Abstract
The digital transformation of maintenance operations, driven by the fourth industrial revolution and characterized by the widespread adoption of emerging technologies, is essential for enhancing operational efficiency, optimizing asset management, and increasing equipment reliability. Despite the significant benefits, maintenance organizations face numerous challenges in implementing these technologies and ensuring successful transformations, including technological integration complexities, cultural resistance, skill gaps, and aligning digital strategies with broader business objectives, among others. This work addresses the critical gap in structured methodologies for evaluating and guiding digital maturity in Maintenance Digital Transformation (MDT) by proposing a comprehensive maturity assessment model. The model is grounded in nine key maturity dimensions spanning organizational culture, technology & data management, leadership & management aspects, organizational development & change, digital strategy, knowledge & skills, and internal integration. Building on emerging research in this field, data collection and advanced statistical techniques, and feedback from experts in the field, this work adopts a structured and rigorous methodology to (1) identify and test the maturity dimensions and subdimensions driving the success of this transformation, (2) adopt well-established guidelines to develop a structured and comprehensive maturity assessment model allowing organizations to assess their current MDT maturity level, and (3) perform an initial empirical validation of the proposed maturity grid. The resulting maturity grid provides organizations with detailed guidelines on their current digital maturity level and actionable recommendations for advancing to higher stages of maturity. The model offers diagnostic insights and a strategic roadmap to guide maintenance organizations through the multifaceted digital transformation process.
Keywords
1. Introduction
The Industry 4.0 era, marked by rapid advances in digital technologies, has reshaped how industries operate worldwide. This shift draws on technological tools such as Internet of Things (IoT), data analytics, Artificial Intelligence (AI) and cloud platforms to reconfigure business processes, including maintenance operations.1,2 In the maintenance sector, the shift towards the digitalization of maintenance represents a strategic endeavor to enhance operational efficiency, optimize asset management, and increase overall reliability and uptime of equipment. 3
Maintenance, traditionally seen as a cost center, has gradually been recognized as a critical component of operational strategy that can significantly influence the financial and operational performance of an organization. 4 Maintenance digital transformation (MDT) enables organizations to transition from reactive to predictive strategies, wherein data-driven insights facilitate the anticipation of failures before they occur. This predictive approach helps in reducing downtime, extending the life of assets, and reducing the total cost of ownership. 5 Beyond predictive maintenance, MDT encompasses a range of sophisticated strategies such as prescriptive maintenance, which in addition to forecasting when equipment will fail, also suggests actionable interventions to mitigate potential failures. 6 Another pivotal aspect of MDT is its capacity to integrate asset management with real-time monitoring and decision-making capabilities. This integration enables a more holistic view of asset health, operational demands, and maintenance scheduling. 7 Furthermore, the shift towards MDT has facilitated the adoption of mobile and cloud-based solutions that enhance the accessibility and agility of maintenance operations. Technicians equipped with mobile devices can access maintenance histories, schematics, and component information on-the-go, enhancing service speed and quality. 8 However, despite these significant benefits, implementing and maximizing the value of digital technologies in maintenance remains challenging. Organizations often face technological integration complexities, cultural resistance to change, skill gaps among personnel, and difficulties in aligning digital strategies with broader business objectives.9–11 Overcoming these barriers requires more than robust technological solutions. It also necessitates significant shifts in organizational culture and processes. This highlights that the journey toward digital transformation extends beyond technology itself.12,13
Furthermore, one of the significant hurdles in this transformation is the lack of structured and standardized methodologies to assess and guide the maturity of an organization’s digital transformation capabilities within the maintenance domain. 14 Without a clear understanding of their current digital maturity level, organizations struggle to effectively plan and implement strategies that harness the full benefits of Industry 4.0. The necessity for a maturity assessment model in MDT stems from the need to provide organizations with a framework to identify their current capabilities, benchmark against best practices, and systematically advance towards higher levels of digital integration. 15 A comprehensive maturity model aids in assessing the current state of digital transformation. It also serves as a roadmap that guides organizations through the complexities of implementing digital initiatives. Such models are pivotal in diagnosing critical gaps in technology, processes, capabilities and skills, and providing targeted recommendations that are aligned with the organization’s specific context and industry standards. The need for such a model is particularly pressing given the rapid evolution of technology and the varying pace of adoption across different sectors. 16
Building on prior research on MDT,17–21 particularly studies examining its enablers, barriers, and implementation requirements,9,15 this study addresses the need for a structured and empirically grounded maturity assessment model tailored to the maintenance context. Existing contributions have improved understanding of MDT implementation; however, they do not yet provide a comprehensive and empirically validated instrument that organizations can use to systematically assess their maturity and guide transformation efforts. To address this gap, this paper develops a Maintenance Digital Transformation Maturity Model (MDTMM) grounded in the multidimensional nature of MDT, encompassing strategic, technological, organizational, managerial, and human-related aspects. The literature review therefore not only describes MDT enablers but also serves as the conceptual basis for identifying the core maturity dimensions that structure the proposed model. These theoretically grounded dimensions are subsequently examined and refined through empirical validation using Partial Least Squared (PLS) Structural Equation Modeling (SEM) and expert evaluation. Thus, this study contributes by proposing a multidimensional and empirically validated maturity model specifically tailored to MDT, addressing limitations of existing maturity models that are often generic or focused on isolated aspects of digital transformation. In line with this aim, the study is designed to meet the following objectives. • Identify and test the key maturity dimensions and indicators of MDT. • Develop a structured and comprehensive maturity assessment model for MDT based on established maturity model development guidelines and insights from both academic research and industry practice. • Provide a practical roadmap enabling organizations to assess their MDT maturity level and identify improvement priorities. • Conduct an initial validation of the proposed model through empirical analysis and expert evaluation.
The subsequent sections are structured as follows. Section 2 provides a detailed literature review that examines the current trends of MDT, its key enablers and challenges, the existing maturity models and frameworks, and the current gaps in the literature. Section 3 describes the research methodology used to develop the maturity model including the macro and micro approaches for model development and the validation processes. Section 4 presents the maturity model in detail including 1 the analysis of the maturity dimensions and components, 2 the development of the maturity grid, and 3 the empirical validation of the preliminary model through expert consultations and refinement. Section 5 concludes the paper and suggests directions for future research.
2. Review of the literature
2.1. Digital transformation of maintenance operations
Digital maintenance, also referred to as Maintenance 4.0 or MDT, involves integrating advanced technologies into maintenance practices to enhance efficiency and agility. It leverages technologies such as AI, big data, and machine learning to enable data-driven decision-making. This transformation improves maintenance planning, reduces downtime, and enhances overall asset performance. 22 In addition, MDT enables the use of Virtual Reality (VR) and Augmented Reality (AR) for remote training, inspection, and troubleshooting, improving accuracy and reducing operational risks. 23 Through analyzing data patterns and trends, digital maintenance also enhances asset reliability and predictability, allowing organizations to anticipate and prevent potential failures. 24 Furthermore, the integration of IoT and cloud solutions supports real-time coordination among stakeholders, fostering a more connected maintenance environment. Therefore, MDT represents a shift toward proactive, data-centric maintenance management, enabling improvements in efficiency, productivity, and performance. 25
Research on MDT can be broadly categorized into several streams. One major stream addresses the technical aspects of digital maintenance with particular focus on the integration and application of Industry 4.0 technologies in maintenance processes. Researchers have extensively studied the design and deployment of predictive maintenance models and algorithms, emphasizing the importance of predictive analytics in foreseeing equipment failures and optimizing maintenance schedules.26,27 The incorporation of robotics and automation technologies into maintenance processes is another significant area of research, highlighting advancements in automated inspections and repairs that enhance operational efficiency and safety. 28 Furthermore, the contribution of machine learning, AI, and digital twins to maintenance has been explored, demonstrating how these technologies can create virtual replicas of physical assets to monitor conditions in real-time and predict future failures. 29
Another significant research stream investigates the benefits and limitations linked to the implementation of digital maintenance systems. Key benefits identified include improved operational efficiency, enhanced safety, reduced downtime, and cost savings, among others. 30 These advantages stem from the ability to leverage real-time data and advanced analytics to make informed maintenance decisions. However, significant challenges accompany these benefits and are investigated by many studies.10,31,32 Among these challenges, issues of data privacy, security, and interoperability are prominent concerns, as the integration of various technologies and data sources necessitates robust cybersecurity measures and seamless data exchange protocols. 33 Other key barriers include, but are not limited to, resistance to change, financial constraints, and lack of standardization. 34
A third research stream examines practical examples and case studies providing valuable insights into the real-world application of MDT. Various industries have been the focus of these studies, including manufacturing, automotive, and oil and gas sectors.35–37 Furthermore, the successful implementation of digital maintenance systems extends beyond technical aspects to include human factors and organizational impacts. Although not very extensive, research in this area emphasizes the critical contribution of training and competency development in equipping maintenance personnel with the necessary competencies to utilize advanced technologies effectively. 38 Organizational and cultural aspects related to the adoption of smart maintenance are also significant areas of study that are directly tied to successful implementations. 39 Additionally, the impact of digital maintenance on organizational structures and strategies has been explored. These studies highlight the need for organizations to adapt their processes and structures to accommodate new technologies and data-driven decision-making. 40
Moreover, ethical and social considerations are increasingly gaining attention in the context of digital maintenance. In this context, researchers have explored how smart maintenance reshapes labor demand and roles, examining how automation and AI might affect employment and the nature of maintenance work.41,42 Additionally, the sustainability gains enabled by maintenance 4.0 is a key theme, as digital maintenance technologies can enhance resource efficiency, reduce environmental impact, and support sustainability goals. 43
Researchers have also looked into the importance of understanding the factors that influence success for organization seeking to implement digital maintenance. They have identified various multifaceted factors including technological readiness, management support, and internal and external collaborations, as critical determinants of successful MDT implementations. 9 Adoption trends and strategic guidance for managerial decisions in digital maintenance have been also studied, providing guidance on how organizations can effectively navigate the MDT journey. 44 Additionally, roadmaps for implementation offer structured approaches to planning and executing digital maintenance initiatives, ensuring that organizations can systematically integrate new technologies into their maintenance practices.15,18,45
Despite the growing research on various aspects of digital maintenance, there remains a notable gap in the development of comprehensive maturity models and implementation guidelines. While existing studies provide valuable insights into individual components of digital maintenance, there is a lack of holistic frameworks that guide organizations through the entire process of this transition. Maturity models that evaluate an organization’s preparedness and progress in adopting digital maintenance practices are particularly scarce which highlights the need for further research in this area. Additionally, comprehensive implementation guidelines that address both technological and organizational challenges are essential to support maintenance organizations in their digital transformation journey.
These findings indicate that MDT is not driven solely by the adoption of individual digital technologies but rather by the coordinated development of organizational, technological, and managerial capabilities. This multidimensional nature of MDT provides an important conceptual basis for structuring the maturity dimensions of a dedicated maturity model for MDT.
2.2. Key enablers of maintenance digital transformation
The shift toward MDT is not only about adopting new technologies, but also about creating an environment in which these technologies can be effectively utilized to improve maintenance processes. Understanding the key drivers and enablers is therefore essential for organizations seeking to successfully implement digital transformation. These enablers provide the theoretical basis for the maturity dimensions developed in this study, as they represent the core organizational, technological, and managerial capabilities required for MDT implementation. Among these drivers, the rapid advancement of technological innovations, combined with the pursuit of operational efficiency, plays a central role in transforming maintenance operations. The integration of these technologies facilitates real-time monitoring of equipment, prediction of failures, optimization of maintenance schedules, and overall improvement of decision-making processes.3,25,46 However, the successful implementation of MDT is not solely driven by technological advancements and operational needs; it also hinges on other organizational and managerial aspects.
Firstly, a supportive organizational culture and strong leadership commitment are crucial for fostering an environment where digital technologies can thrive. This involves promoting a culture of innovation, continuous learning, and collaboration. Employees must be encouraged to embrace new technologies and adapt to changing maintenance practices.47,48 Leadership commitment is equally important. Leaders must articulate a clear vision for MDT and ensure alignment across all levels of the organization. This includes securing the necessary resources, setting realistic goals, and actively participating in the digital transformation journey. Leadership support helps in overcoming resistance to change and ensures that MDT initiatives are prioritized and sustained over time.49,50
Secondly, effective data governance and analytical capability are critical enablers of MDT. The massive volumes of data generated by IoT devices and sensors must be accurately and promptly collected, stored, and analyzed to extract meaningful insights. To that end, data management and analytical skills are critical for transforming raw data into actionable intelligence, and quality and integrity are paramount, as poor data quality can lead to incorrect analyses and suboptimal decisions.51,52
The human element is another critical component of MDT. As maintenance processes become more digitalized, the skill sets required for maintenance personnel also evolve. There is agreement among researchers that organizations must invest in continuous education and training programs to develop the digital competencies of their workforce. This includes training in data analytics, IoT, AI, and other relevant technologies.53,54 Moreover, it is essential to address potential resistance to change among employees. Change management strategies, such as involving employees in the transformation process, articulating the benefits of MDT in a clear and accessible manner, and providing assistance throughout the transition, can alleviate resistance and foster a positive attitude towards digital maintenance.3,55
Furthermore, collaboration within and beyond the organization is a vital enabler of MDT. Internally, cross-functional collaboration between maintenance, operations, IT, and other departments is essential. Such collaboration ensures that digital maintenance initiatives are well integrated and aligned with organizational goals. 56 Externally, partnerships with technology vendors, research institutions, and other industry players facilitate knowledge sharing, access to cutting-edge technologies, and the development of best practices. 17
Despite the advancements and opportunities presented by MDT, several gaps remain. While many studies focus on the technological aspects of MDT, a holistic approach that also addresses organizational, cultural, and human factors is essential. In an attempt to address this gap, a recent work by 9 identified the multifaceted underpinning enablers driving this transformation. The authors also validated the identified enablers through conducting a Delphi study with a highly qualified expert panel including both researchers and practitioners. However, research on the specific enablers and drivers of MDT is still evolving, and more empirical studies are needed to understand the various impacts of these factors in different industrial contexts.
2.3. Maturity models and frameworks in maintenance digital transformation
Given the multidimensional nature of MDT identified in the previous sections, organizations require structured approaches to assess their progress in implementing MDT initiatives. Maturity models provide a suitable mechanism for this purpose, as they enable organizations to evaluate their current transformation stage and identify structured pathways for improvement.
Maturity models have become essential tools in various fields to evaluate and guide organizations’ progress towards achieving optimal performance and efficiency. The essence of maturity assessments lies in their ability to provide a systematic evaluation of an organization’s processes, capabilities, and overall maturity within a specific domain. 57 This concept originated with the Capability Maturity Model (CMM) in the 1980s, developed to improve software development processes by Carnegie Mellon University’s Software Engineering Institute (SEI). CMM provided a structured framework for assessing the maturity of software development practices and identifying areas for improvement. 58 In the early 2000s, the SEI introduced the Capability Maturity Model Integration (CMMI), which was designed to provide a more comprehensive approach to process improvement, covering areas such as systems engineering, integrated product and process development, and supplier sourcing. 59 As the concept of maturity models gained popularity, researchers and practitioners began to develop models for other domains beyond software engineering, including supply chain management, 60 project management, 61 government services, 62 and innovation management, 63 among others. These maturity models share the common goal of helping organizations systematically improve their processes and capabilities through clearly defined stages of maturity. Each stage represents a higher level of sophistication and effectiveness, guiding organizations from initial, ad-hoc practices to optimized, continuous improvement processes. 64
In the field of maintenance, the integration of maturity assessments became a logical progression given the paradigm shift in maintenance strategies. In essence, the domain of maintenance maturity assessment has seen significant evolution over the past two decades. Initially, research efforts were concentrated on establishing foundational frameworks. Pioneering contributions by65–67 focused on the maturity of corrective maintenance processes, the importance of education and training for maintainers, and introduced a process model for problem management aimed at enhancing all aspects of maintenance. This early period marked the beginning of a structured approach to maintenance maturity, acknowledging the critical role of human factors alongside processes and technology. Moving into the early 2000s, research began to adopt a more integrated and strategic view of maintenance. 68 and 69 highlighted a strategic asset management framework and the development of an evaluation framework within rail transit, blending strategic management principles with maintenance operations. The late 2000s and early 2010s saw the introduction of CMMs to identify potential improvements within maintenance organizations, with 70 proposing a “house of maintenance” framework based on this model. By the turn of the decade, the focus shifted towards the adoption of information and communication technologies (ICT) in maintenance. 71 explored the relationship between e-Maintenance technology selection and the maturity of maintenance service provision systems. This period reflects an increased interest in leveraging digital solutions to enhance maintenance operations, a trend that continues to grow today. Entering the second decade of the 21st century, the focus expanded to include specialized sectors, as seen in the study by 72 on problem management maturity in the telecommunications sector. Similarly, 73 assessed maintenance processes within air traffic control service providers, aligning with ISO/IEC 15504 standards, showcasing the application of maturity models in highly regulated and safety-critical environments.
Comparative analysis of existing works.
Despite these advancements, the application of maturity assessment in MDT is a relatively new and evolving area, and research on MDTMM remains limited. Existing works often lack comprehensiveness, focus on specific segments of the transformation, or fail to validate their proposed frameworks. That is, there is a clear gap in the availability of a comprehensive maturity model that thoroughly covers the various dimensions involved in the MDT journey. Current research often evaluates the extent to which some digital technologies are integrated into maintenance practices, but a holistic model encompassing all facets of MDT remains elusive. As noted by, 15 having pertinent models to comprehensively assess readiness in this context helps organizations understand their current capabilities and identify areas needing improvement along the maturity scale. However, most existing digital maturity models are designed for broad digital transformation contexts and do not explicitly capture the specific organizational, technological, and managerial characteristics associated with MDT. Furthermore, aside from a few notable exceptions, a significant critique of existing maintenance maturity models is that their development often relies on the subjective best efforts of the authors, rather than following clearly defined, standardized guidelines as discussed in section 2.4. To that end, there is a pressing need for adopting more standardized and structured methodologies in developing maintenance maturity models in general and MDTMM in particular, as highlighted by 80 and. 57
Taken together, the reviewed literature highlights that existing maturity models in the context of digital transformation and maintenance are often either too generic, focus on isolated dimensions, or lack empirical validation. At the same time, these insights indicate that MDT maturity must be understood as a multidimensional construct emerging from the interaction of organizational, technological, managerial, and human capabilities. These limitations and conceptual observations point to the need for a more comprehensive and context-specific framework, directly motivating the structure of the proposed MDTMM, which operationalizes these interdependent dimensions into a coherent and empirically validated maturity model tailored to the maintenance context.
2.4. Guidelines for developing maturity models
Over the years, some methodologies and guidelines have been proposed to guide the systematic development of maturity models. This section reviews the most influential works in this area and compares their contributions.
Reference 80 provided one of the most comprehensive guidelines applicable across various domains. Their methodology includes five phases: “scope, design, populate, test, deploy, and maintain”. Each phase is carefully detailed to ensure a robust model development process. They stress the importance of thorough domain understanding, iterative refinement, and applying the model across multiple organizations for validation and benchmarking. This framework is particularly noted for its flexibility and adaptability to different organizational contexts, making it highly relevant for diverse industries. 82 proposed a detailed procedure specifically tailored for IT management maturity models. Their model comprises eight phases: problem formulation, benchmarking against existing models, development strategy formulation, iterative model development, evaluation, implementation, and maintenance. Emphasizing design science principles, the authors advocated for a rigorous and iterative process to ensure the practical applicability and effectiveness of the maturity model. Based on this work, 83 extended the existing guidelines by offering suggestions on how to improve extant design principles for maturity models development. 84 introduced the concept of focus area maturity models, which differ from fixed-level models like CMM by allowing incremental improvements within specific functional domains. This approach is particularly suited for developing areas such as enterprise architecture and software product management, providing a balanced and incremental development path. 57 provided a structured approach to creating maturity grids that is practical and applicable across different domains. They outlined a four-phase roadmap: planning, developing, evaluation, and maintenance. The authors highlighted the necessity of clear definitions and logical progression of maturity levels, and they provided guidance on formulating cell text for behavioral characteristics of processes at each maturity level.
García-Mireles 85 conducted a systematic literature review to analyze existing methods and practices for developing maturity models. They identified common activities across various methodologies, such as establishing goals, designing model architecture, setting capability levels, and pilot testing. Their review highlighted the need for a sound theoretical basis and rigorous methodology in the development of maturity models, pointing out that many models lack these elements which leads to ambiguous results in practice. While each of the above works provides valuable insights and methodologies for developing maturity models, 80 and 57 offer the most comprehensive and universally applicable guidelines. Both frameworks share several common stages, such as scoping, designing, populating, and validating the model, which can be mapped to each other and complement each other in some other respects. For instance, 80 phases of design and populate align with 57 steps of defining maturity levels and identifying key process areas. These stages emphasize the iterative development and empirical validation of the model, ensuring its relevance and effectiveness. Furthermore, the complementary nature of these two frameworks provides a robust foundation for developing maturity models in the context of MDT through unifying the common stages and taking advantage of the complementary ones.
Taken together, the reviewed literature suggests that MDT cannot be understood solely through the adoption of individual digital technologies. Instead, MDT maturity emerges from the alignment of multiple organizational dimensions, including strategic orientation, leadership commitment, technological infrastructure, knowledge and skills, and organizational integration. This insight provides the conceptual foundation for the present study. Accordingly, the proposed maturity model conceptualizes MDT as a multidimensional transformation process and translates the identified dimensions into measurable constructs. The empirical phase of the study is therefore designed to test the reliability and validity of these constructs and to operationalize them into a structured maturity model and maturity grid that can support organizational assessment and improvement.
3. Research methodology
Based on the literature synthesis presented in the previous section, this study adopts the premise that MDT maturity is a multidimensional construct that must be operationalized through theoretically grounded and empirically validated dimensions. The methodological design therefore aims to translate the conceptual understanding of MDT into a structured maturity assessment model. To achieve this, the study combines established maturity model development guidelines with empirical construct validation using PLS-SEM and qualitative expert evaluation to refine and operationalize the final maturity grid.
The development of a comprehensive maturity model for MDT requires a structured and systematic approach. This study employs a dual methodology: a macro approach based on adaptation of the guidelines established by
80
and,
57
and a micro approach that defines the detailed methodological steps adopted to conduct each macro step. The macro approach provides a high-level framework for the development process, while the micro approach offers specific methods and techniques to ensure the robustness and validity of the model, as illustrated in Figure 1. Research methodology.
3.1. Macro approach: Phases of model development
The macro approach to developing the MDTMM incorporates the following phases, drawing on the works of
80
and
57
. • • • • •
3.2. Micro approach: Methodological steps
3.2.1. Scope & design
As discussed in the previous sections, the focus of the MDTMM is to evaluate organizational readiness and progression in adopting digital maintenance practices. A comparison with existing models is conducted in Section 2.3, illustrated by Table 1, which highlights the contributions and gaps of extant works and the peculiarities of this research.
Defined maturity levels.
3.2.2. Populate
The components and subcomponents forming the maturity domains were identified in a previous work by,
9
which used a hybrid-reactive Delphi approach. This approach involved four rounds of iterative feedback and consensus building with a highly qualified expert panel, ensuring comprehensive coverage of the enablers of successful MDT. That study systematically identified and empirically validated the key enablers of MDT through expert consensus, providing a theoretically grounded foundation for the maturity dimensions adopted in the present research. A snapshot of the various MDT maturity domains and their corresponding indicators are presented in Figure 2. These validated enablers represent the core organizational, technological, managerial, and human capabilities required for successful MDT implementation and therefore serve as the conceptual basis for defining the maturity dimensions of the proposed model. Domain components and subcomponents.
3.2.3. Test
The objective of this phase is to rigorously validate the maturity dimensions and their respective indicators, identify the most impactful ones, and bring these dimensions to a manageable level by organizations. This is a crucial step to ensure the reliability and accuracy of the developed maturity model, confirming that it effectively measures the intended aspects related to MDT. To achieve this, a rigorous methodology is adopted, involving the development and administration of a detailed questionnaire followed by advanced statistical analysis.
Firstly, a questionnaire is designed to capture data on the identified maturity dimensions and indicators. The survey targeted professionals involved in maintenance management and digital transformation initiatives across multiple industrial sectors, including manufacturing, energy, transportation, and other asset-intensive industries. The participating organizations vary in size, ranging from medium-sized firms to large multinational companies with established maintenance operations. Geographically, the respondents are distributed across multiple regions (e.g., UAE, France, India, USA, KSA, Tunisia, South Africa, Sweden), reflecting the international scope of MDT initiatives and providing a diverse representation of maintenance practices across different organizational and industrial contexts. Participants were identified through professional networks, industry contacts, and relevant professional communities focusing on maintenance and digital transformation. The sampling approach aimed to capture respondents with direct experience in maintenance operations and digital initiatives to ensure informed assessments of the maturity dimensions. Although the sampling strategy can be characterized as purposive and snowball-based, it enabled the collection of insights from practitioners actively engaged in MDT initiatives. Given the relatively large number of respondents, their characteristics are summarized in aggregated form rather than presented individually. Respondents were encouraged to provide detailed and honest feedback, which is essential for the robustness and accuracy of the subsequent analysis. The questions were formulated to assess different aspects of MDT, such as the adoption of digital technologies, the integration of data analytics, the organizational culture towards innovation, and the efficiency of maintenance processes. Since the survey responses were collected from knowledgeable professionals involved in MDT initiatives and the constructs represent organizational capabilities rather than perceptual attitudes, the risk of common method bias is limited.
Following data collection, PLS-SEM is employed to analyze the data. It is is a powerful statistical technique that allows for the testing of complex relationships between observed and latent variables.87,88 The selection of PLS-SEM is appropriate for this study for several reasons. First, PLS-SEM is well suited for exploratory and predictive research contexts where theoretical development is still evolving, as is the case with MDT maturity assessment. Second, the method can handle complex models with multiple latent constructs and indicators while remaining robust with moderate sample sizes. 89 Third, this method is particularly suitable for validating the constructs of the maturity model, as it can assess both the measurement model (the relationships between indicators and their corresponding dimensions) and the structural model (the causal relationships between the dimensions themselves).
This analysis helps in evaluating the reliability and validity of the maturity dimensions. Reliability is assessed by examining the internal consistency of the indicators, typically using Cronbach’s alpha. A high Cronbach’s alpha value indicates that the indicators reliably measure the same underlying construct. Validity, on the other hand, is assessed through convergent and discriminant validity tests. Convergent validity ensures that indicators of a specific dimension are highly correlated, while discriminant validity confirms that the dimensions are distinct from each other.
89
In this study, the measurement model is reflective, meaning that the indicators reflect the underlying construct they are intended to measure. This specification is appropriate because the indicators represent observable manifestations of underlying organizational capabilities related to MDT maturity rather than independent formative components. Once the reliability and validity of the measurement model are established, the structural model is assessed. Essentially, the path model tests the significance and relevance of the relationships between each maturity dimension/construct and the overall MDT maturity. Figure 3 describes the steps adopted for measurement model and structural model evaluation. Measurement and structural models evaluation.
Several methodological assumptions underpin the empirical analysis conducted in this study. First, the measurement model is specified as reflective, assuming that the observed indicators represent manifestations of the underlying maturity constructs. 89 Second, the empirical data used in the SEM analysis are cross-sectional, capturing respondents’ perceptions of MDT maturity at a specific point in time. While this approach is appropriate for the initial validation of the maturity dimensions, it does not capture the dynamic evolution of digital transformation over time. Finally, although the sample size is adequate for PLS-SEM analysis, the findings should be interpreted with consideration of potential sampling bias associated with survey-based studies.
3.2.4. Development
The development phase is integral to the overall maturity model creation, focusing on populating the grid cells with detailed descriptions and guidelines that correspond to each maturity level and dimension. This phase builds on the previously validated maturity dimensions and their indicators, providing practical insights for organizations to understand their current maturity level and the steps needed to advance.
The process begins with a thorough review of existing literature on maturity models, not limited to the field of maintenance but also encompassing broader areas such as digital transformation in general and other industry-specific maturity models. This comprehensive review helps in identifying best practices and common elements that can be adapted to the MDT context. Given the limited literature on MDT-specific maturity models, insights are drawn from analogous fields to ensure the development of a robust and comprehensive model.
Next, the grid cells are populated with detailed descriptions of each maturity level for all identified dimensions. These descriptions serve as benchmarks, illustrating the characteristics and capabilities that organizations should exhibit at each maturity stage. The five levels (Initial, Managed, Defined, Quantitatively Managed, and Optimizing) provide a clear progression path to help organizations systematically enhance their MDT practices. Each level is carefully defined to ensure clarity and practical applicability.
3.2.5. Evaluation
The evaluation phase is crucial for ensuring the practical applicability and robustness of the developed maturity model. This phase involves an iterative process of refinement through expert consultations and initial validation steps to enhance the model’s accuracy, relevance, and usability in real-world contexts.
The initial phase of the evaluation process entails developing a comprehensive questionnaire to consult with experts in the field. This questionnaire is designed to capture both structured and open-ended feedback. The structured component asks experts to rate the overall comprehensiveness, practical applicability and adaptability of the model as well as their level of agreement with the formulation of each cell in the maturity grid across all levels. Experts rate each maturity cell on a Likert scale, indicating their agreement with how well the cell descriptions represent the intended maturity level and dimension. This structured feedback helps identify the experts’ level of consensus and areas that may need further clarification or adjustment. Whereas the open-ended component provides space for experts to offer suggestions for improvement and refinement. They provide qualitative feedback including comments, suggestions, and insights based on their expertise and experience. These open-ended responses are essential for understanding the complex perspectives of the experts and capturing any overlooked aspects of the model.
Profiles of experts involved in the evaluation phase.
Expert involvement in the MDTMM development process is structured around two distinct groups. The first group consists of 106 professionals who contribute to the empirical validation of the maturity dimensions and indicators through the survey-based SEM analysis described in Section 4.1. The second group consists of 11 experts involved in the evaluation phase, where they assess and refine the maturity grid through quantitative ratings and qualitative feedback, including the evaluation of its comprehensiveness, applicability, and adaptability, as presented in Section 4.3.
The outcomes of the scope & design and populate phases (Steps 1 and 2) establish the conceptual structure and content of the MDTMM, including the definition of maturity levels and the identification of the core dimensions and indicators derived from the literature and prior research, as detailed in Sections 3.2.1 and 3.2.2. These elements form the foundation for the subsequent phases presented in Section 4. To provide a concise overview of the MDTMM development process and its associated outputs, Figure 4 summarizes the progression from the initial conceptual structure to the final validated model. Summary of MDTMM development steps and associated outputs.
4. Results and discussion
4.1. A structural equation modeling analysis of the maturity dimensions
This section presents the results of the test phase (Step 3) described in Section 3.2.3, where the maturity dimensions and their associated indicators are empirically validated using PLS-SEM. The analysis is based on survey data collected from maintenance professionals, as described below.
The survey instrument was distributed among maintenance professionals involved in digital transformation initiatives across multiple industrial sectors, yielding 106 complete responses used for the empirical analysis. Prior to the SEM analysis, responses were screened for completeness and consistency to ensure the quality of the dataset. The use of targeted professional networks helped ensure that the collected responses represent practitioners actively engaged in MDT initiatives, thereby reducing the risk of non-response bias. Sophisticated SEM approaches were subsequently applied on this dataset to evaluate the model in terms of reliability and validity and to examine the relationships among latent variables. The systematic approach illustrated in Figure 3 is employed to evaluate the results of PLS-SEM analysis.
4.1.1. Measurement model evaluation and estimation
A series of essential checks are carried out to verify the reliability and validity of the measurement model. The model undergoes iterative modification until the constructs’ measurement criteria are adequately satisfied. 89 First, internal consistency reliability is assessed through measures such as Cronbach’s alpha and composite reliability to determine the degree to which items within a construct consistently capture the same latent concept. Scores greater than 0.7 indicate high internal consistency. Second, convergent validity is evaluated using standardized outer loadings and the average variance extracted (AVE) for each construct. The loadings indicate how strongly each indicator relates to its latent construct, whereas AVE represents the share of variance the construct captures relative to measurement error. As a rule of thumb, loadings above 0.70 and AVE greater than 0.50 provide evidence of strong convergent validity. Indicators with loadings between 0.4 and 0.7 are evaluated for their impact on other reliability and validity measures and their conceptual relevance before deciding on their exclusion. Finally, discriminant validity is evaluated through Fornell-Larcker criterion (FLC) and the Heterotrait-Monotrait (HTMT) ratio where values exceeding 0.85 indicate a potential lack of discriminant validity. 90
Impact of excluding indicators during the measurement model refinement process.
Revised SEM model.
Reliability and convergent validity analysis for the refined measurement model.
These results confirm that the proposed maturity dimensions are empirically robust and suitable for assessing MDT maturity across multiple organizational domains. The strong reliability and validity indicators support the multidimensional conceptualization of MDT proposed in Section 2, suggesting that the organizational, technological, and managerial factors identified in the literature can be operationalized as coherent maturity constructs. This finding reinforces the argument that MDT should be understood as a cross-dimensional organizational transformation rather than a purely technological adoption process.
4.1.2. Path model evaluation
Criteria for evaluating the structural model.
Path model significance.
The relative strength of these relationships reveals meaningful patterns regarding the drivers of MDT maturity. In particular, the strong influence of knowledge management and workforce skills development highlights the central role of human capabilities in enabling digital transformation in maintenance. This finding aligns with prior studies emphasizing that digital transformation initiatives require not only technological infrastructure but also the development of organizational competencies and digital skills among maintenance personnel. From a managerial perspective, this result indicates that organizations seeking to advance their digital maintenance maturity should prioritize investments in workforce digital competencies, knowledge-sharing practices, and training programs that enable personnel to effectively leverage emerging maintenance technologies.
Overall, the structural model results provide empirical support for the multidimensional framework of MDT proposed in this study. The findings demonstrate that multiple organizational and technological dimensions jointly contribute to overall digital transformation maturity, rather than any single factor acting independently. This pattern is consistent with the conceptual foundations presented in the literature review, which emphasize the interdependence of strategic, cultural, technological, and knowledge-related capabilities in driving successful digital transformation initiatives.
Compared with prior maturity models discussed in Section 2.3, the present model offers a broader and more integrated representation of MDT maturity. While several existing models emphasize technological readiness or selected organizational factors, the results of this study support a framework in which strategic, organizational, managerial, technological, and knowledge-related dimensions jointly shape overall MDT maturity. In this sense, the proposed model differs not only by being tailored to the maintenance context, but also by providing a more empirically grounded and cross-dimensional assessment framework than many extant digital maturity models.
4.2. Maturity grid development
This section presents the results of the development phase (Step 4) described in Section 3.2.4, where the validated maturity dimensions are translated into a structured maturity grid. The resulting maturity grid and its formulation are described below.
The development of the maturity grid and formulation of its cells are pivotal steps in operationalizing the MDTMM. Each maturity dimension represents a critical aspect of MDT, encompassing strategic, organizational, and technological factors that collectively drive successful digital transformation in maintenance. Indicators within each dimension provide specific measures for assessment to ensure a comprehensive evaluation of an organization’s capabilities and practices. In constructing the maturity grid, clear and concise descriptions are developed for each maturity level across all the dimensions. These formulations provide organizations with a detailed understanding of what is required at each stage of maturity, from the initial phases of digital adoption to the advanced stages of optimization. The grid articulates clear characteristics and benchmarks for each level to facilitate a structured approach to achieving higher maturity and guide organizations through a systematic progression. As discussed in Section 3.2.1., the maturity levels defined in this study are 1 Initial, 2 Managed, 3 Defined, 4 Quantitatively Managed, and 5 Optimizing. Each level is designed to reflect a distinct stage of maturity to enable organizations to measure their progress and plan for future advancements. The grid descriptions offer practical insights into the expected performance and capabilities at each stage, helping organizations to align their strategies and initiatives with best practices in MDT. In practice, the maturity grid can be used as a self-assessment tool. For example, an organization may evaluate its practices for each maturity dimension and determine the level that best describes its current capabilities. If an organization’s practices align with the characteristics described under the “Managed” level for a given dimension but not yet with those of the “Defined” level, this indicates that further process formalization and standardization may be required to progress to the next maturity stage. From a managerial perspective, the grid can be used to translate assessment results into concrete action priorities. For instance, if an organization demonstrates relatively stronger technological capabilities but lower maturity in knowledge management, change management, or strategic alignment, managers can use the model to redirect investments toward training, leadership development, cross-functional coordination, or governance mechanisms rather than focusing exclusively on technology acquisition.
The practical implications of the model also vary across sectors. In manufacturing settings, the maturity assessment may be particularly useful for guiding the integration of predictive maintenance, data analytics, and shop-floor decision support into established production systems. In other asset-intensive sectors such as transportation, utilities, or oil and gas, the model can help organizations assess how digital maintenance capabilities support reliability, safety, coordination across dispersed assets, and long-term asset performance. Accordingly, while the maturity dimensions remain stable, the operational emphasis and improvement priorities may differ depending on sector-specific requirements.
Table A.1 of the appendix presents the preliminary maturity grid formulations initially developed by the authors. The updated maturity grid, incorporating refinements based on expert feedback obtained during the evaluation phase, is presented in Table 11. In practice, the proposed MDT maturity grid can serve multiple purposes depending on the context of application. First, it can function as a structured self-assessment tool, enabling organizations to evaluate their current capabilities across the different maturity dimensions and determine their current maturity level. Second, the model can support consultancy and diagnostic assessments, where practitioners or consultants use the grid to identify capability gaps and recommend targeted improvement initiatives for advancing MDT implementation. Finally, the framework can also serve as an academic benchmarking instrument, allowing researchers to compare digital maintenance maturity across organizations, sectors, or geographical contexts and to further investigate the determinants and outcomes of MDT maturity.
4.3. Expert evaluation and model refinement
This section presents the results of the evaluation phase (Step 5) described in Section 3.2.5, where the maturity model and grid are refined through expert consultation. The evaluation combines quantitative analysis of expert ratings and qualitative assessment of their feedback to validate and further refine the proposed MDTMM.
4.3.1. Quantitative analysis and expert consensus review
Central tendency and variability statistics.
Key findings were uncovered through this analysis. Firstly, the model’s overall comprehensiveness garnered high ratings with minimal variability, indicating that the majority of experts find it well-structured and thorough. Secondly, the practical applicability and adaptability of the model are rated consistently, achieving mean and median scores of 4, suggesting that experts view the model as broadly applicable and adaptable to different organizational contexts. Additionally, the specific formulations of the cells of the maturity grid received good ratings with acceptable variation levels (STD and IQR ≤ 1) which reflect strong expert approval. This indicates a generally favorable perception of these formulations despite minor divergences in the experts’ viewpoints regarding a few ones.
ICC analysis results.
4.3.2. Qualitative evaluation of experts’ open-ended responses and resulting improvements
Experts feedback and actions taken.
Updated maturity grid formulations based on expert feedback.
5. Conclusion and future research directions
The comprehensive maturity assessment model for MDT developed in this paper addresses a critical gap in current literature and practice. This model, grounded in nine dimensions, provides a structured framework for the evaluation and enhancement of digital maturity in maintenance operations, enabling organizations to systematically transition from traditional maintenance approaches to more advanced, data-driven strategies. This transformation is pivotal in achieving increased operational efficiency, optimized asset management, and enhanced equipment reliability, among others. This paper responds to the lack of structured and standardized methodologies for assessing and guiding the maturity of an organization’s digital transformation capabilities within the maintenance domain. Understanding the current level of digital maturity in maintenance is essential for these organizations to identify their strengths, weaknesses, and opportunities for improvement. Compared to existing maturity models, the proposed MDTMM offers a more comprehensive and empirically validated framework that integrates multiple dimensions of digital transformation within the maintenance context.
The model’s development involved a thorough review of existing maturity models guidelines and digital transformation research and frameworks in order to identify key dimensions and indicators crucial for assessing MDT readiness. These include strategic, technological and managerial capabilities, organizational culture aspects, and human factors, all of which play significant roles in the successful implementation of digital maintenance strategies. The iterative refinement process, including expert consultations and statistical validations, ensured the model’s relevance, robustness, and applicability across various industrial contexts. The expert feedback was invaluable in highlighting areas for improvement, leading to more tailored and detailed formulations within the maturity grid. Moreover, the detailed maturity grid provides organizations with clear guidelines on their current digital maturity level and actionable recommendations for advancing to higher stages of maturity. It also serves both as a diagnostic tool and a strategic roadmap to empower maintenance organizations in making their digitalization efforts successful. Furthermore, the empirical validation, conducted to purify and test the maturity dimensions and subdimensions and to perform a first layer of expert validation of the model, demonstrates its robustness and versatility, making it a valuable asset for organizations at different stages of their digital journey.
From a theoretical perspective, this study advances the emerging body of knowledge on MDT by empirically validating a multidimensional framework of assessing its maturity. While previous studies have often examined individual aspects of digital maintenance, such as technological adoption or predictive maintenance capabilities, the results of this research demonstrate that MDT maturity emerges from the interaction of multiple organizational, technological, and managerial dimensions. By integrating these elements into a unified maturity assessment model, this work contributes to a more holistic conceptualization of digital transformation in maintenance operations.
From a practical standpoint, the proposed maturity model provides maintenance organizations with a structured tool for assessing their current level of digital transformation and identifying priority areas for improvement. The maturity grid developed in this study translates the multidimensional concept of MDT into clear maturity levels and operational indicators that can support maintenance managers and decision-makers in planning digital initiatives, aligning technological investments with organizational capabilities, and systematically advancing their digital maintenance strategies. The model is particularly useful in helping managers sequence improvement efforts across dimensions and adapt those priorities to the specific operational context of their sector.
While this study lays a solid foundation, several areas warrant further exploration to enhance the model’s applicability and effectiveness. Firstly, future research should explore the adaptation of the model for specific industries, considering unique challenges and requirements. Tailoring the model to sectors such as manufacturing, healthcare, or transportation could enhance its relevance and impact. Secondly, research should also investigate how to customize this comprehensive model to various contexts and company sizes, including SMEs, to ensure its scalability and adaptability across diverse organizational environments. Thirdly, conducting longitudinal studies to track organizations’ progression through the maturity levels over time would provide valuable insights into the model’s effectiveness and the long-term benefits of MDT initiatives. Future research could further investigate the relationship between MDT maturity and organizational performance, providing insights into how improvements across maturity dimensions translate into enhanced maintenance and operational outcomes, as well as explore extensions of the model through AI-driven maintenance systems. Furthermore, comparative studies across different industries could help identify sector-specific challenges and best practices to enhance the model’s generalizability and applicability. In addition, as digital technologies continue to evolve rapidly, ongoing research is needed to update and refine the maturity model and ensure that it remains relevant and incorporates the latest advancements. Finally, the empirical validation is based on cross-sectional survey data, which captures MDT maturity at a specific point in time, and although the sample reflects multiple regions and sectors, potential geographical concentration, sector-specific differences, and cultural influences may affect the generalizability of the findings. Future research could further address these limitations by incorporating richer contextual data, linking maturity assessments with performance outcomes, and refining the model to better account for context-specific organizational and cultural factors.
Footnotes
Acknowledgement
The authors sincerely thank the respondents to the survey and consulted experts for their contribution to the success of this research.
Ethical considerations
Approved by the Institutional Review Board of the American University of Sharjah.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
Data are available upon reasonable request after publication of the paper.
Appendix
Table A.1. Maturity grid formulations.
Dimension
Indicator
1. Initial
2. Managed
3. Defined
4. Quantitatively managed
5. Optimizing
Inspiring references
DSA: Digital Strategy - Strategic Alignment
DSA.1
No formal strategy for maintenance digitalization. Digital initiatives are ad-hoc and uncoordinated.
Basic strategy exists but lacks comprehensive planning and support from top management. Efforts are somewhat coordinated.
A well-defined strategy is in place, supported by top management. Clear goals and objectives are set.
Defined strategy is regularly reviewed and updated based on data and feedback. There is a strong alignment with organizational objectives and thorough planning.
Strategy is continuously optimized for maximum impact. Innovative approaches and continuous improvements are integrated into the strategic framework.
93–100
DSA.2
Developments are undertaken without any strategic assessment of gaps and needs.
Some developments are aligned with basic strategic goals, but not systematically assessed.
Developments are consistently aligned with defined strategic goals following thorough strategic assessments.
All developments are strategically assessed and aligned. Continuous monitoring ensures alignment with evolving strategic goals.
Strategic alignment is part of a continuous improvement process. New developments are proactively aligned and optimized for strategic fit.
DSA.3
No shared vision for digital maintenance. Employees are unaware or unengaged with digital initiatives.
Basic efforts to communicate the vision exist but are sporadic and not fully effective.
A clear and compelling vision for digital maintenance is communicated and shared across the organization.
The vision is regularly reinforced and integrated into daily operations. Employees are engaged and understand their role in achieving the vision.
A strong, shared vision drives continuous innovation. Employees are highly motivated and actively contribute to advancing the digital maintenance vision.
TML: Top Management and Leadership Skills
TML.1
Leaders lack a clear vision for digital transformation in maintenance.
Leaders have a general idea of the importance of digital transformation, but no specific vision or plan.
Leaders have developed a clear vision for digital transformation in maintenance and have communicated it to the organization.
Leaders make sure that vision for digital transformation is regularly communicated and updated, and progress towards the vision is tracked and measured.
Leaders made the vision for digital transformation fully embedded in the organization’s culture and used to drive continuous improvement and innovation.
101–104
TML.2
Limited or no involvement from top management in digital maintenance initiatives.
Some involvement from top management, but commitment is inconsistent and support is limited.
Top management is actively involved in and committed to digital transformation efforts, providing resources and support.
Top management regularly reviews progress towards digital transformation goals and adjusts their approach as needed.
Top management is fully committed to digital transformation, championing the effort and ensuring its success.
MPR: Management Practices
MPR.1
Maintenance objectives are not clearly defined or aligned with business goals.
Maintenance objectives are somewhat defined, but not consistently measured or tracked.
Maintenance objectives are clearly defined and aligned with business goals, with metrics in place to track progress.
Maintenance objectives are regularly reviewed and adjusted based on performance data, with a focus on continuous improvement.
Maintenance objectives are continuously optimized. Performance management tools are advanced, and continuous improvement is part of the organizational culture.
105–112
MPR.4
Business processes are not adapted to support digital transformation.
Some processes are reengineered, but efforts are sporadic and lack strategic direction.
Business processes are reengineered to align with maintenance objectives. Strategic direction is provided but not consistently followed.
Business process reengineering is systematically conducted and aligned with strategic objectives. Continuous monitoring and improvement are in place.
Business processes are continuously reengineered and optimized. Strategic direction is clear, and innovative approaches are regularly adopted.
MPR.5
No formal risk management techniques are in place.
Basic risk management techniques are adopted, but not systematically applied.
A formal risk management process is in place to identify and mitigate risks associated with digital transformation.
Risks are regularly monitored and assessed, and the risk management process is continuously improved.
The organization has a proactive risk management culture. Innovative approaches are adopted to manage risks effectively.
MPR.6
Roles and responsibilities are not clearly defined or communicated.
Some roles and responsibilities are defined, but there is inconsistency in their application and communication.
Roles and responsibilities are clearly defined, documented and communicated across the organization.
Roles and responsibilities are regularly reviewed and adjusted as needed to ensure alignment with organizational objectives.
The organization has a flexible and adaptable workforce, with clear roles and responsibilities that can evolve as needed.
ODC: Organizational Development & Change Management
ODC.2
Change is not managed or communicated effectively, leading to resistance and confusion.
Basic change management practices are in place, but they are not consistently applied or followed. Resistance to change is reduced but still present.
Formal and well-defined change management processes are established and consistently applied.
The change management process is regularly reviewed and improved, and its effectiveness is measured and tracked.
Innovative approaches are used to manage change, the organization is highly adaptable and resilient, and employees are actively involved in the change process.
113–119
ODC.3
Changes are implemented in an ad hoc and reactive manner, without a clear plan or strategy.
Some planning is done before implementing changes, but it is not comprehensive or systematic.
Changes are implemented using a structured and planned approach, with clear goals, timelines, and milestones.
The planned change approach is regularly reviewed and refined, and its effectiveness is measured and tracked.
Innovative and proactive planning of changes is embedded in the organizational culture to ensure high adaptability and resilience.
OCL: Organizational Culture
OCL.1
The organization lacks a digital culture, with limited awareness or interest in digital technologies. Decision making is based on intuition and experience.
Basic awareness of digital culture exists. Data-driven decision making is sporadic and not standardized.
A robust digital culture is promoted. Data-driven decision making is encouraged and supported by top management.
Digital technologies and data-driven decision making are integrated into daily work processes. Regular feedback mechanisms ensure ongoing improvement.
The organization has a strong digital culture, with employees actively seeking out and adopting new technologies to drive innovation and to guide their decisions.
120–128
OCL.2
There is no formal process for continuous improvement, and initiatives are implemented in a reactive manner.
Basic efforts to promote continuous improvement exist, but cultural resistance remains.
A culture of continuous improvement is promoted and supported by top management. Employees are encouraged to participate in improvement initiatives.
Continuous improvement is quantitatively managed. Regular assessments and feedback mechanisms ensure ongoing improvement and adaptation.
Continuous improvement is a core value of the organization, and it is used to drive innovation and excellence.
OCL.5
Employees are not or rarely involved in decision-making processes. Decision making is centralized.
Basic efforts to involve employees in decision making exist, but practices are not standardized and their input is not always valued or acted upon.
Employees are actively involved in decision-making processes. Decentralized daily decision making is encouraged and supported by top management.
Employees are empowered to make decisions and take initiative. Regular feedback mechanisms ensure ongoing improvement and adaptation.
Employees are fully engaged and empowered, with a strong sense of ownership and responsibility for their work. Decentralized decision is embedded in the organizational culture.
KMS: Knowledge Management & Workforce Skills Development
KMS.1
No formal training or development programs for employees on digital technologies and maintenance trends.
Some ad-hoc training is provided, but it is not systematic or comprehensive.
A formal training and development program is in place, covering both technical and soft skills related to digital transformation.
The training and development program is regularly updated to reflect changing needs and technologies. Its effectiveness is measured and tracked.
Staff exhibits advanced digital competencies. Continuous learning and upskilling are embedded in the organizational culture.
129–137
KMS.3
The organization struggles to attract and retain employees with the skills needed for digital transformation.
Basic efforts to attract new talents exist but are not standardized or strategic.
Strategic efforts to attract and hire new talents with digital expertise. Recruitment processes are well-defined and supported by management.
A robust talent management program is in place to identify and develop high-potential employees with digital skills.
The organization is highly attractive to top talents with digital skills. Recruitment and retention strategies are continuously optimized and aligned with industry trends.
KMS.4
Training and development programs are not tailored to the specific needs of the organization or its employees.
Some customization of training programs is done, but it is not based on a systematic needs assessment.
Training and development programs are tailored to the specific needs of the organization and its employees, based on a comprehensive needs assessment.
The effectiveness of the defined training and development programs is regularly evaluated and adjusted as needed.
Learning programs are continuously optimized to address emerging trends. The organization has personalized and individualized learning approaches.
TMA: Technology Management Aspects
TMA.2
Systems and technologies are not integrated, leading to data silos and inefficient workflows.
Some integration exists, but it is limited. Basic use of compatible technologies. Initial efforts to adopt standardized solutions and identify specifications for interface compatibility.
Systems and technologies are integrated using standardized protocols and interfaces, enabling data sharing and collaboration. Specifications for interface compatibility are well-defined and implemented.
The level of integration is regularly monitored and improved, with a focus on reducing complexity. Regular assessments ensure technologies and tools remain compatible and standards are up-to-date.
The organization excels in using interoperable and compatible technologies. It has a seamless and integrated technology ecosystem, with data and flowing freely between systems and applications.
15,138–143
TMA.3
Technologies are introduced without considering the company’s context and are chosen based on hype or personal preference, rather than a careful assessment of needs. Unnecessary complexity is common.
Some consideration is given to needs, but technology choices are not always optimal. Basic efforts to avoid unnecessary complexity.
Technologies are chosen based on a thorough assessment of needs and a clear understanding of the business case. Efforts are made to avoid unnecessary complexity and exploit the right technology at the right time.
Technology choices are regularly reviewed and updated to ensure they remain aligned with the organization’s goals and priorities.
The organization is constantly evaluating and adopting new technologies that are optimized and fully appropriate to its context to stay ahead of the competition and drive innovation.
TMA.4
The organization lacks access to adequate technical support from technology vendors.
Basic technical support is available, but it is not always timely or effective.
The organization has a formal agreement with technology vendors for technical support, with clear service level agreements (SLAs).
Technical support is regularly evaluated and feedback is provided to vendors to ensure that the services are aligned with organizational needs.
The organization has a strong partnership with technology vendors, with regular collaboration and knowledge sharing.
TMA.5
Cybersecurity is not a priority, and there are no formal policies or procedures in place.
Some basic cybersecurity measures are in place, but they are not comprehensive or regularly updated.
A formal cybersecurity and data protection program is in place, with policies, procedures, and training for employees.
The cybersecurity program is regularly reviewed and tested, and its effectiveness is measured and tracked.
The organization has a robust cybersecurity culture, with employees actively involved in protecting the organization’s data and systems.
DMA: Data Management Aspects
DMA.1
Data is collected and processed in an ad hoc and manual manner, with limited visibility or control.
Some basic data collection and processing tools are used, but the process is not standardized or efficient.
Data is collected and processed in a standardized and automated way. A well-integrated Big Data system covers all aspects of the data value chain from generation and acquisition until analytics.
Data is used to drive decision-making, with regular reviews and adjustments to the data management processes.
The organization has a data-driven culture, with employees actively using data to improve their work. Data is collected and processed in real-time, enabling agile decision-making and continuous improvement.
53,140,144–150
DMA.3
Limited attention to data quality. Data quality is poor, with frequent errors, inconsistencies, and missing values.
Basic standards for data quality are established. Initial efforts to ensure data accuracy, comprehensiveness, clarity, and applicability.
Well-defined quality standards for data are in place. Data quality is ensured through completeness, accuracy, timeliness, availability, and consistency.
Data quality is continuously monitored and improved, with a focus on accuracy, completeness, and timeliness.
The organization has a high level of trust in its data, with a strong focus on data quality and integrity. Data is used to drive innovation and competitive advantage.
DMA.4
Data collection and processing solutions are inflexible and cannot easily be scalable and adapt to changing needs.
Some flexibility exists, but it is limited and requires significant effort to implement changes.
Data collection and processing solutions are modular and scalable, allowing them to adapt to changing needs and volumes of data.
The flexibility and scalability of data solutions are regularly assessed and improved, with a focus on agility and responsiveness.
The organization excels in maintaining flexible and scalable data collection solutions, continuously optimizing to meet changing demands.
DMA.5
The organization lacks the right infrastructure to support advanced data management and analytics.
Some basic infrastructure is in place, but it is not sufficient to support advanced analytics.
The organization has invested in advanced data management and analytics infrastructure, including data warehouses and analytics tools.
The data infrastructure is regularly updated and expanded to support new technologies and applications.
The organization has a state-of-the-art data infrastructure, enabling it to leverage the full power of its data to drive innovation and competitive advantage.
INI: Internal Integration
INI.1
Departments operate in silos. Limited integration of operational functions. Data sharing and collaboration between departments are minimal.
Basic level of data sharing and interdepartmental collaboration. Initial efforts to integrate maintenance with operations, IT, engineering, R&D, and purchasing.
Formal processes and systems are in place to facilitate collaboration and communication between departments. Maintenance works closely with other departments for joint decision-making and knowledge sharing.
Collaboration is measured and tracked, with regular reviews and adjustments to improve effectiveness.
The organization has a culture of collaboration and teamwork, with departments working together seamlessly to achieve common goals.
132,151–154
INI.3
Minimal collaboration between employees with up-to-date competencies and those with traditional expertise.
Basic efforts to facilitate this collaboration exist, but they are not consistent or structured.
Formal mechanisms are in place to facilitate collaboration between employees with different levels of expertise, such as mentoring and knowledge sharing programs.
The effectiveness of this collaboration is measured and tracked, with regular reviews and adjustments to improve knowledge transfer and skill development.
The organization has a culture of continuous learning and development, with employees actively sharing knowledge across different levels of expertise.