Modernizing Small and Medium-Sized Enterprises: A Lean Audit Model for Digital Integration

Abstract

This study proposes an audit model to modernize artisanal manufacturing companies and facilitate their transition to Industry 4.0. Based on Lean Manufacturing, Lean Thinking, and Lean Management principles, the model enhances operational efficiency and competitiveness while considering the resource constraints of Small and Medium-Sized Enterprises (SMEs). It provides a structured approach to identifying key improvement areas and guiding digital transformation. The research follows a four-phase methodology: (1) a company assessment questionnaire to diagnose the current state, (2) a method matrix to analyze improvement strategies, (3) a dimension map to structure key transformation areas, and (4) prioritization of improvement dimensions to define a tailored action plan. A case study in an SME validated its applicability. Findings show that the model helps identify critical improvement factors and implement targeted Lean interventions, enhancing Industry 4.0 readiness. It enables a progressive adoption of digital enablers while optimizing traditional manufacturing processes. The originality of this study lies in its integrated auditing framework, structured around four dimensions and twelve key factors. It introduces a 48-question assessment tool, methods matrices, and prioritization mechanisms. Additionally, it defines four strategic development stages—Readiness, Start-up, In-transition, and Advanced—providing a roadmap for continuous improvement in SMEs.

1. Introduction

The current industrial landscape is characterized by rapid advancements and the integration of digital technologies, commonly referred to as Industry 4.0. Industry 4.0 represents a significant transformation that integrates advanced technologies such as the Internet of Things (IoT), artificial intelligence (AI), big data analytics, and cyber–physical systems into manufacturing processes. This transformation aims to enhance efficiency, flexibility, and customization, marking a shift towards smart manufacturing environments [1,2]. Industry 4.0 (I4.0) applies Information and Communications Technology (ICT) comprehensively and integrally within industrial environments, presenting opportunities to leverage the capabilities of the Digital Age in response to new market demands [3,4].

Lean Manufacturing (LM) has been widely recognized for its ability to streamline processes, reduce waste, and improve overall operational efficiency. Originally developed as a set of principles and practices by Toyota, Lean methodologies emphasize the elimination of non-value-adding activities, continuous improvement, and fostering a culture of efficiency [5,6,7]. With the advent of Industry 4.0, the concept of Lean has evolved into Lean 4.0, which combines LM principles with Industry 4.0 technologies to enhance process efficiency further. Lean 4.0 synergizes the strengths of both paradigms, leading to a more advanced stage of manufacturing characterized by the integration of processes, flexibility of the value chain, and increased automation, while maintaining Lean attributes such as continuous value flow and waste elimination [8,9,10,11].

Lean Six Sigma (LSS) further enhances these principles by integrating Lean’s waste reduction focus with Six Sigma’s emphasis on reducing process variation and improving quality [12,13]. Six Sigma methodologies, initially developed by Motorola, are particularly useful in managing technological advancements and high-uncertainty scenarios, facilitating continuous process improvement and performance enhancement [14,15,16]. The integration of Lean, Six Sigma, and Industry 4.0 technologies offers companies a competitive edge by combining process efficiency with advanced technological capabilities, thus enabling more robust and adaptable manufacturing systems [17].

In recent years, the pivotal role of Small and Medium-Sized Enterprises (SMEs) in fostering economic growth and innovation has been widely acknowledged. Despite their significance, SMEs frequently face unique challenges in adopting digital technologies due to resource constraints, limited technological capabilities, and the absence of tailored frameworks. Concurrently, the rapid evolution of digital technologies and the emergence of Industry 4.0 have redefined competitive landscapes across sectors, creating both opportunities and pressures for organizations to adapt. Most existing digital transformation models, however, are designed with large enterprises in mind, leaving a notable gap in addressing the practical needs of SMEs.

Despite the potential benefits, SMEs face unique challenges in adopting Industry 4.0 standards. SMEs often have limited resources in terms of knowledge, experience, and technology, making the transition to Industry 4.0 appear daunting [18,19]. Many SMEs perceive the implementation of Industry 4.0 technologies as complex and costly, exacerbated by a comparative scarcity of expertise and capital [20,21]. Nonetheless, the early adoption of Industry 4.0 can provide strategic advantages, particularly for SMEs that supply larger enterprises, as it facilitates integration into the larger value chain and enhances competitiveness [10,22,23]. SMEs must tailor their Industry 4.0 implementations to their specific circumstances, often focusing on the digitalization of specific operations or parts of their value chain in the initial stages to mitigate risks and manage resource constraints [19,21,24].

The literature review (2) on digital transformation and Industry 4.0 has predominantly focused on large-scale enterprises, leaving a noticeable void in addressing the unique challenges faced by artisanal or traditional manufacturing companies and SMEs [25]. Although numerous frameworks and assessment tools have been proposed, many of these fails to account for the limited resources, distinct operational constraints, and the continuous improvement demands driven by Lean principles that are critical for SMEs [26,27]. Consequently, decision-makers in these sectors lack a cohesive, tailored approach that not only evaluates current digital readiness but also offers strategic guidance for an effective transformation [28]. This gap is further accentuated by the absence of an integrated audit model that systematically combines diagnostic assessments, a methodical analysis of improvement strategies, and structured prioritization mechanisms.

The present study addresses the proposal of an audit model aimed at developing a tool for monitoring the progress of production modernization projects towards Industry 4.0 (I4.0) within companies specializing in traditional manufacturing systems, such as artisanal furniture production. This model is designed to be sufficiently general to inspire future projects and professionals facing different conditions than those that motivated this initial audit proposal. The primary objective of the proposed audit model is to support the modernization of companies with traditional manufacturing systems through the paradigms of Lean and Six Sigma, laying the groundwork for incorporating I4.0 standards with a particular focus on modernization efficiency.

This study proposes the following research questions (RQs): (1) RQ1: Is it possible to determine the current state and the desired state of the audited company by evaluating key dimensions and factors? (2) RQ2: Can general and transversal guidelines be established for SMEs in different sectors to address various improvement implementation approaches? (3) RQ3: Can improvement priorities be established to guide the company towards efficient modernization for Industry 4.0 based on Lean Manufacturing and Six Sigma?

To address these research questions, this study has outlined the following objectives (Objs), which are shown in Figure 1: Obj1 is to establish dimensions and key factors for a detailed evaluation of the current and desired state of the company. Obj2 is to develop assessment questionnaires with a Likert scale to understand the current and desired state of the audited company. Obj3 is to define the implementation approaches necessary for the modernization process of the company and its manufacturing system. Obj4 is to establish general guidelines to meet different improvement implementation approaches through method matrices for each dimension and key factor. Obj5 is to create graphical representations through dimension maps of the current and desired state of the company for easy and quick interpretation. Obj6 is to develop a priority dimension map to analyze potential imbalances in the key factors, setting improvement priorities to lead the company to efficient modernizations towards Industry 4.0. Finally, Obj7 is to apply the proposed audit model to an SME and analyze the results obtained.

This paper is organized into five sections. Section 1 provides a summary of the concepts related to the proposed model, such as Industry 4.0, Lean 4.0, Lean Six Sigma, and SMEs. Section 2 presents a literature review focused on the modernization of SMEs with an emphasis on Industry 4.0. Section 3 explains the design and development of the proposed Lean 4.0 audit model. Section 4 proposes and analyzes details the application of the model presented in a real case study for an SME, analyzing the results obtained from applying the techniques and tools used in the improvement proposals. Finally, Section 5 summarizes the findings, identifies gaps, and discusses future directions for the proposed model.

2. Literature Review

This study proposes a literature review following the classification of review types established by Mayer [29]. This author conceptualizes such a review as an analytical synthesis of the most recent advancements within a specific domain. Additionally, in accordance with the typology of bibliographic research outlined by Squires, this work adopts a descriptive review approach. Squires [30] characterizes this type of review as a structured synthesis that compiles relevant concepts in areas undergoing continuous transformation. Both review methodologies are integrated to ensure a multifaceted exploration of the subject matter, offering complementary perspectives that enhance the depth of the analysis [31].

Industry 4.0 represents a significant transformation that integrates advanced technologies such as the IoT, AI, big data analytics, and cyber-physical systems into manufacturing processes [1,3]. This transformation aims to enhance efficiency, flexibility, and customization, marking a shift towards smart manufacturing environments [1,2]. According to Weng et al. [32], a company can adopt Industry 4.0 in three distinct ways: through the implementation of a Smart Factory, by experimenting with the integrated use of various technological enablers, or by developing software and solutions for other companies interested in I4.0. These pathways reflect the diverse strategies companies can employ to leverage the benefits of Industry 4.0 technologies, depending on their unique contexts and capabilities.

Three critical foundations emerge from this analysis: (1) the technological-operational duality of I4.0 implementation (particularly challenging for SMEs), (2) Lean Manufacturing integration as a prerequisite, and (3) adaptable readiness-maturity frameworks. Significantly, before implementing I4.0 in a manufacturing system, foundational elements must be in place. These foundations for I4.0 can be both technological (technological capabilities for digitalization) and operational/organizational (prior employment of Lean Manufacturing, corporate culture, stakeholder attitudes, etc.) [33]. The successful implementation of Industry 4.0 depends significantly on these foundational aspects, which prepare the company for the technological leap.

Therefore, the first key factor to consider for the modernization of a manufacturing system is “readiness”. Readiness is a comprehensive indicator of a company’s ability to adopt and implement Industry 4.0 practices from technological, operational, and organizational perspectives [34]. By assessing readiness, companies can make informed judgments about their overall state and preparedness to embark on the transition to I4.0.

However, once the transition to I4.0 starts properly, the “maturity” approach is used to measure any progress. Maturity can be defined as the level of evolution that an organization has achieved, indicating the progress made during or after the transition to I4.0 [33]. Understanding maturity levels helps organizations identify their current state and plan subsequent steps to further their Industry 4.0 journey.

Since the concept of Industry 4.0 was introduced, several models have been developed for its implementation. Some models focus on the company’s readiness level, covering the development of a personalized strategy for transitioning to I4.0 and ensuring an optimal starting position. These models typically do not include the actual implementation phase [35]. Other models focus on the maturity level of I4.0 within the company during the transition, serving as indicators of the progress achieved at a given time. Additionally, some models concentrate on specific areas of interest, such as logistics or Lean Manufacturing (LM). Furthermore, specific models and guidelines for creating I4.0 strategies have been developed for SMEs, which often face more challenges in implementing I4.0 [34].

Among the models considered for the proposed audit are (1) a model for measuring the degree of preparation presented in the studies by Pacchini et al. [33]; (2) a model for evaluating maturity presented by Weng et al. [32]; (3) a model based on the high-performance L4.0 implementation roadmap proposed by Cifone et al. [35]; and (4) experiences gained from real-life L4.0 implementation projects, such as those presented by Ghobakhloo [20].

Recent studies have expanded on the importance of readiness and maturity models in the context of Industry 4.0. For instance, the model by Schumacher [36] provides a comprehensive framework for assessing Industry 4.0 readiness, emphasizing the importance of organizational culture and workforce skills alongside technological capabilities. This model highlights that readiness is not merely about technology but also about aligning organizational practices and capabilities with Industry 4.0 principles [36].

Further, the study by Mittal et al. [37] offers a multi-dimensional readiness assessment model that includes factors such as leadership commitment, employee readiness, and innovation capability. This model provides a holistic view of what it takes for an SME to successfully transition to Industry 4.0, addressing both internal and external factors that influence readiness.

On the maturity side, De Carolis et al. [38] proposes a maturity model that focuses on the stages of digital transformation. Their model identifies specific milestones and key performance indicators (KPIs) that companies must achieve to progress through different maturity levels. This structured approach helps organizations plan their digital transformation journey more effectively and ensures sustained progress towards full Industry 4.0 integration.

Moreover, Grufman [39] have developed a model that integrates readiness and maturity assessments to create a more dynamic understanding of a company’s progression towards Industry 4.0. Their approach includes detailed metrics for both technological adoption and process integration, ensuring that companies can track their development in real-time and adjust strategies accordingly.

Additionally, the integration of Lean Manufacturing and Industry 4.0, often referred to as Lean 4.0, has been extensively studied. Kolberg and Zühlke [9] suggest that Lean principles provide a strong foundation for Industry 4.0 implementation. Their study indicates that Lean practices such as continuous improvement, waste reduction, and value stream mapping can significantly enhance the effectiveness of Industry 4.0 technologies. This integration not only improves operational efficiency but also accelerates the digital transformation process.

The implementation experiences of SMEs, as discussed by [21], underscore the practical challenges and benefits of adopting Industry 4.0. Their research highlights that while SMEs face significant hurdles, such as limited financial resources and lack of expertise, the strategic use of readiness and maturity models can facilitate a smoother transition. Moreover, they found that SMEs benefit from incremental adoption strategies, focusing on specific aspects of Industry 4.0 that align with their immediate needs and capabilities.

Similarly, the way in which digital transformation in SMEs is often hindered by a lack of managerial resources and organizational experience, and how it may create strong barriers to adoption, has also been emphasized by Ben Slimane et al. [18].

Overall, the literature emphasizes the critical role of readiness and maturity assessments in the successful implementation of Industry 4.0. These models provide structured pathways for companies to follow, ensuring that they build a solid foundation before advancing to more complex stages of digital transformation. By integrating these models with Lean Manufacturing principles, companies can achieve greater efficiency and competitiveness in the era of Industry 4.0.

3. Methodology

Based on existing models [33], roadmaps in the literature [32] and recommendations, a preliminary process for the implementation of Lean 4.0 is established [20]. This process includes several phases designed to assess the company’s readiness, implement Lean principles, and gradually introduce advanced technologies. The phases are described as follows [40]:

• Phase I: Readiness Assessment The first phase focuses on evaluating the company’s readiness for the project and adopting corrective measures if necessary. This initial diagnosis is crucial for identifying existing technological capabilities, organizational culture, and operational competencies [37]. Tools such as the “Readiness Assessment Model” by Schumacher et al. [36] can be used to measure the company’s readiness in terms of multiple dimensions.
• Phase II: Implementation of Lean Thinking/Manufacturing/Management: In this phase, the company should implement Lean Thinking, Lean Manufacturing, and Lean Management principles as much as possible. This involves adopting techniques such as waste elimination, continuous improvement (kaizen), just-in-time, and using tools like value-added analysis and value stream mapping [6,41]. The literature suggests that a solid Lean foundation can facilitate the integration of Industry 4.0 technologies by improving operational efficiency and reducing variabilities [9].
• Phase III: Gradual Introduction of Technologies: Once Lean principles are established, the gradual introduction of technologies follows, prioritizing those that offer the greatest efficiency. These technologies include real-time information sharing with suppliers and customers, the use of Radio Frequency Identification (RFID) tags on products and machinery, sensors for monitoring production, automation of processes, and information distribution through integrated systems [42,43]. These technologies are fundamental to creating an intelligent and connected production environment [3].
• Phase IV: Maturation and Introduction of Advanced Technologies: The final phase focuses on maturing the technologies and capabilities introduced in the previous phase and gradually incorporating more complex technologies such as additive manufacturing, augmented reality, artificial intelligence, machine learning, big data, and collaborative robots [4,21]. The integration of these technologies should be carried out to varying degrees based on availability, cost, and the ability to integrate them quickly and efficiently into the existing system [44].

3.1. Lean 4.0 Audit Proposal

For the formulation of the initial proposal, the ISO 19011:2018 standard on guidelines for auditing management systems was taken as a reference, although it has been applied to a different type of audits [45]. This standard provides detailed guidance for managing an audit program, including audit principles, managing audit programs, and conducting audits of management systems.

The audit program of this study consists of a single audit (Lean 4.0 audit) whose scope is determined by defining its extent, limits, and frequency: (i) Extent: The audit refers to a project aimed at modernizing the production of the audited company, an SME in the furniture sector, by implementing Lean 4.0 principles. (ii) Limits: This refers to limits imposed by the activities and planning of the collaboration between the stakeholders involved in the modernization process. (iii) Locations: The audit is physically conducted in the industrial plant and the technical office of the company.

3.1.1. Type of Audits

The audit program of this project addresses a single audit (Lean 4.0 audit), determined by the object (combined audit), the auditor (joint audit), and the scope (product design and development audit). The audit is suggested to be combined, as per the UNE-EN ISO 19011 standard, which is suitable for auditing multiple systems from different disciplines together [46]. This applies to various branches of Industrial Engineering integrated with Industry 4.0 technologies and Lean principles.

According to ISO 9001:2015, this audit is a joint audit conducted on a single auditee by two or more auditing organizations [47,48]. Here, the company is the auditee, engaged in modernizing its production systems to Industry 4.0 standards using Lean paradigms. The auditing organizations include the company itself (internal audit) and an external party interested in the project’s knowledge advancement (external second-party audit).

For the scope, the audit focuses on product design and development, with an emphasis on project innovation within the company.

3.1.2. Audit Criteria

Audit criteria are a set of policies, procedures, or requirements used as a reference against which the audit evidence is compared. In turn, audit evidence is a set of records, factual statements, or any other information relevant to the audit criteria and verifiable. Audit criteria include capabilities, procedures, practices, techniques, technologies, and other aspects related to Industry 4.0 and Lean [49].

3.1.3. Audit Methods

The audit method will be a combination of Six Sigma and the Factors Balance Method for Concurrent Engineering adapted for Lean 4.0 with a projection in SMEs:

• Six Sigma will be deployed according to the DMAIC cyclical phases: Define, Measure, Analyze, Improve, and Control [49,50]. Six Sigma is a robust methodology that helps improve business processes by reducing variability and improving quality [16].
• The Factors Balance Method for Concurrent Engineering proposed by Carter and Baker [51] will provide the application phases and the way to analyze the information collected during the audit. This method is crucial for coordinating design and development activities in a concurrent manufacturing environment, which is essential for implementing Lean 4.0.

3.2. Factors Balancing Approach Method for Lean 4.0

The proposed Lean 4.0 audit method is based on concepts from Carter and Baker’s [51] evaluation model, focusing on innovation in product development and audits oriented towards Concurrent Engineering. Carter and Baker’s model balances four dimensions—organization, communication, requirement, and product development—applicable to task, project, program, and enterprise approaches [52]. This method has sparked interest in terms of its application in modernizing manufacturing systems and their audits.

Adapting Carter and Baker’s phases [51], this proposal introduces new dimensions and key factors specific to Lean Six Sigma and Industry 4.0 paradigms [53]. The original model’s evaluation approaches were modified to suit the company’s modernization process, meeting various maturation milestones. Unlike Carter and Baker’s single-level, dichotomous evaluation scale, the proposed model evaluates each key factor across four levels, offering a more detailed method matrix.

The methodology focuses on auditing the current state and establishing the desired state of the company through four phases: a company assessment questionnaire (phase 1), a method matrix (phase 2), a dimension map (phase 3), and improvement priority dimension setting (phase 4). This approach serves as an audit and a vision for the future, outlining the changes needed to modernize the manufacturing system to Industry 4.0 standards, and identifying efficient resource allocation.

The model is premised on several dimensions critical for evolving towards Industry 4.0 [32]. These dimensions, along with key factors for detailed analysis, are presented in

Table 1. Proposed dimensions and key factors.

Dimensions	Key Factors
Principles	Human Participation Logistics Decentralized Systems
Self-driven	Automation Self-Regulation Quality Control
Information	Ubiquity Security Decision Making Inferences
Integration	Knowledge Management Continuous Improvement

.

Table 1 outlines the Principles dimension, which assesses the company’s adoption of concepts that support Lean Manufacturing (LM), Six Sigma, and Industry 4.0 technologies to enhance modernization opportunities. This dimension comprises three key factors:

• Human Participation: Ensuring the manufacturing system focuses on customer satisfaction and considers employee input from all levels, including technical staff and management.
• Logistics: Enhancing the organization of movements of raw materials, parts, subassemblies, and finished products within the plant, and improving their reception from suppliers or delivery to customers through LM techniques and Industry 4.0 technological integration.
• Decentralized Systems: Supporting flexible mass manufacturing that maintains high efficiency and effectiveness. This involves lines and work cells capable of reorganization and the production of modular, resilient, and highly customizable products. The Smart Factory operates as a decentralized system within a business environment that interacts digitally.

The Self-driven Manufacturing dimension evaluates the company’s ability to internally execute, regulate processes, and control results. This dimension includes three key factors:

• Automation: Striving to minimize human intervention in manufacturing processes.
• Self-Regulation: Ensuring a constant flow of value generation and efficient operation with minimal external intervention.
• Quality Control: Maintaining and improving quality control systems to ensure product quality and efficiency, even as the company gains flexibility.

The Information dimension is crucial for making a Smart Factory truly “intelligent”, focusing on data collection and information management. It includes four key factors:

• Ubiquity: Ensuring access and collaboration anytime and anywhere through remote and synchronized communication technologies, promoting operational continuity and team efficiency.
• Security: Protecting digital assets and data with Cybersecurity measures to ensure confidentiality, integrity, and availability, preventing attacks and mitigating risks.
• Decision Making: Enhancing task, project, and company management through Lean Management techniques, both traditional and modern, to improve results and accelerate modernization.
• Inferences: Assisting experts in handling large amounts of information with statistical analyses or processing technologies.

The Integration dimension ensures that modernized manufacturing systems effectively employ previous dimensions. It includes two key factors:

• Knowledge Management: Enhancing pre-existing expert knowledge with technical and organizational knowledge gained during the transition, offering a chance to rethink management practices.
• Continuous Improvement: Emphasizing continuous process improvement in line with Six Sigma principles to avoid digitizing inefficiencies.

Along with the dimensions and their key factors, two approaches are defined: the readiness and the maturity approach. They characterize the necessary approaches for the company’s modernization process and its manufacturing system. In turn, the maturity approach focuses on three consecutive stages: the Start-up, In-transition, and Advanced approach [33,54].

3.2.1. Phase 1: Assessment Questionnaire

The questionnaires aim to determine the initial state of the company through questions evaluated for each key factor of each dimension. To evaluate each key factor, four different questions are proposed, each assessing a specific approach of the key factor within the proposed dimensions. In this model, we propose introducing a Likert scale for each question based on four levels, from 1 to 4.

Value 1 is taken for “strongly disagree”, value 2 for “disagree”, value 3 for “agree”, value 4 for “strongly agree”, and the value “No answer or Not applicable (N/A)” for departments or areas where a specific aspect is not applicable. The reference model proposed by [51] uses a dichotomous YES/NO response for each question. However, focusing on obtaining the most information possible from each survey, the use of a Likert scale is proposed. This more detailed approach allows capturing nuances in the responses and provides a more granular view of the company’s state.

The choice of a Likert scale is supported by literature on research methods in social sciences and management, where this type of scale has been shown to be effective in capturing complex perceptions and attitudes [55,56]. Likert scales are widely used in management and audit research due to their ability to measure the intensity of respondents’ attitudes and perceptions, thus allowing for deeper and more detailed analysis [57].

A total of 48 company assessment questions have been proposed.

Table 2. Information dimension. Assessment questionnaire for the key factor Ubiquity.

No.	Question	Response
Q25	Parameters have been determined to measure aspects of production.	N/A 1 2 3 4
Q26	Sensors exist to monitor parts of the production.	N/A 1 2 3 4
Q27	Digital interfaces and sensors exist on most machines.	N/A 1 2 3 4
Q28	Augmented reality devices are used to visualize real-time information.	N/A 1 2 3 4

shows an example of an assessment questionnaire for the key factor “Ubiquity” of the Information dimension. This example illustrates how the questions and response options are structured.

Each of these questions is designed to evaluate specific aspects of the company’s ability to operate ubiquitously, a key factor for the successful integration of Industry 4.0 technologies. The collection and analysis of these responses will provide a detailed view of the current state of the company in terms of its readiness and technological capacity, allowing for the identification of improvement areas and the prioritization of actions in the subsequent phases of the modernization project.

3.2.2. Phase 2: Method Matrix

The reference method matrix model by [51] focuses on the implementation of Concurrent Engineering. Therefore, this proposal is a new model based on the implementation of Industry 4.0 with a Lean Six Sigma approach. In the proposed method matrix, the necessary means for successful implementation of the manufacturing system according to Industry 4.0 are identified. Similarly to the assessment questionnaire, the method matrix is structured according to dimensions, key factors, and approaches. The method matrix can provide general guidelines to meet various maturation milestones and can be adapted to the specifics of each project and the company’s context.

To improve the adaptability of the method matrix, it is proposed that levels corresponding to each key factor of each dimension be incorporated, achieving a higher level of detail in the analysis of responses obtained in phase 1 of the proposed model. These levels are linked with the conversion used to transform the results evaluated in the assessment questionnaire using the Likert scale to the values used in the results analysis conducted earlier (

Table 3. Qualitative and quantitative levels for evaluating the method matrix.

	Level 1	Level 2	Level 3	Level 4
Qualitative	Strongly disagree	Disagree	Agree	Strongly agree
Quantitative	0.0	0.3	0.7	1.0

). The resulting method matrix for each key factor of each dimension (

Table 4. Method matrix with levels for the key factor Ubiquity (dimension: Information).

Levels	Readiness	Maturity: Start-Up	Maturity: In-Transition	Maturity: Advanced
1 (0.0)	No parameters are determined to measure production aspects.	No sensors exist to monitor any part of production.	No digital interfaces or sensors exist in the workstations.	Augmented reality devices are not used to visualize real-time information.
2 (0.3)	Some basic parameters (input, output) are determined for production aspects	Some sensors exist to monitor parts of production.	Digital visualization and/or data interaction interfaces exist in some workstations.	Classical devices are used to visualize asynchronous information.
3 (0.7)	Some parameters are determined to measure some aspects of production (input, processes, output).	Sensors exist to monitor several production lines.	Digital visualization and/or data interaction interfaces and sensors exist in some workstations.	Digital devices (screens, wearables, etc.) are used to visualize real-time information.
4 (1.0)	Several parameters are determined to measure most aspects of production (input, processes, output).	Smart sensors of various types exist to monitor most production lines and centers.	Digital visualization and interaction interfaces and smart sensors exist in most machines.	Augmented reality devices are used to visualize real-time information.

) is structured based on the proposed approaches (columns) and evaluation levels (rows).

To ensure transparency and reproducibility, Appendix A.1 provides the complete set of 48 assessment questions designed for the audit. Additionally, Appendix A.2 presents the methods matrix, detailing the levels associated with each key factor. The inclusion of these appendices enhances the comprehensiveness of the methodology, enabling a clear understanding of the evaluation criteria and facilitating its application in future studies.

3.2.3. Phase 3: Dimension Map

The dimension map graphically represents the current state of the company (based on the survey conducted in phase 1) and the desired state according to the Factors Balance Method, highlighting the differences between them. It uses a radar chart (circular chart), as shown in Figure 2, where the survey questions are organized into quadrants based on the dimension they belong to and into concentric circles according to their approach. Initially, the dimension map in the audit will be empty or blank.

In Figure 2, each concentric circle of the circular chart corresponds to a different approach within the method: (1) center of the diagram: no readiness approach; (2) first concentric circle, corresponding to Q1 (P1-P5-P9-…-P45): readiness approach; second concentric circle, corresponding to Q2: maturity: Start-up approach; third concentric circle, corresponding to Q3: maturity: In-transition approach; and fourth concentric circle, corresponding to Q4: maturity: Advanced approach.

Once the survey is completed, the cells of the map corresponding to each question will be marked according to the Likert scale level answered, using a color code for each associated value. Subsequently, all cells corresponding to the desired state of the company will be marked with a different type of fill (e.g., stripes).

The radar chart effectively displays multidimensional data, allowing for a clear comparison between the current and desired states across various dimensions and key factors. This visualization aids in identifying specific areas where improvements are needed and helps in prioritizing actions.

The dimension map serves as a powerful visual tool to capture the state of readiness and maturity across different dimensions of the company’s operations. By plotting the current state (as assessed through the questionnaire responses) against the desired state (as defined by strategic goals and benchmarks for Industry 4.0 implementation), stakeholders can easily identify the gaps and areas needing attention.

3.2.4. Phase 4: Improvement Priority Setting

The priority dimension map serves to highlight the imbalances in different key factors according to the proposed approaches. This enables the inference of necessary improvements to drive the company towards efficient modernization under Industry 4.0, grounded in Lean Manufacturing and Six Sigma. In Figure 3, the four dimensions of modernization and their respective key factors are distributed along the x-axis. The y-axis is occupied by the four proposed approaches, and the matrix is completed with the questions proposed in the assessment questionnaire (Q1 to Q48).

From the survey results, a target future state of modernization should be established, attending to recommendations for SMEs and indicating them on the dimension map. Additionally, any cell of the readiness approach left unanswered (N/A) must be marked obligatorily, as it indicates a basic deficiency in the company preventing system improvement.

In this modernization project, the company progresses from an initial state (readiness approach) in adopting Lean 4.0 to a more advanced state (maturity approach). However, it does not advance too far to avoid inefficiencies. In future projects, the company could perform the audit again to consider improvement towards that advanced maturity state (Advanced approach).

4. Results: Case Study Analysis

Marpe S.L. is a SME specializing in the design and development of upholstered furniture, particularly sofas. The company has a traditional manufacturing setup, focusing on craftsmanship and high-quality materials. However, to stay competitive and meet the evolving market demands, Marpe S.L. aims to modernize its production processes by integrating Industry 4.0 technologies and Lean Manufacturing principles. The company’s structure includes the administration, commercial and purchasing, and manufacturing departments, each playing a crucial role in its operations.

4.1. Lean 4.0 Audit Model

The proposed Lean 4.0 audit model was implemented in three departments of the audited company: the administration department, the commercial and purchasing department, and the manufacturing department. These departments are common in the vast majority of SMEs, making the proposed model applicable to other companies in different sectors with basic adaptation.

In the first phase, the assessment questionnaire was conducted across all departments. The data collected from these surveys were then processed and analyzed, resulting in both numerical and visual outcomes from the Lean 4.0 audit.

Based on the results of the questionnaires administered to the employees in the different departments, three potential approaches for data processing were analyzed:

• Calculation of the System Implementation Percentage: Based on the “Evaluation of the Demerit Points System” [58], this approach requires translating the demerit scale to the Likert scale used in the surveys.
• Data Processing Based on Brito et al. [59]: This study used dichotomous YES/NO responses, presenting data in radar charts based on the proposed evaluation system shown in

  Table 5. Values associated with assessment questionnaire for the key factor Ubiquity.

  Criterion	Value	Percentage
  Strongly disagree	1	0%
  Disagree	2	30%
  Agree	3	70%
  Strongly agree	4	100%

  .
• Data Processing Based on Starzyńska et al. [60]: This approach involves an audit questionnaire based on a five-value scale and the calculation of assessment indicators. The indicators proposed are defined within each perspective or approach in each audit area. The formula used is shown in (1):

$$in{d}_{area,perspective}=(\sum {\displaystyle \frac{an{s}_{area,perspective,i}}{4n_{ans,area,perspective}}})·100\%$$

(1)

where ind_{area,perspective} is an averaged indicator of good practices for the area and perspective; ans_{area,perspective}, is the answer for question i in the area and perspective.

In this application, the areas represent the four dimensions of the model (Principles, Self-driven, Information, and Integration) and the perspectives represent the four approaches (readiness, Start-up, In-transition, and Advanced). For each department of the audited company, an indicator will be calculated. The data obtained from the indicators will be used to create radar charts.

After evaluating the three alternatives for processing the data obtained from the audits, the approach proposed by Brito et al. [59] and the calculation of indicators by Starzyńska et al. [60], adapted to the proposed audit model, were selected.

4.2. Analysis of Audit Results

Based on the selected data processing method, calculations were made of the summations of the responses from the individuals audited in each respective department. The established percentages and shown in Table 5 were then applied. The calculation of the percentages is applied both to the dimensions and to the key factors of each dimension as shown in (2):

$${\%}_{dimensions,key factor}={\displaystyle \frac{0·n_{i}1+0.3·n_{i}2+0.7·n_{i}3+1·n_{i}4}{\sum answers}}$$

(2)

where n_i is the number of responses obtained for each value of the Likert scale from the assessment questionnaire.

After calculating the percentages from the assessment questionnaire, these data are graphed using radar charts for each analyzed department. Each department of the company receives a graphical result of the dimensions and a graphical result of the key factors as shown in Figure 4.

Following the surveys and obtaining the results, the method matrices for each key factor proposed in the Lean 4.0 audit model were analyzed. The method matrices in the proposed model are general enough to adapt to any sector and business context targeted by the audit. The method matrix exemplified in Table 4 for the key factor of ubiquity in the dimension of Information does not require adaptation to the company’s context and department, so the same matrix from the proposed model will be used.

To graphically visualize the results obtained by applying the method matrix and represent the company’s current state, the dimension map of Figure 5 is employed.

Here, the survey questions are ordered by quadrants according to the dimension and key factors to which they belong, and by concentric circles according to their approach. In this case, we propose establishing a grayscale to show the level of each quadrant, corresponding to the levels established in the proposed method matrices.

After obtaining the results from the assessment questionnaire, evaluating the method matrix, and generating the dimension maps, each key factor should be individually analyzed. For instance, the conclusions for the key factor “Ubiquity” within the “Information” dimension, after applying the proposed audit model to the manufacturing department, can be summarized as follows:

• Basic parameters (input, output) for production aspects are determined. Real-time information on product status, completed orders, orders in process, and pending orders is available.
• There are no sensors to monitor any part of the production process.
• Digital interfaces for data visualization and interaction exist at some workstations. Typically, area managers have access to PCs connected to the data and information management platform.
• Augmented reality devices or other wearables are not used for real-time information visualization.

Based on the results obtained for each department and the specific conclusions for each key factor within each dimension, a comprehensive priority dimension map for the company is developed. This map clearly visualizes the state of each dimension and determines the level of each key factor (Figure 6). Furthermore, it identifies which dimensions and key factors require more attention and the best approach to address these needs effectively.

Similarly to the dimension map shown in Figure 5, the priority dimension map uses the same grayscale to indicate the level of each factor, as shown in Figure 6. Based on the outcomes of the previous phases, a future target state for modernization is established, considering recommendations for SMEs and marking them with a special fill, such as blue stripes, as shown in Figure 6. Additionally, any cell in the readiness approach with a level 1 response (Strongly disagree) must be striped (orange stripes in Figure 6). Each analyzed department will have its own priority dimension map, displaying its current state and the target or future state. Using the data obtained from each department of the audited company, a general company-wide map can be generated.

After thoroughly analyzing the audit results and examining the dimension map, it becomes evident that the company has implemented fundamental improvements in almost all areas, albeit with varying degrees of intensity. This demonstrates a clear willingness to continue progressing and evolving. To achieve this, it is necessary to advance and elevate these levels, which present areas for improvement and, in some cases, require reorientation.

Considering the company as a whole, certain key factors can be identified in Figure 6 that are in a less advanced state of progress. An example is the key factor of Logistics, which will require improvements in the organization of the movements of raw materials, parts, and products within the factory. Another important aspect is the key factor of Inferences, which involves capturing, processing, and analyzing information.

However, more advanced levels are also observed in other key factors, such as Security or Self-regulation, where the goal is to maintain a constant flow of value generation and operate in the most efficient manner. Conversely, for the key factor of Human Participation, which focuses on meeting customer needs to gain a competitive advantage, all members of the company, from employees to senior management, must be considered.

Based on the information collected, the initial ideal objective is to achieve the readiness approach in all key factors and subsequently elevate all readiness approaches to level 2 (maturity: Start-up approach) and then to level 3 (maturity: In-transition approach) or higher, thus creating a solid foundation upon which to continue building. Achieving these objectives will be accomplished through the implementation of Lean, Six Sigma, and Industry 4.0 methodologies, with the aim of modernizing and improving the company’s efficiency and productivity.

Following the application of the proposed Lean 4.0 audit methodology, an exhaustive analysis of various aspects of the company was conducted. The objective was to contextualize the selection and application of the tools offered by Lean 4.0. The data collected serve as a foundation for identifying areas of improvement and proposing concrete solutions that align with Lean, Six Sigma, and Industry 4.0 methodologies. This analysis generates specific recommendations and implementation proposals aimed at optimizing processes and achieving significant improvements in the company’s efficiency and performance.

Firstly, a study of the company’s internal and external logistics organization was carried out, encompassing each process, materials, tools, necessary machinery, and involved personnel. Relevant information, such as product quantities and production times, was obtained.

Secondly, a comprehensive analysis of the product manufacturing process was conducted. This involved examining the times and activities involved in each stage of the production process. Detailed information was collected on all materials used in manufacturing, times spent in each workstation, order dates and times, reception dates and times, and quantities received, among other relevant data. Additionally, a production analysis for a complete month and a thorough review of all orders made during the previous year were conducted.

Based on the collected information, the development of a value stream mapping (VSM) is proposed. VSM is a graphical tool that quickly and easily identifies areas where waste or non-value-adding activities occur, such as delays, unnecessary movements, waiting times, or overproduction [61]. Conducting a VSM helps analyze the total time required to complete the process and provides the opportunity to identify areas for improvement. To perform the VSM, it is necessary to calculate the times between processes (considering the optimal, pessimistic, and probable times), the number of orders, and other relevant data. Furthermore, a study of the movements carried out by workers in various workstations was conducted. Once these parameters were obtained, an updated VSM was developed, including a timeline that shows the time for the Value-Added Process and the Total Processing Time. Additionally, the inclusion of Kaizen bursts is proposed to highlight improvement opportunities identified during the analysis.

4.3. Lean 4.0 Implementation

From the information gathered and the analysis of the company’s operations and structure, especially in the production area, the implementation of tools based on Lean, Six Sigma, and Industry 4.0 methodologies is proposed.

The 5S tools are a set of principles and techniques primarily used in Lean Manufacturing, aimed at improving productivity, competitiveness, and quality within companies. Each “S” corresponds to a Japanese word representing a key concept in the process: Seiri (Sort), Seiton (Set in order), Seiso (Shine), Seiketsu (Standardize), and Shitsuke (Sustain) [62]. To initiate the improvement process based on the 5S methodology, a 5S audit will be conducted at each workstation. This audit will evaluate the current state of each area, identifying opportunities and determining aspects that require attention and improvement [49]. The goal is to perform a detailed analysis of each result obtained in the different phases of the methodology and to apply the corresponding actions in each of them.

Additionally, improvements are proposed based on the evaluation of the company’s current situation using the method matrix and its various levels according to the proposed model. This involves defining a series of objectives with the primary aim of addressing the readiness approach first. The purpose of this is to establish a solid foundation upon which continuous advancement and improvement can be achieved. To accomplish these objectives, the use of Lean manufacturing tools is proposed as part of the strategy.

For example, to improve the key factor of Logistics and task planning, it is proposed that the company incorporate techniques such as Kanban, Heijunka, Kaizen, Jidoka, Lean’s Auto Quality Matrix, Ishikawa diagram, Pareto analysis, Total Productive Maintenance (TPM), and Single-Minute Exchange of Dies (SMED). These techniques can be extrapolated to other key factors that need to improve their level of implementation.

The implementation of the Lean 4.0 audit methodology in the audited company’s manufacturing department has led to significant advancements in various dimensions of operational efficiency and readiness for Industry 4.0 integration. The comprehensive analysis of the company’s processes, coupled with targeted improvements based on the dimension map and method matrix, has facilitated a substantial enhancement in the company’s readiness approach. The initial state of the company’s processes has evolved into a more advanced and structured system, ready to tackle subsequent stages of maturity.

By addressing key factors that were not previously at the readiness level, the company has made considerable strides in several critical areas. For instance, the establishment of digital systems for collecting feedback from employees and customers (Q1), the application of Lean Manufacturing techniques to internal logistics (Q5), and the optimization of manufacturing processes using basic techniques (Q9) have collectively contributed to streamlining operations and enhancing efficiency. The implementation of quality control systems in various stages of production (Q21), the measurement of production aspects (Q29), and the use of statistical techniques such as Pareto analysis for obtaining inferences (Q37) have further strengthened the company’s ability to maintain high standards of quality and operational effectiveness.

The updated dimension map, shown in Figure 7, reflects these advancements and provides a clear visualization of the current state, showcasing the progress made from the initial readiness approach towards a more mature and efficient system. The immediate positive outcomes of the Lean 4.0 audit have not only improved the manufacturing department but also set a precedent for extending the achieved benefits and methodologies to other areas of the company, as well as to clients and suppliers.

It is crucial for the company to maintain discipline and preserve the state achieved through continuous improvement initiatives. Regular reviews and periodic audits are essential to ensure the sustainability of the improvements and to foster ongoing progress. The company’s commitment to these practices will enable it to remain competitive and agile in the evolving industrial landscape.

4.4. Digital Enablers for Industry 4.0

To develop Industry 4.0 in the manufacturing department of the audited company, our study proposes integrating digital technologies into the manufacturing and production processes. This involves using various technological enablers, with the most relevant being the IoT, AI, robotics, additive manufacturing, and big data. Implementing this industrial model in SMEs presents significant challenges due to limited financial resources, lack of knowledge and training, incompatibility with existing systems, scalability, and adaptability, among others [20]. It is essential to review each of these challenges and identify enablers that could be applicable, even in smaller companies with non-linear production.

To complete and improve the readiness approach for some key factors, we propose incorporating the collection and analysis of data generated during the manufacturing process to optimize efficiency and quality, identify bottlenecks, improve production times, and predict potential problems. The use of big data and predictive analytics is also relevant, as analyzing historical data can provide valuable insights into customer preferences, market trends, and demand patterns. This would allow the company to anticipate customer needs, adjust production, manage inventory, and reduce lead times.

Specifically, we propose implementing big data and predictive analytics to analyze historical data and gain insights into customer preferences, market trends, and demand patterns. This will help in forecasting potential production issues and optimizing production schedules. Implementing RFID tags will facilitate efficient inventory tracking and management, and combining RFID with Kanban cards will streamline inventory processes and ensure timely material flow. Utilizing IoT devices to monitor production processes in real-time will enhance visibility and control, while collecting data from sensors will optimize machine performance and predict maintenance needs. Integrating AI for predictive maintenance and quality control will reduce downtime and improve product quality, and using AI algorithms to optimize production planning and resource allocation will enhance overall efficiency. Employing robotics to automate repetitive tasks will improve efficiency and reduce labor costs, and utilizing additive manufacturing for rapid prototyping and customized production will provide greater flexibility and innovation in manufacturing processes.

To improve the key factor of Human Participation, we propose implementing a digital feedback system to collect comments from both employees and customers. This system will facilitate the identification of improvement areas and allow for adjustments in production processes according to customer needs and preferences. Engaging employees in continuous improvement initiatives and fostering a culture of innovation are critical components for successful Industry 4.0 adoption.

By integrating these digital technologies and addressing the identified challenges, the company can significantly enhance its manufacturing capabilities and achieve a higher level of readiness for Industry 4.0. This strategic approach not only improves efficiency and productivity but also aligns with the evolving demands of the market and customer expectations.

5. Conclusions

This study has presented an innovative Lean audit model specifically designed to address the modernization needs and transition to Industry 4.0 of Small and Medium Enterprises (SMEs) in the manufacturing sector, particularly those with traditional or artisanal production systems. The primary objective of this study was to develop a structured tool that empowers SMEs to assess their current state, identify pivotal areas for enhancement, and establish priorities for an efficient digital transformation. This approach is anchored in the fundamental principles of Lean Manufacturing, Lean Thinking, and Lean Management, while accounting for resource constraints.

The proposed model is articulated in a methodology of four clearly defined phases: An initial assessment phase through a detailed 48-question questionnaire that diagnoses the current state of the company in relation to Lean principles and Industry 4.0 enablers (Appendix A.1). The implementation of a Likert scale in the assessment of responses facilitates a more precise and nuanced capture of perceptions within the organization. The second phase involves the analysis of improvement strategies, which employs a matrix of methods (Appendix A.2) that provide general and adaptable guidelines to reach different levels of maturity in each of the twelve key factors identified within four strategic dimensions: Principles, Self-driven Manufacturing, Information and Integration. The third phase involves the visual representation of the current and desired state through a map of dimensions (Figure 2), a radar chart that facilitates the identification of gaps and areas that require further attention. The fourth phase involves the prioritization of improvement dimensions through the use of a priority map (Figure 3). This process facilitates the establishment of a customized action plan that is centred on the efficient modernization of Industry 4.0.

The application of the model in a real case study in an SME of the upholstered furniture sector, Marpe S.L., has validated its applicability and the results of the study. The findings from the various departments of the company, including administration, sales and purchasing, and manufacturing, have demonstrated the model’s capacity to identify critical improvement factors and implement specific Lean interventions. This, in turn, has enhanced the SME’s readiness for the progressive adoption of digital enablers.

A comprehensive analysis was conducted, establishing key dimensions and factors for detailed assessment (Objective 1), and identifying key areas for improvement. The evaluation questionnaire, which incorporated Likert scales (Objective 2), yielded valuable information for a detailed assessment.

The implementation of Lean tools and Industry 4.0 technologies, including the IoT, RFID, AI, and robotics, has enabled the company to define approaches for modernization (Objective 3). These approaches have established a solid foundation for future developments. The implementation of methodologies such as Kanban, Heijunka, Kaizen, Jidoka, and SMED has established general guidelines for various improvement approaches (Objective 4), thereby providing a structured framework for continuous enhancement.

The subsequent update to the dimension map (Objective 5) following the implementation of initial improvements signifies the company’s substantial progress towards a more mature and efficient system. This instrument has been instrumental in the tracking of progress, the identification of imbalances, and the establishment of priorities for enhancement (Objective 6). The successful implementation of the Lean 4.0 audit model in the SME (Objective 7) has substantiated its efficacy in promoting enhancements and attaining operational excellence.

The primary contribution of this work lies in the proposal of an integrated audit framework that specifically addresses the challenges and resource constraints faced by SMEs on their journey to Industry 4.0. In contrast to numerous extant models that are mainly designed for large companies, this model combines a detailed diagnosis with strategic guidance and prioritization mechanisms, all based on the principles of Lean Manufacturing and Six Sigma. The delineation of four distinct strategic stages (Readiness, Start-up, In-transition, and Advanced) offers a lucid and progressive roadmap for continuous enhancement.

Furthermore, the study adapts and extends existing concepts, such as Carter and Baker’s Factors Balance Method for Concurrent Engineering, to the specific context of modernizing production systems under the Lean Six Sigma and Industry 4.0 paradigms. The integration of a Likert scale in the evaluation questionnaire signifies an enhancement over dichotomous models, enabling a more comprehensive and nuanced data collection.

In conclusion, the proposed Lean 4.0 audit model is presented as a valuable and practical tool for SMEs seeking to modernize their operations and integrate digital technologies efficiently and adapted to their capabilities. The application of the model in the case study demonstrates its potential to identify critical improvement areas, set strategic priorities, and guide the implementation of concrete actions based on Lean principles. These actions facilitate a progressive and optimized transition towards Industry 4.0. The model’s orientation towards the specific constraints and needs of SMEs addresses an important gap in the extant literature and provides a solid basis for future modernization initiatives in this crucial sector of the economy.

Future Works

In addition to the significant contributions of this study, there are several avenues for future research that have the potential to enhance and extend its scope:

• The model’s validation and generalization in diverse contexts warrant further investigation. While the application of the model in a single case study is valuable, it suggests the necessity to validate its effectiveness and adaptability in a more extensive and heterogeneous sample of SMEs. These SMEs should represent various industrial sectors and possess different operational characteristics and levels of digital maturity. This would allow for the refinement of the model, the identification of possible adaptations needed for specific contexts, and the increase in generalizability of its findings.
• Longitudinal studies on long-term impact. Conducting longitudinal studies on SMEs that have implemented the audit model and the derived action plans would be a fruitful avenue of research. This would facilitate the assessment of the sustainability of the implemented improvements, the genuine impact on operational efficiency and competitiveness over time, and the progression through the maturity stages towards Industry 4.0.
• The refinement of dimensions, key factors, and questionnaires is imperative. As the model is applied in more contexts, feedback could be collected from users and experts to refine the strategic dimensions, key factors, and evaluation questionnaire questions. This approach would ensure the model’s relevance, accuracy, and comprehensiveness in addressing the evolving needs of SMEs.
• In addition, in-depth analysis of the implementation of specific enabling technologies could be a fruitful avenue for future research. This analysis could entail a detailed examination of the implementation and impact of specific Industry 4.0 enabling technologies (e.g., IoT, AI, collaborative robotics, Big Data, and predictive analytics) within the framework of the Lean 4.0 audit model. This would provide more specific and practical guidelines on how SMEs can adopt these technologies effectively and aligned with their Lean objectives.
• The consideration of human factors and organizational culture is imperative for a comprehensive understanding of the implementation process. While the "Human Involvement" factor is incorporated into the model, further analysis is warranted on how human factors and organizational culture influence the adoption of Lean 5.0 in SMEs. The development of modules or tools that are complementary to the model is recommended. These modules or tools could assess cultural readiness and change management. They could also propose strategies to foster a culture of continuous improvement and collaboration.
• Extend the model to the supply chain. The current model’s primary focus on the internal operations of SMEs necessitates extension to encompass external factors. The model’s scope could be expanded to encompass the integration of Lean 4.0 principles and digital technologies throughout the supply chain. This would involve analysing opportunities for collaboration and enhancement with suppliers and customers.
• A relevant future line of research could be to assess how the adoption of Lean 4.0 in SMEs, guided by the proposed audit model, affects their performance in terms of sustainability (reduction of resource consumption, waste minimization, improvement of working conditions, etc.).

In summary, the Lean 4.0 audit model presented in this study offers a promising approach to support the modernization of SMEs towards Industry 4.0. Subsequent research in these domains will facilitate the consolidation, refinement, and extension of this work, thereby maximizing its impact and contributing to the development of a more efficient, competitive, and sustainable manufacturing SME sector.