Transitioning to Industry 4.0: an expert-based assessment of critical success factors in large manufacturing companies in Brazil

Abstract

Paper aims  This study evaluates the readiness of large Brazilian manufacturing companies (LBMCs) for Industry 4.0 (I4.0) transition by assessing the extent to which critical success factors (CSFs) are addressed to enable digital transformation.

Originality  Moving beyond technology-centered perspectives, this paper advances the literature by examining technical-managerial readiness for I4.0 transition within an emerging economy. It reframes CSFs as organizational capabilities and shifts the analytical focus from factor identification to their implementation and institutional embedding.

Research method  A survey was conducted with twenty experts in the Brazilian manufacturing sector. The collected data were analyzed using a multicriteria decision-making approach suitable for information-constrained environments, and interpreted through a capability- and socio-technical lens.

Main findings  Most experts classify LBMCs as either superficially (35%) or reasonably (60%) prepared for I4.0 transition. Strategic–governance factors show comparatively higher levels of institutionalization, whereas organizational and technological capabilities remain less consolidated.

Implications for theory and practice  This study advances knowledge by reconceptualizing CSFs as organizational capabilities and shifting the analytical focus from factor identification toward their implementation and institutionalization within large manufacturing firms in emerging economies. It provides practical guidance for managers and policymakers to strengthen governance, workforce development, and coordinated digital transformation efforts.

Keywords:
Industry 4.0; Digitalization; Critical cuccess factors; Manufacturing; Grey systems

1. Introduction

The fourth industrial revolution has emerged as a defining transformation in contemporary manufacturing, reshaping how firms configure strategies, redesign production systems, and govern digitally integrated operations (Ghobakhloo & Iranmanesh, 2023; Ostadi et al., 2024). Industry 4.0 is increasingly understood not merely as the adoption of advanced technologies, but as a systemic organizational transformation that requires alignment between technological infrastructure, strategic intent, and managerial capabilities (Kakouris et al., 2025; Khan et al., 2025). This perspective positions digital transformation as a capability-building process rather than a purely technological upgrade.

Recent empirical research highlights that many Industry 4.0 initiatives fail to deliver sustained value due to organizational misalignment, insufficient governance structures, and limited managerial readiness (Pandey et al., 2025; Alsehaimi & Sanni-Anibire, 2026). These findings reinforce the argument that successful digital transformation depends not only on technological investments, but also on leadership commitment, workforce engagement, structured change management, and strategic coherence. From this viewpoint, Industry 4.0 implementation is fundamentally a socio-technical transition that requires coordinated development of organizational capabilities (Ostadi et al., 2024).

In response to these challenges, the literature has increasingly focused on identifying critical success factors (CSFs) associated with Industry 4.0 adoption. Recent studies have proposed structured sets of managerial and organizational factors such as strategic alignment, top management support, skills development, supply chain integration, and sustainability orientation (Affaki et al., 2025; Beltran-Salomon et al., 2025; Hou et al., 2026). Complementary research has also emphasized the importance of integrating sustainability dimensions into digital transformation strategies (Datta et al., 2025).

While these contributions have advanced the identification and categorization of CSFs, they have also generated a fragmented landscape characterized by overlapping constructs and heterogeneous conceptualizations. More importantly, much of the recent literature remains concentrated on listing, ranking, or modeling CSFs, with limited attention to assessing the degree to which such factors are effectively institutionalized within large manufacturing firms. In other words, the transition from factor recognition to capability embedding remains underexplored.

This gap is particularly salient in emerging economies. Studies conducted in Brazil and other developing contexts demonstrate that structural constraints (e.g., resource limitations, skill shortages, and institutional barriers) significantly affect Industry 4.0 adoption trajectories (Cordeiro et al., 2024; Hossain et al., 2023). These contextual conditions suggest that insights derived from advanced economies may not fully capture the organizational realities faced by firms in emerging markets. Consequently, there is a need for context-sensitive analyses that examine not only which factors matter, but how deeply they are embedded within organizational structures and practices.

Drawing on a capabilities perspective (Ostadi et al., 2024), this study conceptualizes Industry 4.0 CSFs as manifestations of underlying organizational capabilities that must be developed, coordinated, and institutionalized to sustain digital transformation. Rather than evaluating the theoretical importance of CSFs, the study focuses on assessing their perceived level of implementation in large Brazilian manufacturing companies (LBMCs). By doing so, it addresses a critical gap between conceptual identification of enabling factors and empirical evaluation of managerial readiness.

Accordingly, the objective of this study is to assess the perceived level of implementation of ten managerial and organizational CSFs associated with Industry 4.0 transition in large Brazilian manufacturing companies, based on structured expert evaluation. This study contributes to the Industry 4.0 literature in three interrelated ways. First, it advances theoretical understanding by reframing CSFs as capability manifestations embedded in governance routines and organizational structures. Second, it shifts the analytical focus from factor identification or prioritization to the evaluation of institutionalization levels, thereby bridging the gap between recognition and operational embedding. Third, by examining large manufacturing firms in an emerging economy, the study introduces contextual boundary conditions that enrich capability-based interpretations of digital transformation.

The remainder of the paper is structured as follows. Section 2 presents the theoretical background on Industry 4.0 and associated managerial CSFs. Section 3 describes the methodological procedures adopted. Section 4 discusses the empirical findings. Section 5 outlines theoretical and practical implications, followed by conclusions and directions for future research.

2. Theoretical background

2.1. Industry 4.0 as a socio-technical transformation

Industry 4.0 was initially conceptualized as the integration of cyber-physical systems, industrial internet of things, automation, and advanced analytics into manufacturing environments (Xu et al., 2018). Early debates emphasized technological disruption and productivity gains, positioning digitalization as a new paradigm for industrial competitiveness (Dalenogare et al., 2018).

Over time, however, the understanding of Industry 4.0 has evolved from a technology-centric narrative toward a systemic transformation perspective. Contemporary research highlights that digital technologies function as enabling infrastructures whose performance depends on strategic alignment, governance mechanisms, and human capability development (Vial, 2019; Hughes et al., 2022). In this sense, Industry 4.0 is better understood as a socio-technical transformation process in which technological change is intertwined with organizational redesign and managerial adaptation.

Empirical studies increasingly show that technological investment alone does not guarantee transformation success. Digital complacency, resistance to change, and misalignment between strategy and operational routines may undermine competitiveness even in technologically advanced firms (Ahmad & Neely, 2022). Similarly, research on implementation patterns indicates that firms often adopt isolated digital technologies without achieving systemic integration (Frank et al., 2019). These findings reinforce that Industry 4.0 readiness is shaped by managerial orchestration rather than technological sophistication alone.

This socio-technical interpretation becomes particularly relevant in manufacturing contexts characterized by complex production systems, long capital cycles, and multi-tier supply chains. In such environments, digital transformation requires coordinated strategic decisions, workforce development, and sustainability integration (Abubakr et al., 2020; Ghobakhloo, 2020). Consequently, the central analytical challenge shifts from “which technologies to adopt” to “how organizational capabilities enable sustained digital transformation”.

2.2. Critical success factors (CSFs) in Industry 4.0 implementation

The identification of CSFs has become a central approach in understanding Industry 4.0 implementation. Early reviews (Sony & Naik, 2020; Moeuf et al., 2020) consolidated managerial, technological, and environmental factors influencing digital transformation. Since then, the literature has expanded considerably, offering sector-specific and cross-sector analyses.

Recent empirical studies confirm persistent convergence around core managerial dimensions. For example, Bhatia & Kumar (2022) identify leadership commitment, training programs, and change management as central CSFs in the automotive industry. Affaki et al. (2025) highlight the interaction between lean management, quality standards, and Industry 4.0 technologies in achieving operational excellence. Alsehaimi & Sanni-Anibire (2026) similarly emphasize governance, strategic clarity, and organizational readiness in construction contexts. Khan et al. (2025) demonstrate the centrality of managerial coordination and capability development in aerospace and defense industries.

At the same time, the CSF discourse has expanded to incorporate sustainability and circular economy dimensions. Datta et al. (2025), Pandey et al. (2025), and Hou et al. (2026) show that digital transformation increasingly intersects with environmental and circular performance outcomes. These contributions suggest that sustainability integration is no longer peripheral but structurally embedded within Industry 4.0 success frameworks.

Despite this growing convergence, fragmentation persists. Studies differ in terminology, measurement scales, and analytical focus. Some emphasize sector-specific configurations (Beltran-Salomon et al., 2025), while others integrate lean thinking and quality management perspectives (Kakouris et al., 2025; Tortorella et al., 2022). Although this plurality enriches the field, it complicates cumulative theory development.

More importantly, much of the literature continues to focus on identifying or prioritizing CSFs rather than evaluating their degree of institutionalization within firms. Even maturity-based models (Bueno et al., 2025) primarily assess technological and operational sophistication, leaving managerial embeddedness underexplored. This distinction between factor identification and capability institutionalization remains insufficiently addressed.

2.3. The connection between CSFs and organizational capabilities

Recent theoretical developments suggest that CSFs should be interpreted not as isolated enablers, but as manifestations of deeper organizational capabilities. From a capabilities perspective, sustained competitiveness depends on a firm's ability to sense opportunities, seize them through coordinated action, and reconfigure resources in response to environmental turbulence (Ostadi et al., 2024).

Applied to Industry 4.0, this implies that top management support reflects governance capability; workforce engagement reflects adaptive learning capability; supply chain digitization reflects integration capability; and sustainability integration reflects strategic reconfiguration capability. These capabilities must be embedded in routines, decision-making processes, and organizational culture to support systemic transformation.

Evidence from emerging economies reinforces this interpretation. Brazilian and other developing-country studies show that firms often initiate digital pilot projects but struggle to institutionalize transformation due to capability gaps and structural barriers (Cordeiro et al., 2024; Hossain et al., 2023; Tortorella & Fettermann, 2018). This pattern aligns with research on digital complacency and resistance to change (Ahmad & Neely, 2022), suggesting that superficial adoption may coexist with limited organizational embedding.

Thus, managerial readiness emerges as a more analytically robust construct than technological maturity alone. Unlike maturity models that emphasize infrastructure sophistication (Bueno et al., 2025), managerial readiness captures the depth of institutionalization of management-related CSFs within organizational structures and governance systems.

This perspective is particularly salient in emerging economies, where structural constraints, resource allocation limitations, and institutional complexity may hinder the consolidation of digital transformation initiatives (Cordeiro et al., 2024). Assessing the perceived implementation level of managerial CSFs therefore provides a nuanced evaluation of Industry 4.0 readiness, bridging the gap between conceptual recognition and operational embedding.

3. Methodology

This study adopts a four-stage research design (Figure 1) combining literature review, expert-based survey, and multicriteria decision analysis, a configuration widely used in studies addressing complex and uncertain organizational phenomena (Zheng et al., 2020; Guo et al., 2022).

Figure 1
Research stages and procedures. Source: Authors.

3.1. Identification of CSF for Industry 4.0 implementation

Given the substantial number of systematic literature reviews already published on CSFs for Industry 4.0 implementation, such as Liao et al. (2017), Sony & Naik (2020), and Khan et al., 2025), this study did not conduct a new PRISMA-based systematic review. Instead, it adopted a structured narrative review approach grounded in the most comprehensive and methodologically rigorous prior synthesis available.

The study by Sony & Naik (2020) was selected as the initial analytical foundation, as it represents a systematic literature review that methodically examined 84 peer-reviewed articles from diverse geographical and industrial contexts. Their framework provided a consolidated taxonomy of managerial and technical CSFs associated with Industry 4.0 implementation.

However, considering the rapid evolution of the Industry 4.0 literature, the original framework was not adopted uncritically. Rather, it was refined and conceptually updated through an examination of recent empirical and theoretical contributions published after 2020. This process ensured that emerging themes (e.g., sustainability integration, digital governance, organizational capabilities, and strategic alignment) were conceptually aligned with contemporary developments in the field.

Through this refinement process, a structured and theoretically grounded set of CSFs for Industry 4.0 implementation was proposed. This consolidated framework served as the conceptual foundation for the empirical stage of the study, ensuring coherence between prior literature and subsequent expert evaluation.

3.2. Expert-based assessment

Based on the structured set of CSFs identified and conceptually grounded in the literature, a survey instrument was developed to collect expert assessments regarding their level of implementation within the Brazilian context, specifically focusing on large manufacturing companies. The target population was explicitly defined in the instrument to ensure that evaluations referred exclusively to large firms, thereby avoiding ambiguity regarding firm size or sectoral scope.

Expert-based research designs are particularly suitable when direct organizational data are difficult to access and when informed judgment is required to synthesize evidence across multiple contexts (Rowe & Wright, 2001). Thus, experts were selected based on experience in Industry 4.0 research theme and applied projects in the Brazilian manufacturing sector. This approach aligns with prior Industry 4.0 studies conducted in emerging economies (Kamble et al., 2018; Tortorella et al., 2022) and considering the characteristics of the selection process, the sample is characterized as non-probabilistic by judgment as explained by (Malhotra, 2012).

The selection of specialists was based on clearly defined expertise criteria aligned with the objectives of the study. Participants were required to demonstrate (i) documented academic research on Industry 4.0, digital transformation, or advanced manufacturing systems, and (ii) familiarity with the Brazilian manufacturing sector through applied projects, consultancy activities, executive training programs, or collaborative research initiatives with large industrial firms. It is important to clarify that the expert panel was not designed to provide statistical representativeness of the population of large Brazilian manufacturing companies. Instead, the study follows an expert-based decision-making logic, in which the relevance of the sample is primarily associated with the depth of knowledge, experience, and strategic perspective of the selected respondents. This approach is consistent with Grey Systems Theory, whose research objects are uncertain systems characterized by partially known information, small samples, and poor information (Liu & Lin, 2010; Liu et al., 2022a). Therefore, the sample was intentionally composed of specialists capable of providing informed judgments about Industry 4.0 implementation in the Brazilian manufacturing context, rather than respondents selected for probabilistic generalization.

A total of fifty-three Brazilian experts who fulfilled the research criteria were invited to take part in the study, of whom twenty consented to participate. Most participants (50%) have between 10 to 20 years of professional experience; 25% of the participants have up to 10 years of professional experience, while the remaining 25% have accumulated more than 20 years of professional experience. Regarding their training level, 100% of them hold a PhD, and 40% are doing a post doctorate.

Although all participants hold academic positions, their selection was based not solely on scholarly credentials, but also on documented engagement with large manufacturing firms through applied research projects, industry partnerships, consultancy activities, and executive training programs related to Industry 4.0 implementation. This dual profile ensures that expert evaluations reflect both theoretical knowledge and practical exposure to organizational realities in large Brazilian manufacturing firms. Nevertheless, we acknowledge that the findings represent informed expert-based assessments rather than direct firm-level measurements, which is discussed in the limitations section.

3.3. Grey Target Decision Method application

The Grey Target Decision Method (GTDM) was employed due to its ability to handle uncertainty, incomplete information, and subjective evaluations, which are inherent in expert-based assessments (Deng, 1982; Liao et al., 2017). This method is based on Grey Systems Theory which is applied to solve multicriteria decision problems and it was introduced by Deng Ju-Long in 1982 focused on the analysis and modeling of systems with uncertain or partial information, described as "grey", in contrast to systems with complete (white) or totally unknown (black) information (Deng, 1982, 1989).

This methodological choice is particularly aligned with the nature of the present research problem. Grey Systems Theory was developed to address uncertain systems involving small samples, poor information, and partially known information, extracting useful information from what is available to support analysis, assessment, and decision-making (Liu & Lin, 2010; Liu et al., 2022a). Thus, the adequacy of the method does not depend on large probabilistic samples, but on the use of knowledgeable decision-makers and clearly structured evaluation criteria. In this study, the twenty experts are treated as qualified decision-makers whose assessments are used to classify the perceived implementation level of Industry 4.0 CSFs through distance from predefined grey target classes. Accordingly, the results should be interpreted as an expert-informed assessment under uncertainty, not as statistical inference about the entire population of large Brazilian manufacturing companies.

In the context of decision making, the Grey Target method offers a way to deal with this type of uncertainty by evaluating alternatives in relation to an ideal target, called a "grey target" (Guo et al., 2022; Liu et al., 2022a, b) and its application was carried out in three steps, as explained below. These steps are supported by various authors (Liu et al., 2022b; Liu & Lin, 2010; Zheng et al., 2020).

Step 1. Define the event, the objectives relevant to the decision and the set of possibilities to be evaluated by the experts. In the sequence, an appropriate group of specialists is formed, and a questionnaire based on a numerical rating scale is designed to capture their assessments of each option. Specifically for this research, the event is to identify how well LBMCs adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts. The objective is to identify the extent each one of the ten CSFs presented by (Sony & Naik, 2020) are implemented by LBMCs in the opinion of experts with knowledge of Brazilian Industry 4.0 scenario. Finally, the set of possibilities to be evaluated by the experts are: a) Most LBMCs still do not work on managing this CSF, at most they only debate the subject (score 1); b) Most LBMCs are working superficially on managing this CSF, and there are many possibilities for improvement (score 2); c) Most LBMCs are working reasonably well on managing this CSF, although there are possibilities for improvement (score 3); d) Most LBMCs are working adequately on managing this CSF, with a few possibilities for improvement (score 4); e) Most LBMCs are working on managing this CSF in a well-structured manner (score 5).

Step 2. Evaluate the input from the experts and determine the distance between the solution vectors and the central reference point. This reference point is determined by identifying the most favorable effect vector within the decision-making framework and designating it as the central reference. For each option, the distance in relation to this central reference point is measured. The magnitude of this distance indicates the quality of the decision vector, enabling the identification of the better decision vector for achieving the better decision. The closer an alternative is to the target, the better its classification.

This step is mathematically described by Zheng et al. (2020), i.e., set the event as $A=\left\{a_1,a_2,\dots ,a_n\right\}$, where $a_i\left(i=\mathrm{1,2,3,}\dots ,n\right)$ represents $i$-th event. Similarly, set the countermeasure $B=\left\{b_1,b_2,\dots ,b_j\right\}$, where $b_j\left(j=\mathrm{1,2,3,}\dots ,m\right)$ represents the $j$-th countermeasure. The Cartesian product of the event set $A$ and the countermeasure set $B$ is given by Equation 1:

$AxB=\left\{\left(a_i,b_j\right)\right|a_i\in A,b_j\in B\}$ (1)

This set is referred to as the decision-making scheme set, where each decision-making scheme $\left(a_i,b_j\right)$ is denoted as $s_{ij}$, represented by Equation 2:

$S=\{s_{ij}=\left(a_i,b_j\right)|a_i\in A,b_j\in B\}$ (2)

Let $S$ denote the set of decision-making schemes and let $u_{ij}^{\left(k\right)}$ represent the effect mapping value of the decision-making scheme $s_{ij}$ with respect to objective $k$. The effect vector of the decision-making scheme $u_{ij}$​ is given by Equation 3:

$u_{ij}=\left[u_{ij}^1,u_{ij}^2,\dots ,u_{ij}^s\right]\in S^s$ (3)

Accordingly, $\text{}b$_j is considered the preferred countermeasure for $a$_i’s with respect to targets 1 through $s$. Thus, if target $k$ is profit-oriented, it means that a higher value of $k$ results in a better effect value, as represented by Equation 4:

$u_{i0j0}^{\left(k\right)}=\frac{max}{1\le i\le n\mathrm{,1}\le j\le m}\left\{u_{ij}^{\left(k\right)}\right\}$ (4)

If the target $k$ is cost-sensitive, is means that a smaller value of $k$ results in a better effect value, as represented by Equation 5:

$u_{i0j0}^{\left(k\right)}=\frac{min}{1\le i\le n\mathrm{,1}\le j\le m}\left\{u_{ij}^{\left(k\right)}\right\}$ (5)

Then, if the target k is balanced, meaning that the better effect is achieved when $k$is closest to a moderate value, then define $r_0=\left[r_0^{\left(1\right)},r_0^{\left(2\right)},\mathrm{...},r_0^{\left(s\right)}\right]$. Let $r_0$ be the better effect vector, represented by Equation 6:

$R^s=\left\{\left(r^{\left(1\right)},r^{\left(2\right)},\dots ,r^{\left(s\right)}\right)\right|{\left(r^{\left(1\right)}-r_0^{\left(1\right)}\right)}^2+{\left(r^{\left(2\right)}-r_0^{\left(2\right)}\right)}^2+\dots +{\left(r^{\left(s\right)}-r_0^{\left(s\right)}\right)}^2\le R^2\}$ (6)

$R^s$ is named the $S$-dimensional spherical grey target, being $r_0=\left[r_0^{\left(1\right)},r_0^{\left(2\right)},\mathrm{...},r_0^{\left(s\right)}\right]$ the central reference point and $R$ the radius.

Then, define $g$ as the distance of vector $r_k$, as represented by Equation 7:

$g=\left|r_k-r_0\right|={\left[{\left(r^1-r_0^1\right)}^2+{\left(r^2-r_0^2\right)}^2+\dots +{\left(r^s-r_0^s\right)}^2\right]}^{\frac{1}{2}}$ (7)

The g value is employed to represent the advantages and disadvantages of the effect vector. In the Grey Target Method, the distance can be measured in terms of Euclidean distance or other appropriate metrics, considering the dispersion of the alternatives in relation to the ideal central reference point.

Step 3. Obtain the better effect vector for each expert based on the grey target calculated distances. The better effect vector will be the one that presents the lowest value of $g$.

The data collected from the expert survey (see Section 3.2) was then analyzed following the described steps of GTDM. Thus, the results and discussions based on the analysis of experts’ responses are presented in the next sections (Stage 4).

4. Results and discussions

4.1. Systematization of CSF for Industry 4.0 implementation

Building upon the conceptual refinement process described in Section 3, this study proposes a structured and theoretically grounded set of ten CSFs for Industry 4.0 implementation. Rather than reproducing fragmented factor lists, the framework consolidates classical foundations of Industry 4.0 (Schwab, 2017; Xu et al., 2018; Liao et al., 2017) with contemporary empirical evidence on implementation barriers, enablers, and organizational readiness (Sony & Naik, 2020; Bhatia & Kumar, 2022; Tortorella et al., 2022; Cordeiro et al., 2024; Khan et al., 2025).

Industry 4.0 has progressively evolved from a technology-centric paradigm toward a socio-technical transformation logic (Pereira & Romero, 2017; Hughes et al., 2022). Empirical studies consistently demonstrate that technological adoption alone does not guarantee performance improvements (Dalenogare et al., 2018; Frank et al., 2019). Instead, performance gains depend on strategic coherence, organizational alignment, and managerial capabilities (Ghobakhloo & Iranmanesh, 2023; Ostadi et al., 2024).

Table 1 presents the consolidated set of CSFs adopted as the analytical foundation of this study. The proposed CSFs are organized into four complementary capability domains:

Table 1
CSFs concerning Industry 4.0 transition.

Code	CSFs for I4.0 transition	Description	Capability domains				References
			SG	OM	TI	SU
C1	Strategic alignment of Industry 4.0 initiatives	Definition of short-, medium-, and long-term Industry 4.0 strategies aligned with the organization’s current capabilities, future positioning, and transformation roadmap.	✔				Ghobakhloo & Iranmanesh (2023); Ostadi et al. (2024); Sony & Naik (2020)
C2	Top management support	Financial, political, and strategic commitment of senior leadership to allocate physical, technological, and human resources necessary for Industry 4.0 implementation.	✔				Bhatia & Kumar (2022); Khan et al. (2025); Cordeiro et al. (2024); Sony & Naik (2020)
C3	Employee engagement and workforce adaptability	Development of employee commitment, adaptability, and skill upgrading to cope with automation, integration of business processes, and evolving digital work environments.		✔			Ahmad & Neely (2022); Hossain et al. (2023); Muscio & Ciffolilli (2020)
C4	Smart product and service development	Integration of sensors, RFID, GPS, and digital connectivity into products and services to enable data generation, monitoring, and real-time performance feedback.			✔		Frank et al. (2019); Meindl et al. (2021); Xu et al. (2018); Sony & Naik (2020)
C5	Smart supply chain integration	Digital integration of supply chain actors using CPS, IoT, and cloud technologies to enable real-time data exchange, coordination, and decision support.					Horváth & Szabó (2019); Tortorella et al. (2022); Beltran-Salomon et al. (2025)
C6	Internal process digitization	Implementation of flexible, modular, and interoperable human–machine interaction systems to enable seamless digital integration of internal operations.			✔		Dalenogare et al. (2018); Frank et al. (2019); Hughes et al. (2022); Moeuf et al. (2020)
C7	Change management capability	Structured management of organizational transformation resulting from vertical, horizontal, and end-to-end digital integration associated with Industry 4.0 adoption.		✔			Tortorella & Fettermann (2018); Kakouris et al. (2025); Sony & Naik (2020)
C8	Project management capability	Systematic application of project management principles to coordinate the increased number and complexity of digital transformation initiatives.		✔			Kakouris et al. (2025); Affaki et al. (2025); Khan et al. (2025); Tortorella et al. (2022)
C9	Cybersecurity governance	Implementation of policies, protocols, and technological safeguards to mitigate risks associated with increased digital connectivity and cyber-physical integration.			✔		Thames & Schaefer (2017); Xu et al. (2018); Sony & Naik (2020); Khan et al. (2025)
C10	Sustainability integration	Alignment of Industry 4.0 initiatives with economic, social, and environmental objectives to ensure long-term sustainable value creation.				✔	Abubakr et al. (2020); Datta et al. (2025); Pandey et al. (2025); Hou et al. (2026)

• Source: Authors.

1. Strategic–Governance (SG);

2. Organizational–Managerial (OM);

3. Technological–Integration (TI);

4. Sustainability-oriented (SU).

This structuring responds to the fragmentation highlighted in prior CSF research (Sony & Naik, 2020; Moeuf et al., 2020; Khan et al., 2025) and aligns with capability-based interpretations of digital transformation (Vial, 2019; Ghobakhloo & Iranmanesh, 2023).

4.1.1. Strategic-Governance dimension

Strategic alignment (C1) and top management support (C2) constitute foundational governance capabilities. Strategic alignment has been repeatedly identified as a determinant of digital transformation success (Ghobakhloo & Iranmanesh, 2023; Tortorella et al., 2022). Without clear alignment between Industry 4.0 initiatives and corporate strategy, firms risk “digital complacency” or superficial adoption (Ahmad & Neely, 2022). In large manufacturing organizations characterized by high capital intensity and long investment cycles, the absence of a structured transformation roadmap generates implementation discontinuities (Ostadi et al., 2024).

Similarly, top management support is consistently recognized as a primary CSF across sectors, including automotive (Bhatia & Kumar, 2022), aerospace (Khan et al., 2025), construction (Alsehaimi & Sanni-Anibire, 2026), and supplier ecosystems (Beltran-Salomon et al., 2025). Executive commitment ensures resource allocation, cross-functional coordination, and long-term continuity, particularly relevant in the Brazilian context, where institutional and financial constraints have been documented (Cordeiro et al., 2024; Ruggero et al., 2021).

4.1.2. Organizational-Managerial dimension

Employee engagement and workforce adaptability (C3), change management capability (C7), and project management capability (C8) represent organizational coordination capabilities. The literature increasingly recognizes that workforce readiness and skills development are decisive for Industry 4.0 success (Cazeri et al., 2022; Muscio & Ciffolilli, 2020). Resistance to change, lack of digital competencies, and insufficient learning mechanisms frequently undermine transformation efforts (Ahmad & Neely, 2022; Hossain et al., 2023). Employee adaptability therefore reflects not merely training initiatives, but the institutionalization of continuous learning systems.

Change management capability (C7) addresses the structural transformation triggered by vertical, horizontal, and end-to-end digital integration (Frank et al., 2019). Empirical studies demonstrate that firms often initiate digital projects without adequate transformation governance, leading to fragmented implementation (Tortorella & Fettermann, 2018).

Project management capability (C8) captures the increasing complexity and simultaneity of digital initiatives. Large firms typically implement multiple interconnected projects involving ERP integration, automation systems, data platforms, and cybersecurity upgrades. Structured project governance has been identified as critical in both lean–Industry 4.0 integration studies (Kakouris et al., 2025; Affaki et al., 2025) and circular transformation contexts (Datta et al., 2025; Pandey et al., 2025).

4.1.3. Technological-Integration dimension

The technological–integration dimension encompasses smart product and service development (C4), smart supply chain integration (C5), internal process digitization (C6), and cybersecurity governance (C9). Smart product and service development (C4) reflects the shift toward cyber-physical systems and connected products (Thames & Schaefer, 2017). Empirical evidence suggests that value creation increasingly depends on the integration of sensors, real-time monitoring, and data-driven services (Meindl et al., 2021).

Smart supply chain integration (C5) aligns with findings that Industry 4.0 implementation extends beyond firm boundaries (Horváth & Szabó, 2019). Digital integration across supply networks enhances coordination and visibility but requires interoperability standards and governance mechanisms.

Internal process digitization (C6) represents the operational embedding of digital technologies within production systems. Studies show that firms adopt Industry 4.0 technologies in distinct implementation patterns, often progressing incrementally (Frank et al., 2019; Dalenogare et al., 2018).

Cybersecurity governance (C9) becomes critical as connectivity increases systemic vulnerability. Digital openness introduces risks that can disrupt operations and compromise data integrity (Thames & Schaefer, 2017). The governance dimension of cybersecurity, rather than its purely technical aspects, determines resilience.

4.1.4. Sustainability dimension

Finally, sustainability integration (C10) operates as a transversal capability that aligns digital transformation initiatives with long-term economic, social, and environmental objectives. The inclusion of sustainability responds to contemporary developments in Industry 4.0 research, which increasingly recognize that digitalization strategies disconnected from sustainable value creation may generate short-term efficiency gains but fail to ensure systemic competitiveness.

4.2. Critical success factors for Industry 4.0 transition in large manufacturing firms in Brazil

The individual responses from each of the twenty experts were consolidated by the moderator and it is presented in Table 2. To preserve the anonymity of the experts, each respondent was designated by a letter. In addition to serving as input for the grey target decision method, a descriptive examination of the individual CSF scores was conducted to identify relative strengths and weaknesses across managerial dimensions.

Table 2
Scores presented by each expert individually.

Code	CSF description	Expert
		A	B	C	D	E	F	G	H	I	J	K	L	M	N	O	P	Q	R	S	T
C1	Strategic alignment of I4.0 initiatives	3	3	2	3	2	3	2	3	2	2	3	2	3	3	2	4	3	2	4	3
C2	Top management support	3	4	2	3	2	4	2	3	3	2	4	2	4	3	2	4	3	3	5	3
C3	Employee engagement and workforce adaptability	2	3	1	3	1	4	2	3	2	1	3	2	2	3	1	2	4	2	4	2
C4	Smart product and service development	2	2	3	4	1	3	1	2	2	2	3	2	3	3	2	4	4	3	4	3
C5	Smart supply chain integration	2	2	2	2	1	3	1	4	2	2	4	2	1	2	3	3	3	4	4	2
C6	Internal process digitization	2	3	3	4	3	3	3	4	2	1	3	2	3	3	2	3	4	3	4	2
C7	Change management capability	3	2	3	3	2	2	3	2	2	1	3	2	3	2	2	2	3	2	4	3
C8	Project management capability	3	4	3	3	3	3	2	4	3	1	5	2	1	3	2	2	3	2	4	4
C9	Cybersecurity governance	3	3	3	3	3	3	3	2	2	1	2	2	1	2	2	3	4	3	5	4
C10	Sustainability integration	2	5	2	2	1	3	1	3	2	2	3	1	2	2	2	2	3	4	4	2

• Source: Authors.

A preliminary comparison of the average scores assigned to each CSF reveals meaningful variation in perceived implementation levels. Factors such as Strategic Alignment and Top Management Support tend to receive comparatively higher scores, indicating stronger recognition and formal commitment at the strategic level. Conversely, Employee Engagement, Sustainability Integration, and Supply Chain Digitization display comparatively lower average scores, suggesting weaker institutionalization of human, environmental, and inter-organizational capabilities. This differentiation allows a more granular interpretation of managerial readiness prior to class allocation.

The following classes were created to calculate the distance vector:

• Class 1) Most LBMCs do not address CSFs for Industry 4.0 transition;

• Class 2) Most LBMCs superficially address CSFs for Industry 4.0 transition;

• Class 3) Most LBMCs reasonably address CSFs for Industry 4.0 transition;

• Class 4) Most LBMCs adequately address CSFs for Industry 4.0 transition;

• Class 5) Most LBMCs address CSFs for Industry 4.0 transition in a structured manner.

In the sequence, the vector of respondents' opinions in relation to the target vector for each class was calculated, as presented in Table 3. As mentioned in Methodological Procedure section and according to the literature (Liu et al., 2022b; Liu & Lin, 2010; Zheng et al., 2020), the smallest value for the vector of respondents' opinions designates the better effect vector which represents the best class for the allocation of the expert's response. The final column of Table 3 presents the best class allocation of each expert response.

Table 3
Values for vector of respondents' opinions in relation to the target vector.

Expert	Class 1	Class 2	Class 3	Class 4	Class 5	Class
A	5.00	3.32	2.24	5.00	8.06	3
B	7.28	5.39	3.00	4.12	6.71	3
C	4.90	3.46	2.83	5.48	8.49	3
D	6.63	4.47	2.00	3.74	6.63	3
E	3.87	3.61	4.36	7.14	10.15	2
F	6.86	4.58	1.73	3.32	6.24	3
G	4.00	3.16	4.00	6.78	9.80	2
H	6.78	4.69	2.45	4.00	6.78	3
I	4.00	2.45	2.83	5.83	8.94	2
J	2.24	1.73	5.00	8.06	11.18	2
K	7.68	5.57	2.65	3.32	5.92	3
L	3.00	1.73	3.61	6.71	9.85	2
M	5.20	3.00	3.87	6.24	9.11	2
N	5.29	3.16	2.00	4.69	7.75	3
O	3.46	2.00	3.46	6.48	9.59	2
P	6.56	4.36	2.65	4.36	7.14	3
Q	7.75	5.48	2.00	2.45	5.29	3
R	6.16	4.00	2.45	4.47	7.35	3
S	10.20	7.87	4.00	1.41	2.83	4
T	6.16	4.69	2.45	4.47	7.35	3

• Source: The authors.

As can be noted, there are seven (7) experts that consider most LBMCs superficially adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts, twelve (12) experts that consider most LBMCs reasonably adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts and one (1) expert that consider most LBMCs adequately adopt relevant them. Therefore, according to 95% of experts, most LBMCs superficially or reasonably adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts. More specifically, 60% of experts consider most LBMCs reasonably adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts and 35% of experts consider most LBMCs superficially adopt relevant management aspects that enhance the transition towards Industry 4.0 concepts. This result underscores a notable opportunity to enhance the implementation of Industry 4.0 in Brazilian manufacturing organizations, particularly in relation to the CFSs outlined in Table 1.

Beyond the overall classification, the results reveal relevant positive and negative highlights regarding the specific CSFs evaluated in this study. The comparatively more favorable assessments are associated with factors related to strategic direction and internal coordination, particularly strategic alignment of Industry 4.0 initiatives, top management support, internal process digitization, and project management capability. These results suggest that Industry 4.0 has gained visibility in the strategic agenda of large Brazilian manufacturing companies and that some internal mechanisms for planning, coordinating, and executing digital transformation initiatives are already in place.

However, these positive highlights should be interpreted with caution. The presence of strategic alignment (C1) and top management support (C2) does not necessarily mean that Industry 4.0 practices are deeply embedded in organizational routines. Rather, it may indicate that digital transformation is formally recognized as a strategic priority, while its translation into operational, cultural, and inter-organizational capabilities remains incomplete. Similarly, internal process digitization (C6) and project management capability (C8) suggest progress in localized initiatives, such as automation, process-level digitalization, ERP integration, data platforms, and project-based coordination. Nevertheless, these initiatives do not necessarily imply systemic integration across functions, supply chains, and sustainability objectives.

The less favorable highlights are mainly associated with employee engagement and workforce adaptability (C3), smart supply chain integration (C5), change management capability (C7), and sustainability integration (C9). These factors represent complex dimensions of Industry 4.0 implementation because they require capabilities that go beyond formal planning and internal technological initiatives. Employee engagement and workforce adaptability (C3) depend on continuous learning, reskilling, and the active involvement of workers in transformation processes. Weaknesses in this dimension may limit the effective use of digital technologies, even when technological infrastructure is available.

Smart supply chain integration (C5) also appears as a critical bottleneck because it requires coordination beyond the boundaries of the focal firm. The digital integration of suppliers, customers, and partners depends on interoperability, trust, data-sharing standards, and governance mechanisms across the value chain. Therefore, weaker consolidation of this CSF suggests that Industry 4.0 transition may still be concentrated within firm-level initiatives, rather than fully extended to inter-organizational ecosystems.

Change management capability (C7) is another relevant concern. The implementation of Industry 4.0 modifies work routines, decision-making processes, organizational structures, and power relations. If change management is not sufficiently institutionalized, digital transformation may remain fragmented and dependent on isolated projects, specific departments, or short-term managerial initiatives. This reinforces the idea that Industry 4.0 readiness cannot be assessed only by the existence of technologies or strategies, but also by the organization’s ability to absorb, routinize, and sustain transformation over time.

Finally, the weaker consolidation of sustainability integration (C10) indicates that the connection between Industry 4.0 and long-term economic, environmental, and social value creation is still not fully embedded in managerial practices. This is a relevant limitation because contemporary Industry 4.0 literature increasingly emphasizes that digital transformation should not be restricted to productivity and efficiency gains, but should also support broader sustainability objectives. In this sense, sustainability integration remains an important frontier for large Brazilian manufacturing companies.

These positive and negative highlights reveal an important structural asymmetry. While strategic-level dimensions appear relatively acknowledged, operational and cultural dimensions remain less consolidated. This pattern indicates that Industry 4.0 transition in large Brazilian manufacturing companies may be characterized by strategic awareness without full operational embedding. From a capability-based perspective, this reflects a gap between strategic intent and the routinization of digital transformation practices, particularly in areas requiring workforce adaptation, cross-organizational coordination, and sustainability integration.

In the Brazilian context, such asymmetry may be influenced by structural constraints commonly observed in emerging economies, including uneven digital infrastructure maturity, skill shortages in advanced manufacturing technologies, and fragmented supply chain integration. These conditions may slow the consolidation of organizational capabilities required for sustained Industry 4.0 implementation. Therefore, the findings do not merely indicate moderate adoption levels, but rather reveal specific capability bottlenecks that may limit deeper institutionalization.

Initially, it is also important to mention that the CSFs analyzed by this research should be disseminated and discussed by directors and senior management of LBMCs so that related initiatives can be considered in the Industry 4.0 implementation process and, in this way, optimize the implementation of Industry 4.0 in their organizations. It is worth mentioning that the transition to Industry 4.0 is not just a strategic milestone for LBMCs, but a necessity to ensure long-term survival and growth for these manufacturing companies to strengthen their competitiveness in the global market (Cordeiro et al., 2024; Thames & Schaefer, 2017; Tortorella et al., 2022).Thus, prioritizing these critical management aspects for the transition toward Industry 4.0 is essential for LBMCs to maximize the benefits of digital transformation, allowing them to become relevant players in an increasingly competitive and technologically advanced market.

From a managerial standpoint, the findings suggest that prioritization should move beyond formal strategic alignment toward strengthening human capital development, structured change management routines, and digitally integrated supply chain coordination mechanisms. These dimensions appear comparatively more fragile and therefore represent leverage points for accelerating Industry 4.0 capability development in large Brazilian manufacturing firms.

The findings also suggest that managerial prioritization should move beyond formal strategic alignment and technology acquisition toward the institutionalization of organizational capabilities. In particular, LBMCs should strengthen human capital development, structured change management routines, digitally integrated supply chain coordination mechanisms, and sustainability-oriented governance. These dimensions appear comparatively more fragile and therefore represent important leverage points for accelerating Industry 4.0 capability development in large Brazilian manufacturing firms.

Emphasizing these CSFs enables LBMCs to develop a more agile and resilient production environment capable of speedily responding to transformation in the market and supply chains (Frank et al., 2019). Additionally, training professionals to handle modern technologies and management practices promotes innovation and fosters a data-driven organizational culture, which is fundamental for success in the new industrial era (Cazeri et al., 2022; Muscio & Ciffolilli, 2020). By improving management processes and implementing robust digital transformation, LBMCs not only enhance operational efficiency but also expand their innovation capacity, adaptability, and long-term sustainability (Hossain et al., 2023).

5. Conclusion

The findings reinforce the argument that Industry 4.0 readiness should be interpreted as a technical-managerial capability, rather than only as a technological state. By conceptualizing CSFs as organizational capabilities, this study contributes to literature by shifting the analytical focus from identification and prioritization to implementation and institutionalization of CSFs, an aspect that remains not widely explored in Industry 4.0 research.

Furthermore, the LBMCs context highlights that digital transformation barriers in emerging economies are not solely infrastructural, but deeply rooted in managerial systems and governance structures, consistent with prior empirical evidence. The results show that, from an expert-based perspective, most Brazilian LBMCs lack the robust infrastructure and strategic alignment necessary to effectively manage the complexities of this digital transformation. Consequently, these companies may face limitations in achieving organizational capabilities as competitiveness, operational efficiency, and innovation. Addressing this challenge requires greater investments in employee training, management skill development, and long-term planning that aligns with Industry 4.0 objectives. Emphasizing a culture of continuous improvement and digital innovation is also essential to mitigate resistance and facilitate smoother transitions.

This research adds value by identifying specific areas where LBMCs fall short in Industry 4.0 readiness and offering insights that can guide academics, researchers and industry practitioners. This study contributes to theory by advancing the understanding of Industry 4.0 CSFs beyond identification and prioritization, positioning them as manifestations of underlying organizational capabilities. By examining the degree of institutionalization of these factors within large manufacturing firms, the research bridges a conceptual gap between recognizing enabling conditions and embedding them into organizational routines and governance structures. In doing so, it enriches capability-based interpretations of digital transformation and introduces contextual boundary conditions related to emerging economy settings. The findings provide a structured foundation for future empirical investigations, enabling researchers to design more refined survey instruments and case studies that examine how organizational characteristics (e.g., firm size, sectoral configuration, and institutional environment) influence the development and consolidation of Industry 4.0 capabilities.

From a managerial perspective, the findings suggest that Brazilian LBMCs should prioritize capability-building efforts in areas that exhibited comparatively lower levels of institutionalization. In particular, firms should invest in structured workforce reskilling programs aligned with digital technologies, formalize change management routines to support cross-functional integration, and strengthen digital coordination mechanisms across supply chain partners. Rather than focusing solely on strategic discourse, isolated pilot projects, or technology acquisition, managers should emphasize embedding Industry 4.0 practices into organizational routines, performance metrics, decision-making processes, and governance structures to ensure sustained transformation.

From a public policy standpoint, the results indicate that accelerating Industry 4.0 adoption requires supportive institutional mechanisms. Policymakers may contribute by expanding incentives for digital infrastructure development, fostering collaborative innovation ecosystems that integrate universities and manufacturing firms, and promoting workforce qualification programs tailored to advanced manufacturing technologies. Additionally, regulatory frameworks that encourage cybersecurity standards and sustainability integration may facilitate more structured and resilient digital transformation across the manufacturing sector.

This study has limitations that should be acknowledged. The research relies on expert-based assessments rather than direct firm-level survey data. Therefore, the findings should not be interpreted as statistically generalizable evidence about all large Brazilian manufacturing companies. Instead, they represent an expert-informed diagnostic assessment based on the judgments of specialists with academic and applied experience in Industry 4.0 and the Brazilian manufacturing sector. Although this design is consistent with Grey Systems Theory and its suitability for uncertain, small-sample, and information-constrained decision problems, future studies could expand the empirical basis by collecting direct firm-level data across different manufacturing sectors.

Future studies can investigate the effectiveness of CSFs practical application in small and medium-sized enterprises across various industrial sectors in Brazil. Comparative studies in different regions or countries would also offer a broader understanding of global trends and unique regional challenges in Industry 4.0 adoption.

Data availability

Research data is only available upon request.

• How to cite this article:
  Cazeri, G. T., Sigahi, T. F. A. C., Rampasso, I. S., Moraes, G. H. S. M., Zanon, L. G., Molenda, P., & Anholon, R. (2026). Transitioning to Industry 4.0: an expert-based assessment of critical success factors in large manufacturing companies in Brazil. Production, 36, e20250104. https://doi.org/10.14488/1980-5411.20250104
• Financial Support
  This research was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq/Brazil) under the grants #303787/2024-4 and #303412/2024-0. The CNPq/Brazil was not involved in the collection, analysis, or interpretation of the data, or the writing of the manuscript.
• Ethical Statement
  The research was approved by the Research Ethics Committee of the Universidade Estadual de Campinas, CAAE: 65069022.9.0000.5404. The authors maintain the informed consent signed by all participants authorizing the publication of the data and the manuscript.

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Edited by

• Editor(s)
  Adriana Leiras
  Rodrigo Caiado

Publication Dates

• Publication in this collection
  10 Aug 2026
• Date of issue
  2026

History

• Received
  07 Nov 2025
• Accepted
  03 July 2026