This study aims to address the limitations of current Industry 4.0 evaluation approaches, which often rely on a one-size-fits-all philosophy. It introduces a novel situational evaluation method tailored to the unique contexts of organizations, with a focus on aligning Industry 4.0 initiatives with strategic goals.
A case study methodology was employed involving a large multinational company with over 30 subsidiaries in 14 countries across Europe and Asia. The company operates in three core EU industries: automotive, electric power transformers, and construction. The study involved the entire top management team (TMT) in order to comprehensively assess the effectiveness of the proposed evaluation approach.
The results demonstrate that the TMT highly valued the customized nature of the situational evaluation approach. The proposed Industry 4.0 evaluation approach significantly improved the alignment of the organization's strategic objectives between TMT members.
The study provides a novel approach to Industry 4.0 evaluation that starts with selecting and structuring digital maturity dimensions according to the needs, context, and characteristics of a specific enterprise; continues with evaluations of individual TMT use and completes with group discussion to achieve final strategic alignment and address common challenges in Industry 4.0 adoption (TMT alignment, workforce readiness, and technology integration).
1. Introduction
Industry 4.0 is revolutionizing the business ecosystem, with the adoption of various technologies becoming increasingly crucial for enterprise productivity (Córcoles, 2025). These include, among others, the internet of things, digital platforms, social media, artificial intelligence, machine learning, and big data, which have become as essential as electricity in modern organizations (Cascio and Montealegre, 2016; Services, 2017). The integration of these technologies has led to significant shifts at both macro and micro levels, influencing competition mechanisms, industry structures, work systems, business dynamics, processes, routines, and skills (Cascio and Montealegre, 2016; Cortellazzo et al., 2019). However, one should recognize that the implementation of Industry 4.0 comes with several risks and challenges that organizations need to address, such as a lack of expertise and a short-term strategy mindset (Moeuf et al., 2020).
Industry 4.0, which was first publicly introduced at a German industry fair in 2010 (Gölzer and Fritzsche, 2017) was later formalized in 2011 through governmental programs aimed at enhancing the competitiveness of the manufacturing industry (Kagermann et al., 2013; Bakhtari et al., 2021). Industry 4.0 represents the integration of the physical and digital worlds, enabling advancements in efficiency, productivity, and customization (Büchi et al., 2020). Similar concepts were developed by the United States, the United Kingdom, France, South Korea, China, and Japan (Liao et al., 2017). Scholars have recognized the importance of digital transformation for enterprises and have inspired employees to embrace change (Gardner et al., 2010; Kirkland, 2014), but they also stress that the implementation of Industry 4.0 poses significant challenges. These include the need for top management team (TMT) support and alignment, a skilled workforce, reeducation of employees, financial constraints, and technology integration and compatibility (Hsu et al., 2017; Kiel et al., 2017; Müller and Voigt, 2017; Erol et al., 2016). While all these aspects need to be carefully considered for Industry 4.0 initiatives to succeed, McKinsey and Company (2021) underscores the critical importance of achieving alignment among TMT. Companies with successful digital transformation initiatives are nearly four times more likely than unsuccessful ones to exhibit a shared sense of accountability for meeting transformation objectives (McKinsey and Company, 2021; Lamarre et al., 2023). The TMT refers to the relatively small group of the most influential executives within an organization, typically comprising the CEO and their direct reports (Bevilacqua et al., 2025).
Additionally, there is no one-size-fits-all Industry 4.0 approach that suits all businesses or industries, meaning the Industry 4.0 evaluation, strategy development, and implementation for each company are idiosyncratic, and should be devised based on a company's core competencies, motivations, capabilities, intent, goals, priorities, and budgets (Ghobakhloo, 2018). Similar positions were taken by Hansen et al. (2024) who argue that technology alone is insufficient for Industry 4.0 in SMEs and stress the importance of context-specific competencies, strategic management capabilities, and the need to adapt to the organization size. Likewise, Baiyere et al. (2025) argue that strategy depends on size, capabilities, and goals. Overcoming these challenges by developing an Industry 4.0 situational approach is crucial for manufacturing industries to reap the benefits of Industry 4.0, including increased productivity, efficiency, safety, customization, and sustainability (Pereira and Romero, 2017; Waris et al., 2018). Although the term Industry 4.0 has been widely used for over a decade, the concept of Industry 5.0 is already emerging, which, according to certain arguments, will offer even more advantages, such as mass production, a symbiotic relationship between humans and robots, intelligent product lines, etc. (Lu, 2025). Bhamriya and Gupta (2025) emphasize that a convincing digital transformation strategy requires a clear understanding of both the current state and the desired future state, underscoring the situational nature of strategic planning. Similarly, Kasapoğlu et al. (2025) identify the assessment of an enterprise's readiness as the first step in the Industry 4.0 transition roadmap, highlighting the need for context-aware diagnostics. Faubel and Schmid (2024) also argue that companies should develop their own approach, depending on their specific situation, further supporting the notion that one-size-fits-all models are insufficient in dynamic industrial environments. These recent contributions reflect the evolving complexity of Industry 4.0 implementation and validate the need for adaptive, context-sensitive evaluation methods.
This paper aims to address the above-mentioned shortcomings and proposes a novel situational evaluation approach that considers enterprise-specific dimensions of Industry 4.0. It poses the following research questions:
How to develop a situational Industry 4.0 evaluation approach?
How to achieve evaluation and goal alignment between different TMT members?
Based on the research questions, this study makes four key scientific contributions:
First, this research proposes a novel situational Industry 4.0 evaluation framework tailored to the specific context and strategic needs of individual organizations, moving beyond generic maturity models.
Second, it integrates the perspectives of the entire TMT, enabling a comprehensive and participatory assessment process that reflects diverse strategic views.
Third, the approach fosters strategic alignment among TMT members, enhancing consensus on digital transformation goals through a structured evaluation and group discussion process.
Fourth, the method is empirically validated within a large multinational company operating in multiple industries and regions, demonstrating its adaptability and practical relevance.
The paper is structured as follows. In the next section of the paper, we present relevant literature from two fields related to our research questions: Industry 4.0 evaluation models (RQ1) and evaluation and goal alignment of TMT (RQ2). In the third section, we present a novel approach for evaluating Industry 4.0 strategies that focuses on building TMT alignment. In the fourth section, we present the case study on which the proposed approach was tested. The fifth section presents the discussion of results and provides implications (both theoretical and practical), while the sixth section concludes the paper, providing research limitations and directions for further research.
2. Literature review
To address RQ1, we conducted a literature review of Industry 4.0 maturity evaluation models, specifically focusing on how these models define maturity levels and the dimensions considered, as well as whether they consider the unique requirements and characteristics of a specific enterprise. The reviewed evaluation models varied in their level of detail and the dimensions they encompassed, reflecting the diversity in conceptualizing Industry 4.0. However, none of them was designed to support different enterprises' contingency factors, since their dimensions and maturity levels are, in their opinion, universal.
The dimensions considered in the identified maturity models encompass different areas within organizations. These areas included the organization, the technology, the product, the operations, the supply chain, and more. Each model provided a unique perspective on Industry 4.0, focusing on various aspects such as technology usage, changes enabled by technologies, and the assessment of different organizational dimensions. Some models focused primarily on technology-related aspects, examining the integration of advanced technologies into production sites and product development processes. In contrast, others took a broader approach, assessing organizational changes and strategic aspects alongside technological advancements. Based on the review, we identified the 10 key Industry 4.0 dimensions as follows.
Leadership and top management support for Industry 4.0 (Antony et al., 2023; Schumacher et al., 2016): Leadership plays a crucial role in guiding the organization through the transformation and in navigating the digital environment (Sony and Aithal, Practical lessons for engineers to adapt towards industry 4.0, 2020). The implementation of Industry 4.0 involves significant changes in business processes, supply chains, strategies, and plans (de Sousa Jabbour et al., 2018). Thus, effective change leadership is essential (By, 2020). The implementation of Industry 4.0 will also result in the reorganization, retraining, and reallocation of employees (Bonekamp and Sure, 2015). Here again, strong leadership and top management support are crucial for the adequate resource allocation, strategic understanding, and tactical decision-making to ensure successful implementation (Sony and Aithal, Practical lessons for engineers to adapt towards industry 4.0, 2020). Additionally, top management support facilitates the acquisition of necessary resources, including facilities, capital, IT, and human resources.
Strategy (Agca et al., 2017; Schuh et al., 2017; Zoubek et al., 2021; Antony et al., 2023): To achieve a successful Industry 4.0 implementation, organizations must deploy technologies, principles, and systems. However, the lack of an implementation model or roadmap poses a challenge (Chiarini et al., 2020). An Industry 4.0 strategy should define plans, goals, and objectives that are aligned with digital transformation and innovation. Clear alignment with broader business and industrial objectives is also essential, as it ensures that transformation efforts support overall organizational direction and value creation (Marcon et al., 2025).
Smart products and services (Anderl and Fleischer, 2016; Agca et al., 2017; Schuh et al., 2017; Zoubek et al., 2021; Antony et al., 2023): Smart products consist of physical, smart, and connectivity components, enabling communication with the product cloud (Porter and Heppelmann, 2015). Smart services involve real-time data collection, continuous communication, and interactive feedback. Smart services can be used to collect data on customers to improve service offerings and provide customized features (Wünderlich et al., 2015).
Processes (Anderl and Fleischer, 2016; Schuh et al., 2017; Agca et al., 2017; Zoubek et al., 2021; Amaral and Peças, 2021): Industry 4.0 requires efficient management of activities, data, information, and knowledge. This dimension assesses how various processes are affected by the implementation of different technologies, and how different departments collaborate with each other (Bauza et al., 2018). There should be a deliberate focus on examining and improving the processes involved in production, supply chain, R&D, and other core processes that can be supported and enabled by digital technology.
People (Schuh et al., 2017; Zoubek et al., 2021; Antony et al., 2023): This dimension includes people-related considerations, indicating the importance of human factors, skills, training, and collaboration in the successful implementation of Industry 4.0 initiatives. Industry 4.0 combines human and non-human components in a socio-technical system (Avis, 2018). Supporting employees in actively engaging in solving production and other challenges can lead to the generation of new ideas and business opportunities that promote innovation and transformation. Moreover, leveraging advanced technologies alongside recognizing and empowering people are crucial factors in facilitating this integration and advancing digital transformation within the sector (Sehnem et al., 2025).
Technology (Schuh et al., 2017; Agca et al., 2017; Zoubek et al., 2021; Antony et al., 2023): Industry 4.0 encompasses various technologies, including IoT, RFID, smart manufacturing, digital twins, cloud computing, and robotics. The readiness of an organization to implement these technologies is crucial (Masood and Sonntag, 2020) as it determines its ability to acquire, develop, customize, and transition to these technologies (Sony and Aithal, Practical lessons for engineers to adapt towards industry 4.0, 2020).
Organization (Schuh et al., 2017; Agca et al., 2017; Zoubek et al., 2021; Antony et al., 2023): The organization needs to enable the utilization of digital technologies to radically transform various functional departments and the systems and processes associated with them, including production, purchasing, marketing, accounting, HR, and finance (Sony and Aithal, 2020a, b). The digital transformation of an organization refers to the extent to which digital technologies are integrated into all aspects of its operations (Verhoef et al., 2021). This transformation brings about fundamental changes in the organization's day-to-day activities (Schwertner, 2017). Unlike mere changes in individual processes or tasks, the digital transformation of an organization has a comprehensive impact on the entire organization (Sony and Aithal, 2020a, b).
Culture (Schuh et al., 2017; Anderl and Fleischer, 2016; Zoubek et al., 2021): Industry 4.0 needs a supportive organizational culture that fosters innovation, adaptability, collaboration, and a willingness to embrace technological advancements. In addition to technical requirements, the critical role of change management as a key enabler of successful digitization is also discussed in the literature (Sordan et al., 2025). This means that underscoring cultural readiness must be paired with active efforts to guide and support organizational transformation.
Information systems (Schuh et al., 2017; Santos and Martinho, 2020; Anderl and Fleischer, 2016): Information systems have a key role in the context of Industry 4.0, enabling vertical and horizontal integration within a company to facilitate the seamless integration of systems across various organizational levels and departments. Additionally, it promotes horizontal integration throughout the entire value chain, ensuring smooth coordination and collaboration between different stakeholders. An additional crucial aspect is the provision of information security to ensure secure and reliable IS operations, as well as standardizing communication interfaces to facilitate the efficient and effective exchange of information.
Employee remuneration (Sony and Aithal, 2020a, b; Antony et al., 2023): A remuneration system that rewards and recognizes employees' contributions to implementing Industry 4.0 will increase the likelihood of successful Industry 4.0 adoption. As discussed by (Liu et al., 2025), employees are more willing to put in effort when they clearly understand how their work contributes to meaningful outcomes. Moreover, in the context of digital transformation and environmental innovation, engagement strengthens when organizations connect sustainability initiatives with visible and tangible rewards.
A review by Zoubek et al. (2021) found that there are strong similarities between different maturity models in terms of the levels of measurement of different dimensions. Most models use ordinal scales with four to six levels, simply depicting the order of levels rather than the differences between each level. Examples of the lowest-level designations are Outsider, Digital novice, Initial, Beginner, Undefined, Novice, Absence, etc. Examples of the highest-level designations include Top Performer, Digital-Oriented, Optimizing, Expert, Intelligent, Excellence, Integrated, Ready, Autonomy, etc. Moreover, recent literature has advanced the understanding and classification of digital maturity models in the context of Industry 4.0 and Industry 5.0. For example, Zineb et al. (2024) offer a comprehensive framework that synthesizes existing research on maturity models, providing a structured foundation for future development. (Kucińska-Landwójtowicz et al. (2024) identify key areas of organizational maturity model evolution, classify current approaches, and highlight research gaps relevant to both academic inquiry and managerial practice. Queiroz et al. (2025) contribute a technology-centric perspective by categorizing Industry 4.0 technologies into four clusters, namely, industrial operations, industrial network architecture, object communication, and intelligence tools. Their work also emphasizes the underdevelopment of routinization and continuance phases, suggesting areas for further exploration. In comparison, Zare et al. (2025) present a systematic review of the challenges SMEs face in implementing Industry 5.0, identifying barriers across six pillars: technology, workforce, economy, environment, community, and organization. These studies collectively underscore the need for adaptable and context-sensitive maturity models that reflect the evolving technological landscape and the diverse realities of organizational transformation.
In the context of Industry 4.0, where project execution relies on interdependent tasks carried out by TMT members and other stakeholders, alignment plays a pivotal role in the success of these complex initiatives (Humphrey and Aime, 2014; Raveendran et al., 2020). Moreover, recent Industry 4.0 research highlights the importance of explicit strategic alignment mechanisms such as frameworks that systematically guide project prioritization and facilitate coordination across human and technological domains to enhance implementation success (Raddi-Mira et al., 2024; Serey et al., 2023).
To address RQ2, we break down the concept of alignment into three core components: agreement between TMT members, TMT's understanding of their roles and responsibilities, and TMT's commitment.
Regarding TMT agreement and goal setting, the studies show that agreement during the evaluation phase ensures sustained focus on an initiative's core objectives, thereby enhancing strategic clarity (Yang et al., 2004). Similarly, setting clear goals and ensuring alignment between individual and team goals positively influences performance outcomes (Yan and Dooley, 2013; Kozlowski and Bell, 2013; Ergene et al., 2023; van der Hoek et al., 2016).
Regarding TMT understanding of their roles and responsibilities (McKinsey and Company, 2021), research shows that it is critically important to get top management alignment right for such projects to succeed. Alignment means more than agreement; without it, top management teams cannot understand their respective roles and what they need to do, which is important since most digital transformation projects require tight cross-functional collaboration. Companies reporting successful digital transformation efforts are nearly four times more likely than those reporting unsuccessful efforts to have a shared sense of accountability for meeting the digital transformation's objectives (McKinsey and Company, 2021; Lamarre et al., 2023).
Regarding TMT commitment, studies show that when TMT members share a unified vision for strategic Industry 4.0 initiatives, they are more likely to secure organizational support and resources (Lechner and Floyd, 2012). Conversely, misalignment among TMT members undermines cohesion and resource commitment, hindering implementation (Lechner and Floyd, 2012; Walter et al., 2016; Ergene et al., 2023).
3. Proposed approach
To address RQ1 and RQ2, we have developed an approach that results in a situational Industry 4.0 digital maturity evaluation model tailored to the specific needs of the enterprise, with the goal of strengthening TMT alignment in evaluation and in strategic Industry 4.0 projects. This, in turn, will increase the organization-wide legitimacy of the Industry 4.0 strategy. The proposed approach is based on TMT alignment literature and existing Industry 4.0 evaluation maturity models. It includes the following steps (see Figure 1):
The structured framework is divided into two main horizontal sections, with a participant legend on the right. At the top is a large rounded rectangle titled “Situational evaluation”. Inside it, on the left, a smaller rounded box is labeled “Selection and structuring of I4.0 digital maturity dimensions and development of situational questionnaire”, illustrated with a group-of-people icon representing a T M T and a single-person icon representing an I4.0 Expert, and marked “Step 1”. To its right, another rounded box is labeled “Execution and analysis of survey”, illustrated with a group-of-people icon representing a T M T, a group-of-people icon representing key middle managers and a single-person icon representing an I4.0 Expert, and marked “Step 2”. Below, a second large rounded rectangle is titled “T M T alignment and formulation of key I4.0 projects”. Inside it, on the left, a rounded box is labeled “Personalized individual assessments”, illustrated with multiple single-person icons representing individual members of T M T, individual key middle managers and an I4.0 Expert , and marked “Step 3”. To its right, a larger rounded box is labeled “Achieving group consensus on current I4.0 dimensions maturity and key I4.0 projects for its improvement”, illustrated with a group-of-people icon representing a T M T, a group-of-people icon representing key middle managers and a single-person icon representing an I4.0 Expert inside a circular arrow, and marked “Step 4”. On the far right, a vertical column titled “Participants:” lists three items with icons: “Top management team” (group-of-people icon), “Key middle managers” (group-of-people icon), and “I4.0 experts” (single-person icon).The proposed approach consists of four key steps, divided into two phases. The situational evaluation phase begins with the selection of Industry 4.0 digital maturity dimensions relevant to a specific organization and concludes with the execution and analysis of individual TMT (Top Management Team) surveys. The TMT alignment and formulation of the key Industry 4.0 projects phase starts with personalized individual assessments and ends when group consensus on the dimensions' maturity is achieved, and key Industry 4.0 projects are defined. Sources: Authors’ own work
The structured framework is divided into two main horizontal sections, with a participant legend on the right. At the top is a large rounded rectangle titled “Situational evaluation”. Inside it, on the left, a smaller rounded box is labeled “Selection and structuring of I4.0 digital maturity dimensions and development of situational questionnaire”, illustrated with a group-of-people icon representing a T M T and a single-person icon representing an I4.0 Expert, and marked “Step 1”. To its right, another rounded box is labeled “Execution and analysis of survey”, illustrated with a group-of-people icon representing a T M T, a group-of-people icon representing key middle managers and a single-person icon representing an I4.0 Expert, and marked “Step 2”. Below, a second large rounded rectangle is titled “T M T alignment and formulation of key I4.0 projects”. Inside it, on the left, a rounded box is labeled “Personalized individual assessments”, illustrated with multiple single-person icons representing individual members of T M T, individual key middle managers and an I4.0 Expert , and marked “Step 3”. To its right, a larger rounded box is labeled “Achieving group consensus on current I4.0 dimensions maturity and key I4.0 projects for its improvement”, illustrated with a group-of-people icon representing a T M T, a group-of-people icon representing key middle managers and a single-person icon representing an I4.0 Expert inside a circular arrow, and marked “Step 4”. On the far right, a vertical column titled “Participants:” lists three items with icons: “Top management team” (group-of-people icon), “Key middle managers” (group-of-people icon), and “I4.0 experts” (single-person icon).The proposed approach consists of four key steps, divided into two phases. The situational evaluation phase begins with the selection of Industry 4.0 digital maturity dimensions relevant to a specific organization and concludes with the execution and analysis of individual TMT (Top Management Team) surveys. The TMT alignment and formulation of the key Industry 4.0 projects phase starts with personalized individual assessments and ends when group consensus on the dimensions' maturity is achieved, and key Industry 4.0 projects are defined. Sources: Authors’ own work
First, a list of all possible Industry 4.0 digital maturity dimensions, based on existing Industry 4.0 evaluation models, is prepared and presented to TMT digitalization leaders (TMT members with the most expertise in digitalization). This list includes maturity dimensions and subdimensions from existing models such as: resource allocation, strategic understanding, tactical decision-making, top management support, human resources, Industry 4.0 investments, innovation management system maturity, real-time data collection, continuous communication, collaboration between departments, human factors and skills, digital transformation of different functions, horizontal integration throughout the entire value chain, etc. In the discussion, TMT digitalization leaders can select and/or add (sub)dimensions as they see fit. TMT digitalization leaders share their knowledge and opinions on which dimensions to include in the evaluation and how to structure them. The goal of this process is to select and structure the dimensions of a situational digital maturity evaluation model, aligning it with the unique requirements and characteristics of a specific enterprise. Through this process, we prepare a situational questionnaire in collaboration with TMT digitalization leaders adjusting the questions and their scales to the company-specific terminological vocabulary.
Second, a survey with TMT members overseeing the Industry 4.0 strategy formulation and implementation is conducted, and their evaluations and alignments are analyzed. For a systematic analysis, the computation of four metrics is required.
The first metric represents the average individual evaluation of TMT members of all different dimensions of digital maturity (TMTi) and is calculated according to the following formula:
where ei,d denotes TMT member i evaluation of digital maturity dimension d, and n denotes the number of digital maturity dimensions.
The second metric represents the combined TMT members' average evaluation of a specific dimension of digital maturity (DDMd) and is calculated according to the following formula:
where ei,d denotes TMT member i evaluation of digital maturity dimension d, and m denotes the number of TMT members.
The third metric represents individual TMT member standard deviation for all digital maturity dimensions (SDTi). It is calculated by comparing individual member evaluation of digital maturity and the combined TMT members' average evaluation of digital maturity for every digital maturity dimension according to the following formula:
where ei,d denotes TMT member i evaluation of digital maturity dimension d, and n denotes the number of dimensions of digital maturity.
The fourth metric represents the combined TMT members standard deviation for a specific dimension of digital maturity (SDDd). It is calculated by comparing individual TMT members' evaluations of digital maturity and the combined TMT members' average evaluation of digital maturity for a specific dimension according to the following formula:
where ei,d denotes TMT member i evaluation of digital maturity dimension d, and m denotes the number of TMT members.
These metrics are used to create a table of misalignments between individual and average TMT members' evaluations. The last two columns present DDMd and SDDd for each digital maturity dimension d. The last two rows present TMTi and SDTi. The other cells present individual evaluations of digital maturity dimensions for each TMT member (ei,d). Asterisks are used to mark those evaluations (ei,d) that exceeded one (*) or two (**) SDDd, and hashes are used to mark those evaluations that exceeded one (#) or two (##) SDTi. Table 1 shows an example of such a table of misalignments.
Evaluations of Industry 4.0 maturity dimensions by individual TMT (Top Management Team) members
| BM1 | BM2 | (CIO) | (COO) | (CSO) | (CCO) | (CHRO) | DCOO | DDMd | SDDd | |
|---|---|---|---|---|---|---|---|---|---|---|
| Capability | 21%*# | 31%# | 61%# | 19%*# | 51% | 62%# | 51% | 73%*## | 46% | 20% |
| Strategy | 37%*# | 66% | 70%*# | 65% | 51% | 43%*# | 61% | 61% | 57% | 12% |
| Organization | 70% | 63% | 57% | 78%*# | 55% | 62% | 50%*# | 65% | 63% | 9% |
| Processes | 11%*# | 17%# | 17%# | 28% | 56%*# | 33% | 56%*# | 39% | 32% | 17% |
| Information Systems | 31%* | 33%* | 36% | 57%*# | 41% | 44% | 44% | 47% | 42% | 8% |
| Culture | 75% | 65% | 60% | 60% | 85%*# | 60% | 70% | 70% | 68% | 9% |
| Control | 0%*# | 17% | 17% | 17% | 17% | 17% | 42%**# | 17% | 18% | 11% |
| Smart products and services | 0%*## | 6%*## | 25% | 30% | 58%*## | 33% | 40% | 44%# | 30% | 19% |
| TMTi | 31% | 37% | 43% | 44% | 52% | 44% | 52% | 52% | ||
| SDTi | 14% | 11% | 10% | 14% | 14% | 9% | 12% | 9% |
Note(s): * - exceeding one SDDd, ** - exceeding two SDDd, # - exceeding one SDTi, ## - exceeding two SDTi , BM1 (Board member 1 - responsible for digitalization), BM2 (Board member 2 - responsible for R&D) CIO: Chief Information officer, COO (Chief Operating Officer), CSO (Chief Sales Officer), CCO (Chief Control Officer), CHRO (Chief Human Resource Officer), DCOO (Deputy COO)
In this way, we further develop the measurement of TMT (mis)alignment between TMT members by clearly defining two dimensions of (mis)alignment. The first dimension (SDDd) measures the spread of TMT members views from the average TMT member view for a specific dimension. The second dimension (SDTi) measures the spread of individual TMT member evaluations from average TMT member evaluations. This enables us to capture the misalignment that is a consequence of very different individual TMT views as well as misalignment that is a consequence of broad TMT member's differences in views for a specific digital maturity dimension.
Third, after completing the survey, individual feedback sessions are organized with each survey participant. The feedback sessions involve discussions focused on the individual's survey responses, including their evaluation of the dimensions' maturity and their alignment with other TMT members' evaluations. This enables a personalized evaluation of each participant's position, improves alignment, and identifies areas with the best improvement potential (low-hanging fruit). Every interviewee is specifically asked if any SDTi or SDDd evaluation that exceeds one standard deviation represents a serious misalignment of views that has to be discussed with other TMT members (step 4). Alternatively, they can adjust their view to align with the average TMT evaluation that is presented to them.
Fourth, after individual feedback, we organize a focus group discussion to gain shared feedback. The focus group discusses the average survey results and their standard deviation, as well as anonymized individual feedback from the previous step. This collaborative discussion helps improve alignment, builds a common view of the issues, and enhances understanding of the collective view of the enterprise's digital maturity, as well as areas of opportunity and concern within the Industry 4.0 strategy. By employing a collaborative and tailored evaluation approach, TMT can make informed decisions and achieve consensus on key strategic Industry 4.0 directions and projects.
4. Case study of the proposed approach
4.1 Case study description and research methodology
We used a single case study as defined by Yin (2009) to evaluate the proposed approach and answer the two posed research questions. The study was conducted from Q4 2019 to Q1 2020 in a large multinational company (more than 5,000 employees) that spans three industries: automotive, electric power transformers, and construction. The company was founded over 50 years ago and has an established history and reputation in Central Europe, which is still its primary market. The company's total sales in 2020 were approximately 1 billion EUR. The headquarters are located in Slovenia, with more than 30 subsidiaries in 14 countries in Europe and Asia. The studied enterprise primarily sells business-to-business. It faces the typical digitalization challenges for a large multinational company, such as inadequate skills for digital transformation, insufficient investment in digital transformation, a shortage of expertise in Industry 4.0, a traditional organizational structure and processes, and a lack of existing smart products and services. An exploratory single case study design was employed to assess the proposed evaluation approach (Runeson and Höst, 2009). Such a design is appropriate (Yin, 2009) since typical digitalization challenges of the studied company represent an everyday or commonplace situation in larger multinational companies.
This study did not require approval from the Institutional Review Board at the time of data collection, according to the legislation of the Republic of Slovenia and internal acts of the University of Ljubljana. Nevertheless, ethical standards were strictly followed throughout the study. Respondents participated in the case study on a voluntary basis. No personally identifiable information was collected, nor is it reported in this paper. Respondents were presented with the aim and a broad overview of the study before taking the survey. The study did not involve misleading participants in any way, and it did not inflict any harm. Moreover, participants did not receive any incentives for taking part in the case study.
The board of directors selected eight top management members, including two board members, to be part of the TMT overseeing digitalization. Their average age was around 50 years. The TMT comprised three women and five men. The TMT members had extensive experience in conducting strategic projects and were well-acquainted with one another.
To collect the required data and triangulate the results, the TMT digitalization leaders (the board member responsible for digitalization and the CIO - Chief Information Officer) helped with the selection and structuring of Industry 4.0 digital maturity dimensions relevant to their company. Based on this selection, questionnaires for the survey were developed, which were then completed by each member of the TMT to evaluate the current situation in the selected Industry 4.0 maturity dimensions of the company. After analyzing the survey results, we conducted individual interviews with the TMT members, during which we discussed their evaluation of the dimensions' maturity and compared it with the average TMT digital maturity dimension evaluation. This enabled a personalized evaluation of each participant's views.
Finally, we held a workshop to facilitate discussion and significantly improve TMT consensus concerning the evaluation and strategic goals. The TMT discussed the average survey results, their standard deviations and anonymized individual feedback from the previous step. This discussion helped improve the collective understanding of the enterprise's digital maturity and helped identify areas of opportunity and concern within the Industry 4.0 strategy. The study results were presented to and validated by the TMT.
4.2 Step 1: selection and structuring of Industry 4.0 digital maturity dimensions and development of situational questionnaire
The TMT digitalization leaders who participated in a workshop led by Industry 4.0 experts were presented with the ten digital maturity dimensions described in the literature section. This presentation included different types of scales and levels that can be used to measure the different dimensions. They decided not to use the ten presented dimensions in their original form but to customize them to better fit the company history and their strategic views. They agreed that the topics covered by the ten dimensions should all be included in the evaluation. However, they modified the way they were grouped together. The dimensions of strategy, processes, smart products and services, and information systems remained unchanged. They split leadership and top management support for Industry 4.0, as they considered leadership to be part of the culture. They joined top management support, organization, and employee remuneration in a common dimension of organization, since the board member responsible for digitalization was accountable for all of these dimensions. They divided the people dimension into culture (collaboration) and capability (skills and competencies), with one TMT member responsible for culture (Chief Human Resource Officer - CHRO) and another for capability management (Chief Information Officer - CIO). The technology dimension was also considered primarily a capability issue and was merged into the capability dimension. They also introduced a new dimension: control. Control was considered an important issue requiring its own dimension, as the TMT team emphasized the importance of measuring the success of the Industry 4.0 implementation. Individual questions always measured the digital maturity of a specific element in a dimension. Because, in the opinion of TMT leaders, some elements have a different number of levels of digital maturity, scales with varying numbers of levels were used. To calculate the overall maturity level of a specific dimension, a normalized weighted average was used. Weights were by default equal across elements. The experts offered the TMT team the option to adjust the weights according to the importance of specific elements, but they decided not to use this option.
4.3 Step 2: execution and analysis of survey
The survey was conducted between November and December 2019. Before the start of the survey, the TMT team was presented with the survey and the terms used. They also had an option to contact consultants during the survey if any additional questions arose. The survey results are presented in Table 1.
As shown in Table 2, initially, there was considerable misalignment between the opinions of TMT members. The biggest differences in the maturity perceptions were in capability, strategy, processes and smart products and services dimensions. The TMT members who often exceeded at least one standard deviation were: board member 1 with six important misalignments, CHRO (Chief Human Resource Officer) with three important misalignments, one of which exceeded two standard deviations, CSO (Chief Sales Officer) and COO (Chief Operating Officer) each with three important misalignments. Others had two or fewer misalignments.
Initial and final Industry 4.0 dimensions' maturity evaluations. The two maturity evaluations that were changed after TMT (Top Management Team) alignment are bolded
| Initial evaluation | Evaluation after TMT alignment | |
|---|---|---|
| Capability | 46% | 46% ○ |
| Strategy | 57% | 47% ↓ |
| Organization | 63% | 63% ○ |
| Processes | 32% | 32% ○ |
| Information Systems | 42% | 42% ○ |
| Culture | 68% | 68% ○ |
| Control | 18% | 18% ○ |
| Smart products and services | 30% | 46% ↑ |
Note(s): ○ (no change, same value), ↓ (decrease), ↑ (increase)
4.4 Step 3: personalized individual evaluation
Next, each TMT member was individually presented with the survey results. Specifically, for each dimension, they were shown their individual evaluations and the deviation of their individual evaluations from the TMT team's average. In general, the TMT members were surprised by the perceived large differences in individual evaluations in certain dimensions of digital maturity. They expected to hold similar opinions and that there would be only minimal differences, since they know each other well and often collaborate. Thus, the fact that they were not considerably more aligned in four of the eight dimensions was unexpected.
Board member 1's (responsible for digitalization) evaluation was, on average, significantly more pessimistic than the average evaluation of the TMT team. For the control dimension, the board member was willing to accept the average evaluation, as they were less familiar with the control dimension than other TMT members. Similarly, the board member accepted the average evaluation for information systems, as they thought the difference was not significant or consequential for the company. For the remaining four dimensions with the largest misalignments, the board member, however, was unwilling to accept the average evaluation and wanted to hear the explanations from the other TMT members behind their differing views.
Board member 2's (responsible for R&D) evaluation was also relatively pessimistic compared to other TMT members. For the information systems dimension, the board member was prepared to change their view and accept the average TMT members' evaluation, as the board member's daily work did not directly involve the information system dimension. The board member was surprised by the other three misalignments and wanted to discuss them with other TMT members.
CIO agreed that the capability dimension evaluation was too optimistic and accepted the TMT average evaluation. The CIO considered it likely that in other departments, Industry 4.0 capabilities are less mature. The CIO wanted to discuss the other two dimensions in which the CIO's opinions differed significantly from those of the average TMT member.
COO agreed that the initial evaluation of the organization's and information system's digital maturity was too optimistic and accepted the average assessment provided by TMT members. The COO explained that in the initial evaluation of the information systems and organization dimensions, the focus was mostly on a comparison to direct competition, rather than on Industry 4.0 potential. However, the misalignment in capability was something to be discussed with other TMT members.
CSO agreed that the initial evaluation of culture was too optimistic, as it was mainly based on personal experience in the sales department. However, after the CSO saw the average TMT members' evaluation, the CSO agreed that most likely in other departments, the situation was not as good. CSO wanted to discuss the misalignments in processes and smart product and services dimensions with other TMT members.
Since CCO's opinion regarding the strategy dimension differed significantly from the average TMT member evaluation, the CCO wanted to discuss it. The CCO was prepared to change the view regarding the capability dimension, as it was likely that Industry 4.0 capabilities in the CCO's department are more mature than average.
CHRO did not expect the average TMT members' evaluation to be considerably higher than that for the HRM department. The CHRO agreed to adjust the evaluation to match the TMT members' average, since the CHRO trusted the evaluation of other TMT members and was not an expert in the field of organization. The CHRO's evaluation of control was considerably more optimistic than the average TMT members' evaluation. The Industry 4.0 experts clarified that the CHRO was the only one to state that the enterprise had a set of indicators that enable it to monitor Industry 4.0 implementation. After this was brought to the CHRO's attention, the CHRO agreed that this was probably a misunderstanding regarding the situation in the control dimension and accepted the average evaluation of the TMT members. Regarding the processes dimension, the CHRO was not willing to change the evaluation and wanted to discuss this misalignment with other TMT members.
Deputy COO agreed to adjust the evaluation of the smart products and services dimension to the TMT members' average evaluation. However, the deputy COO was not willing to adjust the capability evaluation and wanted to discuss this misalignment with other TMT members.
4.5 Step 4: achieving group consensus on the current industry 4.0 dimensions maturity and identifying key projects for its improvement
To achieve group consensus, the TMT first discussed the four maturity dimensions (one half) with the lowest alignment (highest std. dev.) that are presented in Figure 2 (marked with triangles -▲).
The horizontal axis is labeled “Average maturity score by T M T” and ranges from 0 percent to 80 percent in increments of 20 units. The vertical axis is labeled “T M T alignment (standard deviation)” and ranges from 0 percent to 25 percent in increments of 5 units. The graph shows 8 maturity dimensions represented as individual data points plotted using two marker types: triangle markers representing the maturity dimensions with low alignment and circle markers representing maturity dimensions with high alignment, with each data point labeled by its factor name. Triangle markers are used for “Smart products and services”, “Processes”, “Capability”, and “Strategy”, while circle markers are used for “Control”, “Information Systems”, “Organization”, and “Culture”. The approximate percentage coordinates of the data points are as follows: “Smart products and services” lies at (30 percent, 19 percent), “Processes” lies at (32 percent, 17 percent), “Capability” lies at (46 percent, 20 percent), “Strategy” lies at (57 percent, 12 percent), “Organization” lies at (63 percent, 9 percent), “Culture” lies at (68 percent, 9 percent), “Control” lies at (18 percent, 11 percent), and “Information Systems” lies at (42 percent, 8 percent). Note: All numerical data values are approximated.Industry 4.0 dimensions maturity and alignment. Source: Author's own work. The vertical axis shows TMT (Top Management Team) alignment, while the horizontal axis shows evaluated dimension maturity. Triangles (▲) represent the maturity dimension with low alignment (high std. dev.), while circles (●) represent the maturity dimension with high alignment (low std. dev.). Sources: Authors’ own work
The horizontal axis is labeled “Average maturity score by T M T” and ranges from 0 percent to 80 percent in increments of 20 units. The vertical axis is labeled “T M T alignment (standard deviation)” and ranges from 0 percent to 25 percent in increments of 5 units. The graph shows 8 maturity dimensions represented as individual data points plotted using two marker types: triangle markers representing the maturity dimensions with low alignment and circle markers representing maturity dimensions with high alignment, with each data point labeled by its factor name. Triangle markers are used for “Smart products and services”, “Processes”, “Capability”, and “Strategy”, while circle markers are used for “Control”, “Information Systems”, “Organization”, and “Culture”. The approximate percentage coordinates of the data points are as follows: “Smart products and services” lies at (30 percent, 19 percent), “Processes” lies at (32 percent, 17 percent), “Capability” lies at (46 percent, 20 percent), “Strategy” lies at (57 percent, 12 percent), “Organization” lies at (63 percent, 9 percent), “Culture” lies at (68 percent, 9 percent), “Control” lies at (18 percent, 11 percent), and “Information Systems” lies at (42 percent, 8 percent). Note: All numerical data values are approximated.Industry 4.0 dimensions maturity and alignment. Source: Author's own work. The vertical axis shows TMT (Top Management Team) alignment, while the horizontal axis shows evaluated dimension maturity. Triangles (▲) represent the maturity dimension with low alignment (high std. dev.), while circles (●) represent the maturity dimension with high alignment (low std. dev.). Sources: Authors’ own work
The breakthrough in alignment for the capability dimension occurred when the Deputy COO recognized that the COO and board member 1 concentrated on older production lines that still accounted for a majority share of the company's revenue, whereas the Deputy COO focused on new production lines. The final consensus took into account the average capability maturity of all production lines.
The focus group discussion on the misalignment of the process dimension maturity revealed that board member 1, board member 2, and the CIO evaluated the processes based on all technologies available in the market, so their process dimension maturity evaluations were relatively low. The other evaluators limited themselves to those technologies that the company could finance. This resulted in significantly higher evaluations of process dimension maturity. To alleviate such discrepancies in the future, we improved the question to better specify which technologies it referred to. The TMT agreed to exclude financially inaccessible technologies from the evaluation. Additionally, during the focus group debate, the CSO and the CHRO acknowledged that they had overly focused on their respective areas in the process maturity evaluation, which led them to overestimate the dimensions' maturity. These insights enabled the TMT team to reach a consensus on the overall process maturity within the company.
During the discussion of the strategy maturity dimension evaluation, board member 1 and the CCO convinced the rest of the team that the maturity of the strategy should be lowered by 10% from their original average evaluation, mainly due to relatively low levels of implementation and investment in Industry 4.0.
The focus group discussion of the smart products and services dimension led to another change in the dimension maturity evaluation. The CSO persuaded other TMT members that the dimension was more mature relative to the main competitors than they initially thought, as the company is already negotiating with customers about the development of digital services that rely on the data collected during manufacturing and during the customers' use of products.
The final dimension maturity evaluation, following the TMT alignment, is presented in Table 2.
The shared understanding of the current Industry 4.0 situation as a result of the alignment process enabled the TMT to more easily develop and prioritize key strategic Industry 4.0 goals for the improvement of Industry 4.0 maturity and to order them by the following priority:
Digital modelling will be implemented with the aim of reducing inventory and increasing production efficiency.
Integration of the backend systems has to be improved, especially concerning the closing feedback loops in data collection. This is a requirement for developing digital twins.
Introduction of Industry 4.0 cost-effective improvements on existing non-digitalized production lines, like video surveillance of products and machines.
Automatization of internal logistics compatible with the Industry 4.0 approach.
Enhancement of systematic knowledge management and of Industry 4.0-related training.
Speeding up the process of substituting unqualified jobs by introducing collaborative robots.
Clearly define the responsibilities of each TMT member in the smart products and services dimension, since the focus group discussion showed that most TMT members do not clearly understand their responsibilities regarding this dimension.
Conduct a feasibility study on new Industry 4.0 digital services for customers such as remote maintenance for customers, digital modelling for customers and digital twins for customers.
With this list, the TMT achieved alignment on key strategic Industry 4.0 goals. This enabled the TMT to start the process of detailed planning and implementation. Table 3 presents Industry 4.0 strategic objectives aimed at enhancing targeted aspects of digital maturity.
Strategic Industry 4.0 goals with expected positive impact on specific dimensions of digital maturity
| Capability | Strategy | Organization | Processes | Information systems | Culture | Control | Smart products and services | |
|---|---|---|---|---|---|---|---|---|
| Digital modelling | ✔ | ✔ | ✔ | ✔ | ✔ | |||
| Integration of backend systems | ✔ | ✔ | ✔ | |||||
| Improvements on existing non-digitalized production lines | ✔ | ✔ | ✔ | ✔ | ||||
| Automatization of internal logistics | ✔ | ✔ | ✔ | |||||
| Systematic knowledge management and Industry 4.0-related training | ✔ | ✔ | ✔ | |||||
| Introducing collaborative robots | ✔ | ✔ | ✔ | |||||
| Define the responsibilities of each TMT member in the smart products and services dimension | ✔ | ✔ | ✔ | ✔ | ||||
| Feasibility study on new Industry 4.0 digital services for customers | ✔ | ✔ |
5. Discussion
The presented Industry 4.0 situational evaluation approach is our response to RQ1 (How to develop a situational Industry 4.0 evaluation approach?). Its main theoretical advancement compared to existing models is its ability to adapt to specific enterprise-related circumstances and contingencies. To achieve this, we included the TMT in the first evaluation step, i.e. the selection and structuring of Industry 4.0 digital maturity dimensions and development of situational questionnaire. In this step, they were presented with all possible digital maturity dimensions, educated about what they entail and how to measure them. This empowered them to better understand the Industry 4.0 evaluation process and to modify the evaluation to better fit their organizational goals and processes than they could in existing one-size-fits-all models. The TMT was very satisfied with the high customization of the evaluation that our approach enabled. The fact that the TMT took the time needed to adapt the starting ten Industry 4.0 digital maturity dimensions to better fit their enterprise is an important indication of the value of our approach. Furthermore, the TMT noted that active involvement in the customization of the evaluation approach, where the Industry 4.0 evaluation maturity dimensions were aligned with the responsibilities of specific TMT members, considerably increased their engagement and buy-in throughout the entire evaluation and goal-setting process.
The practical contribution of our evaluation approach, which addresses RQ2 (How to achieve evaluation and goal alignment between different TMT members?), is the proposed process for evaluating and aligning goals among TMT members. Steps 2, 3, and 4 of our approach (see Figure 1) present how such TMT alignment is efficiently achieved. First, we analyzed misalignments based on survey evaluation results (step 2). Next, personalized individual evaluations (step 3) of Industry 4.0 digital maturity dimensions were conducted to understand where the TMT is (mis)aligned. Lastly, focus group discussions were held (step 4), where group consensus was achieved on the current maturity of the Industry 4.0 dimensions, and key strategic goals were identified. The TMT confirmed that these three steps importantly improved alignment and that without them, they would not have been able to agree on the list of eight Industry 4.0 strategic goals. Additionally, they noted their surprise that it took only one focus group to achieve such a wide consensus. While the approach was developed and tested within a single case study, its core principles, like structured evaluation, personalized evaluation, and facilitated consensus-building, are potentially transferable to other organizational contexts. Since the execution of our approach (surveys, interviews, and focus group discussions) is inexpensive, it can be easily replicated in small and medium enterprises (SMEs), service-oriented sectors, or public institutions, where decision-making structures and digital maturity challenges may differ, but the need for TMT alignment remains equally critical.
6. Conclusion
The main contribution is a novel approach to situational evaluation in Industry 4.0. A case study was employed to confirm the effectiveness and efficiency of the proposed approach; both research questions were successfully addressed, and the benefits of the proposed approach were presented.
Two main limitations of the conducted study, however, need to be considered. The first limitation concerns external validity, namely, generalization. We conducted only a single case study with a single TMT team. To mitigate this, we selected a large multinational company that consists of three divisions in the core EU industries: automotive, electric power transformers and construction. It has more than 30 subsidiaries in 14 countries across Europe and Asia. Nevertheless, additional case studies in various industries (IT, finance, defense, etc.) would help confirm the broader usefulness of the approach as well as strengthen its theoretical and practical implications. The second limitation is the practical impossibility of formalizing the focus group discussion (step 4) to produce key must-have Industry 4.0 implementation projects, since experts' and managers' interpretations are needed in order to produce the final insights and recommendations.
Future work should expand the number of case studies in order to test the replicability of the results in different cultural settings. Furthermore, the approach should be broadened to include not only the evaluation and goal-setting phases of Industry 4.0, but also the implementation and maintenance phases. Moreover, future research could also benefit from expanding beyond the implementation and maintenance phases to explore more innovative ways. One promising direction involves conducting comparative multi-case studies across different industrial sectors, which would allow for a deeper understanding of how contextual factors influence the adoption and effectiveness of Industry 4.0 solutions. Additionally, cross-cultural validations could help assess the generalizability of the proposed framework in diverse organizational and socio-economic environments. A further promising area of work involves leveraging AI-based maturity assessment tools, which may improve the responsiveness and accuracy of evaluation frameworks by utilizing real-time operational data and intelligent analytics. Such integration could significantly enhance the adaptability of evaluation processes, making them more aligned with the dynamic and data-rich environments characteristic of Industry 4.0.
We sincerely thank Valter Leban and Maja Leban for their support during the case study. Without their support, the successful completion of the case study would not have been possible. We would also like to thank the Editor for handling our paper and the reviewers for their insightful comments, which helped to improve the paper.
