shows that 16/19 abstract items cluster together (shared inertia), while reviews by Franciosi, C. et al. ^[17], de Campos et al. ^[18], and Santos, A. et al. ^[19] show the highest inertia from the centre of gravity. In contrast, the study by Pires, S. ^[20] did not associate with any cluster. Primarily, the study by Pires, S. ^[20] is also questionable in terms of suitability within the proposed domain, since it mostly investigates the roles of interoperability, ontology and semantics for enabling applicability and transition of sustainability in MDM. This perhaps can be considered a major setback that resulted in a poor transition towards the sustainable approach simply because technological challenges were poorly covered and addressed before delving into sustainable dimensions. This was later recognised in the review conducted by Karki, B. and Porras, J. ^[21] acknowledging the importance of digitalization for sustainable maintenance activities. The work by Franciosi, C. et al. ^[17] motivated many to engage in standalone reviews on the topic of sustainable maintenance. The MCA cluster depicting the work ^[17], can be considered the first one questioning the role of maintenance in sustainable manufacturing with a particular focus on I4.0 and associated enabling technologies. More so, the study provoked many scientists, outside of the maintenance domain, to engage in debates regarding the importance of sustainable maintenance performance indicators (SMPIs) considering their role in minimizing the environmental economic impact, and social impact considering the improvement of companies' image. Although engaging in SMPIs is attractive to manufacturing and service companies, the role of enabling technologies (e.g., big data, augmented reality) in the adoption of SM performance metrics was still unexplored. Later work by de Campos and Simon ^[18] pointed out that lack of knowledge of the subject matter acts as a barrier and a challenge for the implementation of SM practices. Hence, they provided an understanding of how exactly sustainability concepts are being inserted into maintenance strategies by separately examining sustainability, maintenance strategies and sustainable maintenance. The emphasis was placed on the benefits both internally and externally for companies willing to adopt SM practices. Next, from their analysis of 24 selected articles, costs associated with environmental and economic indicators were the most discussed ones. Most importantly, they managed to identify the lack of methods that use technologies (e.g., big data, IoT, CPS) for collecting and storing data regarding the environmental and social impacts. Lastly, the literature review performed by Santos, A. et al. ^[19] identified the research gaps in maintenance policies within the context of reuse and remanufacturing. Contrary to the previous similar research by Acerbi, F. et al. ^[22], the analysis of 53 papers identified a lack of models dealing with EoL processes, a lack of case studies on reuse and remanufacturing, a lack of established frameworks characterising environmental and social costs, and a lack of multicriteria maintenance decision-making methods for alternative business strategies. What is more interesting is that in terms of sustainability impacts considering TBL indicators—economic, environmental and social, the environmental dimension was rarely addressed within this context, while the social dimension was not addressed at all. This was also in contrast with other systematic reviews on the topic of SM, albeit not specifically narrowed to the reuse and remanufacturing strategies.

4. Sustainable Maintenance Criteria Synthesised from Reviews

The umbrella review enabled people to extract factors that previous SM reviews highlighted. However, the extent to which these factors were addressed in the literature was quite limited. Most of the existing reviews were concerned with sustainable indicators, which is contradictory to the fact that relatively few or no primary studies have been performed in the exploratory sense to test whether such indicators can be considered applicable performance metrics. Not for these reasons alone but also to gain insights into the challenges and barriers that SM is facing, researchers extract an extensive number of sustainability factors (environmental, social and economic) in addition to technical and technological factors. Such insights were discussed, and several inferences were made to enable the transition from traditional industrial maintenance towards sustainable maintenance practices. The list comprised 87 criteria, where some were overlapping with others or addressed under different semantics. Even so, in cases of disagreement, the factors were merged to reach a consensus between the authors. The final list comprised 43 criteria. It should be noted that the identified factors are not to be used as indicators, but are rather items identified in the literature as potential research gaps, concerns and/or challenges that prevent or impede the adoption of SM practices. Although the list is extensive, researchers briefly discuss them in the following. The majority identified environmental pollution ^{[17][18][23][24]}, energy consumption ^{[17][23][24][25]}, non-renewable consumption ^{[5][18][23][25]}, maintenance costs ^[5][23][24], energy consumption costs ^[5][18][24], and health and safety of workers ^{[17][18][23][26][27]} as major concerns addressed in the existing reviews. From the technical standpoint, the RAM factors ^[23][24], productivity ^[23][24] and product quality ^[16][23] were the factors mostly considered. Finally, technological factors were the factors addressed the least within the domain of SM research. Specifically, data management and data analytics ^[26] were mostly discussed, followed by information management, data integration and knowledge management ^[20][26].

5. Bayesian Network Analysis

From the BNA-GCGM, including both overall and split samples, the interpretation of the results was made solely on centrality indices of nodes (variables). Namely, the spring (with repulsion) is not used but rather a circle network structure to check the association between different items in the network, i.e., to see the body of knowledge on the criteria researched within the literature. The betweenness centrality measures the extent to which a node lies in the path between other nodes that can represent influence in facilitating the communication or interaction between different parts of the network, which in this case was hard to interpret due to the network structure. Also, the closeness centrality shows how close a node is to the other nodes in the network for disseminating information; this also did not provide much information since the circular network structure was used. However, the strength centrality provided valuable information since it measures the total weight of edges connected to a node. Finally, an expected influence centrality is a bit more complex and specific to a network because it indicates the expected, i.e., average influence a node has on the other nodes in the network. A node having a high expected influence centrality suggests that it has a significant impact on the state of the network or other nodes in the network, which in this case was used for discussion but elaborated with caution since BNA-GCGM interpretation is limited in the literature. In the context of the analysis, the interpretation of the strength centrality refers to how prominently and frequently the specific criterion appears or is discussed across the reviews. A high strength centrality indicates a major concern or focus, ultimately indicating the importance of a particular criterion in the collective landscape. The expected influence centrality can be interpreted as the degree to which a particular criterion (node) influences the conclusion drawn from an overall UR. Unlike strength centrality, it takes the directionality (i.e., edge weight negative or positive) into account. A high influence score suggests that this criterion (node) potentially shapes the research outcome, i.e., acts as a driver or determinant for another criterion being investigated. Considering the strength centrality, it is common that strength is a non-negative metric, considering that it represents the absolute sum of the edge weights connected to a node. Therefore, the results obtained from JASP were unusual. One of the underlying reasons why some centrality indices of nodes have a high negative strength is presumably due to a negative partial correlation matrix and the fact that weights are summed without considering the absolute values. Within such context, the extract weighted the sum of the three most positive and negative nodes. The highest negative strength centralities were lifecycle_and_operational_costs (−1.303), other_env_pollution (−1.147), energy_consumption (−0.696), information_management (−0.672), and human-related_accidents (−0.670). The highest positive strength centralities were maintenance_costs (0.959), product_quality (0.856), process_management (0.752), health_and_safety_of_workers (0.680), and labor_costs (0.656). The negative strength centrality indicates that in cases where these variables are discussed and emphasised, there is a higher probability that other factors are less likely to be included. The least influential nodes in the network were data_analytics (−1.684), maintainability (−1.553), maintenance_waste (−1.369), interoperability (−1.118), and stakeholder_safety (−0.925). This, in turn, suggested that variation and changes in these nodes did not affect other nodes or the structure of the network. Moreover, the theoretical probability was that if a review discusses these nodes, the changes in other criteria (nodes) would be significantly small. This adds to the fact that even if the criteria is discussed throughout reviews, it will not increase the probability that other criteria (nodes) will appear or elevate the score of other nodes in the network. The most influential nodes were labor_costs (1.907), process_management (1.102), resource_consumption (1.061), maintenance_spare_parts_costs (0.974), employee_satisfaction (0.970), transportation_and_delivery (0.878), and energy_consumption (0.765). This suggested that changes in these nodes would affect changes in other nodes and the network’s overall behaviour. Drawing preliminary conclusions, researchers inferred that if influential nodes (e.g., labour costs, energy consumption) are discussed within a literature review, a rise in the levels of other nodes can theoretically be expected. Consequently, this suggests that within the current body of knowledge, these criteria potentially increase the probability of elevating other nodes, i.e., increase the probability of including other nodes in the analysis. This in turn suggests that existing studies are still dealing with economic criteria (e.g., labour costs, maintenance costs) and there is still not enough research space to consider less influential nodes of environmental (e.g., energy consumption, resource consumption) and social criteria (e.g., employee satisfaction). Thus, this leads to the conclusion that solution space for incorporating and addressing issues of less discussed criteria can only be researched and advanced by companies where contextual and industrial settings are of higher standards. This also suggests that most of the existing maintenance functions are still profit-oriented, thus struggling with availability and financial concerns. After splitting the data into Pre_2021 and Post_2021 for BNA, the obtained results suggested the following. Before 2021, the BNA indicates that the most influential nodes were Transporation_and_Delivery (1.333), Data_management (0.9060), Maintenance_Downtime (0.8780), Reliability (0.873), Maintenance_costs (0.828), Data_analytics (0.787), and Production_costs (0.657), suggesting that changes in these nodes would lead to elevated levels in other nodes. Consequently, this suggested that the top influential nodes were mostly economic and technical (or organisational), which paints the picture that reviews up to 2021 was mostly dedicated to profit-oriented decision-making. This is supported by least influential nodes, such as energy_consumption_costs, resource_management, training_and_upskilling, and health_and_safety_of_workers, among others, which suggests that primary evidence and studies assessing these criteria were limited at the time. The post-2021 BNA show that the most impactful nodes were Labor_costs (1.392), Product_Quality (1.316), Health_and_safety_of_workers (1.164), Reliability (1.149), Maintenance_costs (0.954), Lifecycle_and_operational_costs (0.954), Energy_consumption (0.700), and Knowledge_management (0.656), among others. This, in turn, suggests that although techno-economic factors are still a major concern for maintenance decision-making, the social (e.g., health and safety of workers), environmental (e.g., energy consumption), and even socio-technological (e.g., knowledge management) factors are becoming mainstream in SM, but as suggested mostly on a theoretical basis. In sum, the findings suggest that sustainable maintenance research is no longer relying solely on individual dimensions (e.g., economic, environmental), but rather has evolved into interdisciplinary research concerned with the economic impacts of environmental and social dimensions. Although environmental and social aspects may be considered separately, most of the findings still lack practical and empirical evidence. It seems that SM scientists are still struggling to attract funding, and as a consequence, they have started engaging in transforming the social and environmental impacts into monetary value, as a way to present the economic downsides of not considering these dimensions.