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. 2023 Nov 20;9(12):e22506. doi: 10.1016/j.heliyon.2023.e22506

Analysis of barriers affecting Industry 4.0 implementation: An interpretive analysis using total interpretive structural modeling (TISM) and Fuzzy MICMAC

R Ben Ruben a, C Rajendran a,, R Saravana Ram b, Fadoua Kouki c, Haya Mesfer Alshahrani d, Mohammed Assiri e
PMCID: PMC10686847  PMID: 38046174

Abstract

The purpose of this study is to build a structural relationship model based on total interpretive structural modeling (TISM) and fuzzy input-based cross-impact matrix multiplication applied to classification (MICMAC) for analysis and prioritization of the barriers influencing the implementation of Industry 4.0 technologies. 10 crucial barriers that affect the deployment of Industry 4.0 techniques are identified in the literature. Also, the Fuzzy MICMAC approach is applied to classify the barriers. The importance of TISM over traditional interpretive structural modeling (ISM) is shown in this work. Results proved that the barriers, namely IT infrastructure, lack of cyber physical systems, and improper communication models, are identified as the most dependent barriers, and the barriers of lack of top management commitment and inadequate training are identified as the most driving barriers. This study makes it easier for decision-makers to take the necessary steps to mitigate the barriers. The bottom level of the TISM hierarchy is occupied by barriers that need more attention from top management in order to be effectively monitored and managed. This study explains the steps to execute TISM in detail, making it easy for researchers and practitioners to comprehend its principles.

Keywords: Barriers of Industry 4.0, Implementing Industry 4.0 technologies, Driving and dependent barriers, ISM, Fuzzy MICMAC

1. Introduction

Today’s world is on the verge of stepping into the Fourth Industrial Revolution. This industrial revolution incorporates the application of smart technology in manufacturing and production for efficient and real-time machine communication and the Internet of Things (IoT), which are integrated for increased automation and transforming machines into smart machines that visualize the entire manufacturing process and make effective autonomous decisions based on the problems that occur [[1], [2], [3]]. This present industrial revolution, also called Industry 4.0, seeks to minimize or eliminate waste with the adoption of emerging technologies such as smart manufacturing factories, cyber-physical systems (CPS), the industrial internet of things (IIoT), additive manufacturing, cognitive computing, cloud computing, and artificial intelligence (AI) thereby increasing the overall productivity of the industry [[4], [5], [6], [7]]. With the advent of water and steam power, traditional hand production was mechanized, which led to the first and second industrial revolutions. Mass production, which relied mainly on electricity, and the emergence of microprocessors and electronics paved the way for the third industrial revolution.

The present fourth revolution, being digital, is an industrial development that resulted due to advanced networking and automation of all areas of production [[8], [9], [10]]. Rather than defining it further, let us understand and configure its impact. Focusing on technologies such as AI or blockchain not as individual capacity but as a wider system, ensuring the technologies truly empower industries, we are in the midst of Industry 4.0 with technologies still emerging and hence acting for design, not default, and finally, the value and ethics of these technological systems should be considered [[11], [12], [13]]. The main thrust of Industry 4.0 is the advent of modern manufacturing, also defined as the smart factory, which implies smart networking between industry systems, process agility, versatility, and interoperability of industrial processes, collaboration with consumers and suppliers, and the implementation of creative business models [[14], [15], [16]]. The driving aspect of Industry 4.0 is the CPS, which integrates networking and computation into manufacturing machines to provide dynamic interactions of feedback data and synchronize all the sub-systems, providing increased performance for organizations.

It is not an easy job to incorporate these new technologies, as each has a certain contribution to the whole, and hence it is important to examine such components. Instead of determining the factors of Industry 4.0, evaluating the barriers would result in each barrier being progressively solved from the most critical to the least critical. On analysing the literature, it is evident that implementing Industry 4.0 is a tedious task, and the organization must be extremely committed to deploying the techniques. Industry 4.0 technology implementation frequently necessitates a substantial upfront investment in hardware, software, and workforce development. It could be difficult for smaller businesses to set aside the resources needed for such an upgrade. Data interchange and connected devices are used more frequently; therefore, cybersecurity is a major worry. Companies need to address any weaknesses and guarantee the security of sensitive data.

Organizations should concentrate on developing a precise and well-defined implementation strategy, carrying out thorough risk assessments, investing in employee training and upskilling, fostering a culture of innovation and adaptability, and working with experts and technology providers to navigate the complexities of Industry 4.0 integration. These steps will help organizations get past these obstacles. Additionally, through policy frameworks, funding efforts, and knowledge-sharing platforms, governments and industry groups may promote and encourage the implementation of Industry 4.0.

To overcome these barriers, a thorough strategy that includes stakeholder participation, strategic planning, investment in technology and skills, and organizational change management is needed. Overcoming these barriers can enable Industry 4.0’s revolutionary potential and boost businesses' competitiveness and growth. The present study addresses the research objectives, which include:

  • To rank the barriers to the application of Industry 4.0 technology.

  • To construct a structural model based on total interpretive structural modeling (TISM) to investigate the pair-wise interactions between each barrier

  • To classify the identified barriers as dependent, linkage, autonomous and driving based on their dependence and driving powers

Two closely comparable approaches, ISM and TISM, are used to examine the connections and interdependencies between various components or factors in a system. Although both strategies have advantages and uses, it would be incorrect to conclude that one is “better” essentially than the other. The specific needs of the analysis and the complexity of the system under study determine which of ISM and TISM should be used. An easier variation of the process is known as ISM. It focuses on understanding the influence and dependent linkages as well as the hierarchical interactions between the various elements. ISM is helpful when the goal of the analysis is to pinpoint the important variables and the extent of their impact on a system. The hierarchical structure is visually shown, but loops or indirect linkages within the system are not taken into account.

TISM is a method for examining intricate linkages and connections between various components of a system. Understanding the hierarchical structure and connections between these variables is aided by this. The field of systems analysis and decision-making frequently uses TISM. It offers a methodical way to study the interactions between many elements in complex systems. It enables decision-makers to understand the dynamics of the system, set priorities for actions, and create plans for making wise choices and resolving issues. ISM is a more straightforward strategy that emphasizes direct connections and is appropriate for systems with very few interactions. Contrarily, TISM is more thorough and takes into account both direct and indirect interactions, making it appropriate for studying complex systems with a lot of interdependencies. In order to classify the barriers under the above-mentioned category, Fuzzy MICMAC (Matrice d’Impacts Croisés Multiplication Appliqués à un Classement) analysis techniques were applied. Fuzzy MICMAC eliminates the ambiguity that presents itself in the comparison as it allocates a weight-based rating score for each comparison. Organizations can increase the likelihood that Industry 4.0 technologies will be successfully adopted by using Interpretive Structural Modeling to prioritize barriers. By doing this, they can gain important insights into the difficulties of Industry 4.0 implementation and concentrate their efforts on removing the most pressing roadblocks. The study will be useful for managers and deployment experts to analyze the barriers before the actual implementation of Industry 4.0 techniques. This research paper is organized as follows: Section 2 discusses the review of literature; Section 3 explains the methodology followed; Section 4 discusses the research study; Section 5 explains the Fuzzy MICMAC analysis; and Section 6 describes the Results and Discussion. The study is concluded in Section 7.

2. Literature review

The review was performed from two perspectives of Industry 4.0 scenario. The first part discusses about the applications of Industry 4.0 techniques and the second part discusses about the factors of Industry 4.0 that either supports or hinders the deployment in order to identify the key barriers that lead to difficulty in its implementation.

2.1. Literature review on applications of Industry 4.0 techniques

Zhou et al. [17] discussed about the important aspects such as intelligent manufacturing processes and construction of Cyber-Physical Production Systems and their role in deploying a smart factory. They also concluded that strategic planning is one of the core factors for Industry 4.0 implementation and three integrations namely horizontal integration, vertical integration and end-to-end integration are most important for successful deployment. Lu et al. [18] conducted a comprehensive review to study about the applicability and future trends of Industry 4.0. Based on the study, he stated that Cyber Physical System (CPS), Internet of Things (IoT), Enterprise Architecture (EA), information and communications technology (ICT) and Enterprise Integration (EI) are the crucial requirements of Industry 4.0 and these techniques has to be properly deployed for setting up a facility that supports fourth industrial revolution. Scurati et al. [19] created a set of graphical signs and visual manuals to support Augmented Reality technology in deploying Industry 4.0 techniques. This technique was useful in creating a technical document where the maintenance actions are converted into graphical signals that can be processed using data analytics in controlling a firm’s overall production. Xu et al., [20] elaborated about the use of technologies including, cloud computing, cyber-physical Systems, Industrial Integration, IoT, Enterprise Architecture, SOA, Industrial Information Integration and Business Process Management in developing a smart factory. They also concluded that unavailability of powerful tools is considered as one of the biggest obstacles and mentioned that formal methods and systematic tasks bust be enforced before implementing Industry 4.0 tasks for proper deployment.

Yin et al., [21] developed a performance assessment tool based on smart design which can be used to help performance assessment and implementation for a product development process. The developed performance measurement tool will be useful for manufacturing organizations to systematically evaluate each Industry 4.0 tool that supports the product design process. Kumar et al., [22] performed a systematic review to find out the most crucial application frontiers where the need for deployment of Industry 4.0 concepts is more during the COVID-19 pandemic. Based on the review they found out that epidemic control, supply chain, and disaster management are the thrust areas where the need for Industry 4.0 technologies is more demanding during such pandemic times. Kamble et al., [23] stated that integrating the technologies such as IoT, data exchanges, cyber-physical system, and semi-autonomous industrial systems will help a traditional manufacturing to transform into a smart factory. They also developed a Smart Manufacturing Performance Measurement System (SMPMS) framework to evaluate the readiness for adopting Industry 4.0 technologies. The framework uses a combination of empirical and exploratory research design to analyze the performance measures related to the performance measurement of Industry 4.0 techniques.

2.2. Literature review on factors of Industry 4.0

The Fourth Industrial Revolution, often known as Industry 4.0, is defined by the integration of cutting-edge technologies into numerous industries, completely altering how manufacturing and production processes are carried out. The key factors of Industry 4.0 include techniques such as the Internet of Things (IoT), Data Analytics, Artificial Intelligence, CPS, etc. These factors of Industry 4.0 work together to create a revolutionary ecosystem that boosts productivity, efficiency, and creativity across diverse industries. They are interrelated and mutually supportive. Businesses that adopt and use these technologies will have an advantage over rivals in the digital age. The review of various studies that elaborate on the different factors of Industry 4.0 in the literature is shown in Table 1.

Table 1.

Review on factors of Industry 4.0

Research Study Description Type Key Factors
Sanders et al. [24] Analysed the linkage between Industry 4.0 and lean manufacturing and identified the enablers that supports the implementation of the Industry 4.0 scenario Enabler Study Supplier feedback, Employee involvement, Continuous flow
Kamble et al. [25] Identified a set of barriers affecting the adoption of Industry 4.0 scenarios in an Indian Context and applied ISM for classifying such barriers, Barrier Study Lack of clear comprehension about IoT Benefits, High Implementation Cost
Dallasega et al. [26] Identified the enablers of Industry 4.0 and categorized them as Technological, Organizational, Geographical and Cognitive Enablers. The classification helped in understanding the enablers in a precise manner and also in examining the cultural, institutional and social effects of the enablers Enabler study Collaboration Technology, E-business, 3D Printing and web-based technology
Orzes et al. [27] Empirically investigated the difficulties faced by SME’s in Industry 4.0 deployment. A focus group study was performed to determine the effect of the identified barriers on deploying Industry 4.0. Study suggested that Data security concerns and lack of standards are the most important barriers of Industry 4.0 implementation Barrier Study Lack of technical knowledge, Data security concerns, Lack of methodical approach for implementation and lack of standards
Chauhan et al. [28] Identified the barriers that influence the linkage between digitisation and firm’s performance and evaluated them using Analysis of Moment Structure (AMOS). They also classified the barriers as extrinsic and intrinsic examined their relationship with supply chain competency Barrier Study Improper Supply chain policies, Lack of employee involvement
Raj et al. [29] Examined the barriers to Industry 4.0 deployment in manufacturing sectors in context of both developed and developing countries. The barriers were analysed using Grey Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The study suggested that developing countries should focus on the internal capability related barriers and developed countries should focus on the technology-related barriers Barrier Study Lack of a digital strategy alongside resource scarcity, Stringent government regulations
Yadav et al. [30] Developed a model to deploy sustainability in manufacturing organizations using the enablers of Industry 4.0. Study suggested that managerial, environmental and economical enablers make a great contribution toward sustainability deployment in Industry 4.0 scenario Enabler Study Adoption of machine learning system, Effective process management and Adoption of smart factory components
Stentoft et al. [31] Analysed the relationship between drivers and barriers for Industry 4.0 technologies in terms of their readiness and practices. The study was performed in a small and medium sized enterprise perspective and also suggests actions for effective implementation of Industry 4.0 in Danish SME’s Barrier Study Lack of understanding of the strategic importance of Industry 4.0, Lack of qualified workforce, Lack of knowledge about Industry 4.0 and Lack of understanding of the interplay between technology and human beings
Sayem et al. [32] Identified the major hurdles of Industry 4.0 implementation and applied a fuzzy – DEMATEL approach to identify the significant hurdles. The study revealed that lack of qualified workforce was the most significant barrier for Industry 4.0 deployment Barrier Study Lack of technology availability, low maturity of technology, government support and legal issues
Senna et al. [33] Categorized the barriers of Industry 4.0 using a Technology-Organization-Environment framework. ISM methodology and MICMAC approach was applied to prioritize the barriers. Study concluded that organizational barriers had the highest dependency and lowest driving power Barrier Study Need for high level investments, lack of qualified workforce, lack of IoT benefits, Lack of communication

2.3. Research gap

TISM provides a systematic framework to understand the hierarchical structure of these factors, helping organizations prioritize actions and strategies for successful Industry 4.0 implementation. There is a need for research that examines the integration and synergy among the many Industry 4.0 technologies, such as IoT, AI, and robots. Numerous studies have concentrated on these technologies individually. While TISM is primarily a qualitative approach, there is potential to integrate quantitative analysis within the model to provide more precise and data-driven insights for Industry 4.0 implementation. It is clear from the review that the implementation of Industry 4.0 approaches would allow a company to increase its performance and support the company’s ability to maintain global competitiveness in order to adapt to changing market conditions.

A systematic structural model based on the use of TISM and Fuzzy MICMAC methodologies in consultation with industrial experts has not been produced, despite studies on the identification of impediments that hinder Industry 4.0 deployments being accessible. In addition to creating the structural model, it is crucial to compare the barriers to making the model more competitive, pair by pair. In order to fill this gap, the current effort has been made to rank and classify the barriers that prevent the adoption of Industry 4.0 approaches using TISM techniques.

3. Methodology

The present research work focuses on the identification of 10 barriers that are crucial in hindering the Industry 4.0 scenario from the literature. Then the industrial expert inputs and responses are compiled with respect to the relationships between barriers and their mutual dependencies. The TISM technique is applied to analyze the relationship among the barriers. After performing the TISM steps, Fuzzy MICMAC analysis is applied to classify the barriers. One of the limitations of the methodology is that a statistical analysis could be performed along with the structural analysis in order to make the model more reliable and competitive. As the process turns out to be overly long, this has been included as a future scope. The methodology deployed during this study is shown in Fig. 1. The step-by-step procedure [34] followed in applying TISM is explained below

  • Step-I

    : Barrier identification

Fig. 1.

Fig. 1

Research methodology.

This is the initial step of TISM application. Here, the barriers are identified through literature review or from surveys conducted with industrial experts.

  • Step-II

    : Forming relationship among the identified barriers

In this step, inputs have been collected from experts hailing from both industry and academia to determine the barrier’s contextual relationship. The responses have been collected in the form of Yes (Y) or No (N). Yes (Y) if the barrier exhibits an influence on another barrier, and No (N) when the barrier has zero effect on another barrier. This step is most important for constructing the hierarchical model. For example, if Barrier 4 influences Barrier 6, then the relationship will be “Y” or else it will be “N”.

  • Step-III

    : Relationship interpretation

In this step, explanation on how one barrier will influence another barrier has to be provided as it will make the process of understanding the contextual relationship between barriers a lot easier.

  • Step-IV

    : Interpretive logic of pair wise comparison

A comparison of multiple pairs of barriers are performed. Here, the barriers are compared with each other to find the impact of each barrier on other. For a total of n barriers, n(n−1) will be the number of comparisons to formulate the “interpretive logic knowledge base”.

  • Step- V

    : Formation of reachability matrix and performing transitivity check

By replacing all “Y” as “1” and all “N” as “0” from “interpretive logic knowledge base”, Initial reachability matrix is formed. Transitivity has to be checked for the developed initial reachability matrix. The rule of transitivity states that, if Barrier 1 is affecting Barrier 2 and Barrier 2 is affecting Barrier 3, then Barrier 1 can affect Barrier 3. Final reachability matrix is developed after checking for transitivity.

  • Step-VI

    : Level partitioning

In order to find the levelness and position of the barriers in the hierarchical model, Level partitioning is performed. Level partition is to be performed for forming the diagraph. Antecedent and reachability sets are created for each barrier using the final reachability matrix. Barriers that have the same intersection and reachability set are considered level 1 barriers and are placed at the top level in the diagram. To find out about other levels of barriers, the above-mentioned procedure is carried out.

  • Step-VII

    : Diagraph development

Barriers are represented in a diagraph which is a representation of the hierarchical structure, where the barriers are placed in order of its level formed after performing level partitioning, the level 1 barriers must be placed at the top and last level barriers must be placed at the bottom.

  • Step-VIII

    : Formation of total interpretive structural model

The TISM model is generated through the combination of the interpretive matrix and diagraph. The digraph nodes are replaced by barrier interpretation and the interpretive matrix finding is described in direct lines with the barrier-to-barrier relation. This will result in the creation of a total interpretive structure model.

4. Data collection

In order to produce valuable insights into the intricate relationships between the items under inquiry, conducting a good TISM analysis necessitates careful expert selection and efficient facilitation. The experts required for collecting the data for the analysis were identified based on their understanding of the domain of the study. An expert committee comprising six practitioners and four academicians was established to examine the barriers to the adoption of Industry 4.0 technologies that have been found. The complexity of the problem and the scope of the analysis can affect how many specialists are involved in the ISM process. More specialists may produce more reliable results, but managing the debates may be more difficult. Considering this point, the number of experts was fixed at 10. The practitioners come from various industrial firms and have an extensive understanding of the use of Industry 4.0 and smart factory technologies. The academicians possess a wide knowledge of the different Industry 4.0 technologies and are also involved in the deployment of these technologies in various organizations. The experts hail from different geographical locations. Considering the time zone differences, online meetings were scheduled for group interactions and discussions, and responses were collected and recorded during such sessions.

The fact that the expert may lack common sense in certain decision-making and that experts are not always in a position to defend their logic and reasoning are two faults that are readily apparent in this form of examination. Errors can also arise from a lack of understanding and result in poor choices. By giving the respondents clear instructions regarding the survey and its results, these inaccuracies can be avoided or eliminated. To gather the comments and analyze the relationships, a number of brainstorming sessions were planned with a facilitator. Prior to the brainstorming, the facilitator also held an introductory session to give a thorough explanation of the chosen barriers.

The experts were consulted throughout a series of brainstorming meetings to get their opinions in order to establish a contextual relationship between the identified barriers. All of the contributing experts' official approval was required before the final contextual relationship could be determined. A “leads to type” of relationship was used to record the responses. This connection demonstrates how one barrier affects the other. The profile of the expert committee is shown in Table 2.

Table 2.

Profile of the experts.

Expert ID Domain Area Sector Experience (Years)
BI1 Production Manufacturing 6
BI2 Planning Manufacturing 12
BI3 Industrial Engineering Manufacturing 14
BI4 Big Data Analytics Information Technology 5
BI5 IoT, Automation Information Technology 6
BI6 Mechatronics Manufacturing 10
BI7 Lean Manufacturing Academia 14
BI8 Smart manufacturing Academia 12
BI9 Robotics and Sensors Academia 8
BI10 Quality engineering Academia 10

5. Study

The prime objective of the study is to identify the barriers pertaining to Industry 4.0 and to create a hierarchical model of barriers using TISM approach. Steps to be followed for application of TISM are discussed below:

  • Step-I

    : Identification and description of barriers:

The first step is to identify the barriers that hinder the implementation of Industry 4.0 technologies. These barriers can be identified through a literature review, expert interviews, surveys, or other data collection methods. The identified barriers should be relevant and specific to the context of the study. 10 such barriers that are critical for Industry 4.0 deployment have been identified from the literature. These 10 barriers heavily affect the Industry 4.0 implementation process. The definition of each barrier is given in Table 3.

  • Step-II

    : Forming relationship among the identified barriers

Table 3.

Description of the selected barriers.

S.
No
Barrier Descriptions Reference
1 Lack of top management commitment In general, organizational policy and acceptable behaviors start at the top management level. The function of top management is not only to take decisions that affect employees but also to show commitment, which motivates the workers to implement Industry 4.0 with the realization of its impact and assistance. In order to accomplish these new industry advancement initiatives, top-level management should be focused, and their participation is mandatory. Kumar et al., [35]
Muruganantham et al., [36]
2 Inadequate Training Before attempting to use it, understanding how a machine works is very important. The successful implementation of Industry 4.0 is dependent on its employees because user engagement with machines is an integral part of it. Therefore, adequate training should be given to each worker to work with the machines and to recognize their position in the industry as their future goal. Inadequate training leads to unnecessary expenses, and companies can’t overlook their objectives. Antony et al., [37]
3 Lack of IT infrastructure Industry 4.0 is an encapsulation of IT elements such as networks and security, storage and servers, business applications, operating systems, and databases. It functions as a bridge connecting the manufacturing machines and the IT software applications through CPS. Hence, reliable and efficient management of IT infrastructure is necessary for the successful deployment of Industry 4.0. This is found to be a crucial barrier, as its persistence is critical for the project’s start-up. Moktadir et al., [38]
Raj et al. [29]
4 Lack of Cyber Physical Systems The interconnection of various manufacturing machines for the purpose of real-time data sharing manifests the sole objective Industry 4.0. This is achieved by CPS, which is identified as an integral component of Industrial IOT and is a network of machine-to-machine embedded systems that are interconnected with the ability to monitor and manipulate industrial processes. CPS systems are designed to enhance the product’s quality, availability, and reliability. Yao et al. [39]
Schleinkofer et al. [40]
5 Unavailability of Funds Implementing Industry 4.0 requires a huge sum. IT infrastructure with CPS, training for employees, and upgradation of machinery demand a lot of money. Separate allocations of funds for each category are necessary as part of the overall build-up to Industry 4.0. This is a prime barrier for small-scale enterprises. Chauhan et al., [28]
6 Non upgradation of Mechatronic systems State-of-the art mechatronic systems with constant upgrades are the infrastructure that lays the foundation for the implementation of Industry 4.0. Lack of such infrastructure is a hurdle, and top-level management needs to invest in the advancement of existing infrastructure to attain the full potential of an organization. Chen et al., [41]
7 Longer learning cycles Sufficient education and knowledge in the various domains of Industry 4.0, such as IoT and systems engineering, is a pivotal encounter to be faced, and the lack of it is a massive setback. This can be compensated by longer learning cycles, which are not affordable for a cost-effective venture. Top-level management has to intervene and train employees and review these training performances periodically for a swift conversion of ill-equipped employees, as a lack of education can make people act indifferent and perfunctory. People with a highly diverse skill set are essential to enabling adaptability with technology, which is predicted to be in constant flux in the years to come. Souza et al., [42]
8 Lack of Agility in Supply Chains Agility in supply chains is crucial in the modern business landscape to achieve streamlined operations while satisfying the cardinal objective of any supply chain, which is getting the right product to the right customers at the right time. Espousing agile supply chains has a positive effect on performance levels. Lack of agility will lead to the functioning of the entire process in a manner where its full potential will not be unlocked. Ghadge et al., [43]
9 Stringent National Policies The government’s industrial policies are stringent and compatible only with the previous generation of industries. New laws and policies have to be put into action to improve the performance of industries through the integration of Industry 4.0. On the contrary, strict policies regarding the standardization of Industry 4.0 and data protection need to be deployed for structured, safe, and robust manufacturing. Oztemel et al., [44]
10 Improper Communication Models Improper communication can lead to misinformation and pave the way for wrongful strategies to be implemented, which hinders efficacy and productivity. Kamble et al., [45]
Rane et al. [46]

The relationship among two barriers is identified as “Barrier 1 will influence or enhance Barrier 2”. Suggestions and opinions were collected from the experts working on Industry 4.0 to define the conceptual relationship between barriers and also from those who are highly expert in Industry 4.0. The responses are collected as a Yes (Y) or No (N) answer depending on the effect of one barrier on another barrier.

  • Step-III

    : Relationship interpretation

The responses collected from the team of experts were used to establish the relationship between each barrier. If the response is recorded as “Yes”, it is recorded that Barrier 1 will influence Barrier 2. This has to be done for every pair of barriers.

  • Step-IV

    : Interpretive logic of pair wise comparison

Each barrier is compared to every other barrier to establish the direction of their interaction before being combined into the interpretive matrix. Binary (directed) or non-binary (undirected) relationships are both possible. Usually, the interpretation of relationships is based on professional judgment and an awareness of the obstacles. To demonstrate the relationship among the barriers, a pair-wise comparison has been performed. The responses have been recorded in the form of Yes (Y) or No (N). If the response was “Yes”, the reason for influence has to be justified. An excerpt of the “interpretive logic knowledge base” is shown in Table 4.

  • Step-V

    : Formation of reachability matrix and performing transitivity check

Table 4.

Sample interpretive logic knowledge base.

S.
No
Linkage Interaction Response Explanation
1 10–8 Improper Communication Models will influence Lack of Agility in Supply Chains Y Effective communication is considered as one of the important measures of agility
2 10–7 Improper Communication Models will influence Longer learning cycles Y Improper communication has a larger influence on the learning cycles
3 10–6 Improper Communication Models will influence Non upgradation of Mechatronic systems Y Effective communication and transfer of information is required for alerting the management about up gradation of systems
4 10–5 Improper Communication Models will influence unavailability of Funds N Communication and allocation of funds has no connectivity
5 10–4 Improper Communication Models will influence Lack of Cyber Physical Systems N Cyber Physical Systems are considered as technological infrastructure and has no linkage to improper communication

The reachability matrix, which shows the link between barriers in terms of reachability, is developed from the interpretive matrix. It establishes whether one barrier can directly or indirectly affect another barrier. The reachability matrix creates a hierarchy among the barriers and aids in identifying the levels of relationships. From the “interpretive logic knowledge base”, the initial reachability matrix is developed by changing all “Y” to “1” and all “N” to “0,” as shown in Table 5. A transitivity check is to be performed for the developed reachability matrix. The final reachability matrix is developed after checking for transitivity, as shown in Table 6.

  • Step-VI

    : Level partitioning

Table 5.

Initial reachability matrix.

10 9 8 7 6 5 4 3 2 1
1. Lack of top management commitment 1 1 1 1 1 1 1 1 1 1
2. Inadequate Training 1 1 1 1 1 0 0 0 1 0
3. Lack of IT infrastructure 0 0 1 0 1 0 1 1 0 0
4. Lack of Cyber Physical Systems 0 0 0 0 0 0 1 1 0 0
5. Unavailability of Funds 1 1 1 1 1 1 1 1 0 0
6. Non upgradation of Mechatronic systems 1 1 1 1 1 0 1 1 1 0
7. Longer learning cycles 1 1 1 1 1 0 0 1 0 0
8. Lack of Agility in Supply Chains 1 1 1 1 1 0 1 1 1 0
9. Stringent National Policies 1 1 0 1 1 0 0 1 0 0
10. Improper Communication Models 1 1 1 1 1 0 0 0 0 0

Table 6.

Final reachability matrix.

10 9 8 7 6 5 4 3 2 1
1. Lack of top management commitment 1 1 1 1 1 1 1 1 1 1
2. Inadequate Training 1 1 1 1 1 0 0 0 1 0
3. Lack of IT infrastructure 0 0 1 1* 1 0 1 1 0 0
4. Lack of Cyber Physical Systems 0 0 0 0 0 0 1 1 0 0
5. Unavailability of Funds 1 1 1 1 1 1 1 1 0 0
6. Non upgradation of Mechatronic systems 1 1 1 1 1 1* 1 1 1 0
7. Longer learning cycles 1 1 1 1 1 0 0 1 0 0
8. Lack of Agility in Supply Chains 1 1 1 1 1 1* 1 1 1 0
9. Stringent National Policies 1 1 1* 1 1 0 0 1 0 0
10. Improper Communication Models 1 1 1 1 1 0 0 0 0 0

Based on the links between the barriers, the reachability matrix is used to divide the obstacles into several tiers. The highest-level obstacles are referred to as driving barriers, since they have a direct impact on lower-level barriers. The driving barriers determine the barriers at later levels, and these barriers may also indirectly affect other barriers. The barriers that have the same reachability and intersection set are considered to be at level 1 and are placed at the top level of the diagraph hierarchy. This step is repeated until the levels of all barriers are calculated. Five such level partitioning iterations were performed and are shown in Table 7.

  • Step-VII

    : Diagraph development

Table 7.

Level partitioning of reachability matrix.

Iteration 1
Barrier Reachability set Antecedent set Intersection set Level
1 {1,2,3,4,5,6,7,8,9,10} {1} {1}
2 {2,6,7,8,9,10} {1,2,5,8} {2,8}
3 {3,4,6,7,8} {1,3,4,5,6,7,8,9} {3,4,6,7,8} I
4 {3,4} {1,3,4,5,6,8} {3,4} I
5 {3,4,5,6,7,8,9,10} {1,5,6,8} {5,6,8}
6 {2,3,4,5,6,7,8,9,10} {1,2,3,5,6,7,8,9,10} {2,3,5,6,7,8}
7 {3,6,7,8,9,10} {1,2,3,5,6,7,8,9,10} {3,6,7,8,9,10}
8 {2,3,4,5,6,7,8,9,10} {1,2,3,5,6,7,8,9,10} {2,3,5,6,7,8}
9 {3,6,7,8,9,10} {1,2,5,6,7,8,9,10} {6,7,8,9,10}
10
{6,7,8,9,10}
{1,2,5,6,7,8,9,10}
{6,7,8,9,10}
I
Iteration 2
Barrier
Reachability set
Antecedent set
Intersection set
Level
1 {1,2,5,6,7,8,9,} {1} {1}
2 {2,6,7,8,9} {1,2,5,8} {2,8}
5 {5,6,7,8,9} {1,5,6,8} {5,6,8}
6 {2,5,6,7,8,9} {1,2,5,6,7,8,9} {2,5,6,7,8}
7 {6,7,8,9} {1,2,5,6,7,8,9} {6,7,8,9} II
8 {2,5,6,7,8,9} {1,2,5,6,7,8,9} {2,5,6,7,8}
9
{6,7,8,9}
{1,2,5,6,7,8,9}
{6,7,8,9}
II
Iteration 3
Barrier
Reachability set
Antecedent set
Intersection set
Level
1 {1,2,5,6,8,} {1} {1}
2 {2,6,8} {1,2,5,8} {2,8}
5 {5,6,8} {1,5,6,8} {5,6,8} III
6 {2,5,6,8} {1,2,5,6,8} {2,5,6,8} III
8
{2,5,6,8}
{1,2,5,6,8}
{2,5,6,8}
III
Iteration 4
Barrier
Reachability set
Antecedent set
Intersection set
Level
1 {1,2} {1} {1}
2
{2}
{1,2}
{2}
IV
Iteration 5
Barrier
Reachability set
Antecedent set
Intersection set
Level
1 {1,} {1} {1} V

A directed graph (digraph) is built using the partitioned levels and reachability matrix. The diagram illustrates the relationships and hierarchical structure between the obstacles. The dependent barriers are positioned below the driving barriers, reflecting the interdependence between the two. The diagram comprises the barriers placed at their respective levels. In a diagraph, the barriers are connected by solid lines and broken lines. The solid lines represent the direct relationship among the barriers, and the broken lines represent the transitive links. The developed diagram is shown in Fig. 2.

  • Step-VIII

    : Formation of total interpretive structural model

Fig. 2.

Fig. 2

Developed diagraph.

The TISM analysis’s findings must be interpreted at the last stage. The driving barriers, the dependent barriers, and the important pathways or clusters of barriers are highlighted, along with the hierarchy and interactions among the barriers. The findings shed light on the obstacles that require intentional overcoming in order to get Industry 4.0 up and running. A total interpretive structure model comprising the barriers and their relationships is shown in Fig. 3.

Fig. 3.

Fig. 3

Developed TISM model comprising of barriers of Industry 4.0.

6. Fuzzy MICMAC

In MICMAC analysis, the factors are analysed depending on their dependence and driving powers. The interrelationship among the factors may not be the same every time, as it may be either weak or very weak or strong or very strong [47]. In many cases, the MICMAC analysis fails to record this interrelationship [48,49] To rectify this drawback in ISM models, a Fuzzy set is being applied to improve the validity of the MICMAC analysis. The combined approach is called Fuzzy MICMAC analysis. The step-by-step procedure for applying the Fuzzy MICMAC analysis is elaborated below.

6.1. Establishing a Binary Direct Relationship Matrix

A Binary Direct Reachability Matrix (BDRM) is formed by analysing the interrelationship among the barriers in TISM as shown in Table 6. The diagonal entities are replaced to zero in Table 6. The obtained BDRM is shown in Table 8.

Table 8.

Binary direct reachability matrix.

10 9 8 7 6 5 4 3 2 1
1. Lack of top management commitment 0 1 1 1 1 1 1 1 1 1
2. Inadequate Training 1 0 1 1 1 0 0 0 1 0
3. Lack of IT infrastructure 0 0 0 1* 1 0 1 1 0 0
4. Lack of Cyber Physical Systems 0 0 0 0 0 0 1 1 0 0
5. Unavailability of Funds 1 1 1 1 0 1 1 1 0 0
6. Non upgradation of Mechatronic systems 1 1 1 1 1 0 1 1 1 0
7. Longer learning cycles 1 1 1 1 1 0 0 1 0 0
8. Lack of Agility in Supply Chains 1 1 1 1 1 1* 1 0 1 0
9. Stringent National Policies 1 1 1* 1 1 0 0 1 0 0
10. Improper Communication Models 1 1 1 1 1 0 0 0 0 0

6.2. Fuzzy direct reachability matrix

After BDRM formation, Fuzzy sets are being deployed for assessing the relationship among the barriers. Fuzzy sets are defined as membership functions with a unit interval that ranges from [0, 1]. Linguistic scale values are used for this assessment as shown in Table 9. The evaluation is performed with experts who provided responses in linguistic scale for developing the linguistic assessment direct reachability matrix (LADRM). A triangular fuzzy number is defined with a lower value, an upper value and middle limit value represented as a, b and c in which a < b < c. A Fuzzy direct reachability matrix (FDRM) as shown in Table 10 is formed by de-fuzzifying the Fuzzy numbers to form a crisp value.

Table 9.

Linguistic scale used for assessment [50].

Linguistic Variable Fuzzy Number
No impact (No) (0, 0, 0)
Very low impact (VL) (0, 0.1, 0.3)
Low impact (L) (0.1, 0.3, 0.5)
Medium impact (M) (0.3, 0.5, 0.7)
High impact (H) (0.5, 0.7, 0.9)
Very high impact (VH) (0.7, 0.9, 1)
Complete impact (C) (1, 1, 1)

Table 10.

Linguistic assessment direct reachability matrix.

10 9 8 7 6 5 4 3 2 1
1 0 L H H VH VH VH VH C 0
2 M 0 M H VL 0 0 0 0 0
3 0 0 0 L C 0 C 0 0 0
4 0 0 0 0 0 0 0 C 0 0
5 VH C H L 0 0 VH C 0 0
6 L VL L L 0 0 H H VL 0
7 M N VL 0 VL 0 0 M 0 0
8 H VL 0 M L N N 0 M 0
9 N 0 N N N 0 0 VL 0 0
10 0 N VH H VL 0 0 0 0 0

6.3. Stabilization of fuzzy matrix

Based on the provided Linguistic response, a fuzzy number is assigned for each response to form the Fuzzy MICMAC Stabilized matrix is shown in Table 11. The rule of Fuzzy matrix [51] is shown as below

C=max{minaij.bij}

Table 11.

Fuzzy MICMAC Stabilized matrix.

10 9 8 7 6 5 4 3 2 1 DR
1 0 0.3 0.7 0.7 0.9 0.9 0.9 0.9 1 0 6.3
2 0.5 0 0.5 0.7 0.1 0 0 0 0 0 1.8
3 0 0 0 0.3 1 0 1 0 0 0 2.8
4 0 0 0 0 0 0 0 1 0 0 1
5 0.9 1 0.7 0.3 0 0 0.9 1 0 0 4.8
6 0.1 0.1 0.3 0.3 0 0 0.7 0.7 0.1 0 2.3
7 0.5 0 0.1 0 0.1 0 0 0.5 0 0 1.2
8 0.7 0.1 0 0.5 0.3 0 0 0 0.5 0 2.1
9 0 0 0 0 0 0 0 0.1 0 0 0.1
10 0 0 0.9 0.7 0.1 0 0 0 0 0 1.7
DE 2.7 1.5 3.2 3.5 2.5 0.9 3.5 4.2 1.6 0

The driving power and dependence power of barriers affecting Industry 4.0 deployment in Fuzzy MICMAC is obtained by summing up the values of interactions of columns for dependence power and interactions of rows for driving power.

7. Results and discussions

The TISM model has been built by iterating the digraph based on the total interpretive logic base as obtained from the experts. The top level of the TISM depicts the barriers that are most dependent on or influential on others. The barriers, namely Lack of IT Infrastructure, Lack of cyber physical systems, and Improper Communication models, are such barriers. The barriers that occupy the bottom of the hierarchy are the most independent barriers. They have the most driving power and overcome all other barriers. Identifying such barriers early would make the implementation of Industry 4.0 hassle-free. The barrier of lack of top management commitment is the most independent barrier and occupies the bottommost level of the TISM model.

Careful planning, investments in workforce development, strong cybersecurity measures, stakeholder collaboration, and strategic leadership dedicated to embracing Industry 4.0's revolutionary potential are necessary for removing these hurdles. By overcoming these obstacles, industries that use Industry 4.0 technology can become more competitive, efficient, and innovative. Based on the graph obtained after performing Fuzzy MICMAC analysis, as shown in Fig. 4, the barriers were classified into four clusters based on their driving and dependence powers. The four clusters are analysed below:

  • Cluster A: Autonomous barriers

Fig. 4.

Fig. 4

Graph obtained based on Fuzzy MICMAC Analysis.

Autonomous barriers are barriers that possess both less driving power and less dependence power. These barriers will not impact the implementation on a larger scale but have to be eliminated to facilitate the proper deployment of concepts. The barriers, namely Stringent National Policies and Inadequate training, are such barriers. The functioning of autonomous barriers can be hampered since it may be difficult to directly impact them and they may not react to changes in other components. They may serve as bottlenecks or barriers in the system, preventing it from producing the required results or goals.

  • Cluster B: Dependent barriers

The barriers, namely Lack of IT infrastructure, Lack of cyber-physical systems, non-upgradation of mechanical systems, longer learning cycles, Lack of Agility in Supply chains, and Improper Communication models, are identified as the dependent barriers. These barriers have high dependence power but less driving power. They occupy the top level of the TISM hierarchy. The firm must carefully analyze these barriers and systematically remove them. Organizations can increase system stability and resilience by identifying and mitigating dependent barriers. Knowing how elements are interconnected makes planning and decision-making more efficient, ensuring that steps taken to remove higher-level obstacles also take into account the potential effects on dependent elements.

  • Cluster C: Linkage barriers

The Linkage barriers possess high driving power and as high dependence power. These barriers are considered to be unstable has a larger influence on the system. The present study does not have any linkage barriers.

  • Cluster D: Driver barriers

The barriers Lack of Top Management Commitment and the unavailability of Funds were identified as the driving barriers. These barriers have the highest driving power and have no dependence power. These barriers will drive the other barriers. These driver barriers are considered the root cause of other barriers, and the firm must ensure that stringent steps are being deployed for the elimination of such barriers. They have a high level of influence on other elements and can significantly impact the system’s behaviour. Based on their effect and reliance on other system elements, elements in ISM are often characterized as “drivers” or “dependents.” “Driver elements” are those that exert a large amount of influence on other elements and have the potential to drastically alter the behaviour of the system. Conversely, “dependent elements” are those parts of the system that have little impact and are extremely reliant on other parts of system. A comparative study of the results obtained in the present study with previous studies is shown in Table 12.

Table 12.

Comparative study of results.

Research Study Context Methodology Driving Barriers
Kumar et al., [52] Industry 4.0 Barriers ISM Lack of adequate skills,
Uncertainty about economic benefits
Vinodh [53] Lean with Industry 4.0 barriers ISM Increasing Competitive Pressure, Lack Of Long-Term Vision, Lack Of Management Support, Lack Of Capital Fund
Kamble et al., [25] Barriers of Industry 4.0 in Indian manufacturing context ISM Lack of clear comprehension about IoT benefits, High implementation cost
Majumdar et al. [54] Barriers of Industry 4.0 in textile sector ISM Lack of understanding and commitment of top management, lack of government support and policies for Industry 4.0
Present Study Barriers of Industry 4.0 TISM Lack of Top Management, Commitment and Unavailability of Funds

7.1. Removal of barriers influencing Industry 4.0 scenario

After forming the TISM Model and classifying the barriers, the results were presented to a committee consisting of practitioners belonging to different manufacturing organizations and academicians. The position of each barrier in the TISM model and the classification of the barriers in their respective clusters were also analysed. The committee was satisfied with the outcome of the model and stated that the identified driving and dependent barriers are properly aligned as per the industrial implantation scenario. The committee also proposed certain improvements to remove these barriers. The proposed improvement actions are as follows:

  • 1.

    The firm must plan a proper strategy under efficient leadership to systematically deploy Industry 4.0 concepts. The drive starts with the commitment of the top management to influence the employees and make sure that enough knowledge is being transferred to them regarding the core concepts and implementation.

  • 2.

    Adequate funds must be allocated by the top management in advance to spend on the IT-based infrastructure and Physical systems. Proper Budgeting must be prepared well in advance to support this and to deploy the implementation process without any delays or breaks.

  • 3.

    The firm must invest in programs that will retrain and upskill the workforce in order to provide them with the digital and technological capabilities needed for Industry 4.0. Create a culture that values lifelong learning and ongoing development.

The firm must establish supportive laws and policies that promote the implementation of Industry 4.0 while promoting the moral and responsible use of technology. Avoid overly rigid standards that limit creativity and adaptability.

Employee skills must be properly enhanced, and adequate training must be provided to them pertaining to the technological advancements and usage of IT and computer-aided tools. Focus must be placed on the core values of the individual value chains to enhance their performance. Collaboration between governmental entities, businesses, academic institutions, and communities is necessary to remove these barriers. Additionally, it calls for a forward-thinking strategy that accounts for the long-term effects of adopting Industry 4.0 while guaranteeing fair chances for all parties concerned.

7.2. Managerial implications

The developed TISM model helped identify the driving and dependent barriers that hinder the implementation of Industry 4.0 technologies. Systematic removal of barriers by adopting effective strategies would help the firms mitigate these barriers early and focus on consistent deployment of concepts. Organizations and industries may be significantly impacted by the outcomes of the application of Industry 4.0 technology. The study revealed that lack of Top Management Commitment and Unavailability of Funds were the most driving barriers. It is crucial to educate senior executives about the potential advantages of Industry 4.0 and its strategic relevance to the business in order to overcome the lack of top management commitment. This can be accomplished by giving case studies and examples of successful implementations, as well as by educating and training people. Including top management in pilot programs, involving them in decision-making processes, and highlighting early successes can all inspire excitement and commitment.

Additionally, encouraging senior management to actively support and drive the deployment of Industry 4.0 can be accomplished through building a culture of innovation, cooperation, and continuous learning inside the firm. When seeking to integrate Industry 4.0 technology, businesses may encounter a substantial hurdle in the form of a lack of funding. Throughout the implementation process, insufficient financial resources may cause difficulties. To address this issue, the organization may follow strategies like seeking external funds, cost optimization methods, long-term partnerships, and collaborative partnerships. By deploying these strategies, the organization can overcome the challenge of a shortage of funds. The findings from the model would help practitioners deploy the concepts systematically as the barriers are analysed prior to implementation.

The firm must focus on creating a workforce with a skill set that suits the adoption of Industry 4.0 in order to perform a technology-cantered manufacturing task. A proper communication model and cyberphysical systems must be installed by the firm to ensure proper connectivity and data security. Implementing Industry 4.0 technology has wide-ranging effects that can affect a variety of areas of businesses, markets, and society at large. Organizations may develop a competitive advantage, spur innovation, and adjust to the changing digital landscape by embracing and effectively utilizing these technologies. Technologies like Artificial Intelligence (AI) and Machine Learning (ML), Blockchain, Augmented Reality (AR) and Virtual Reality (VR) Integration, Big Data analytics, and Predictive Analytics can contribute to the ongoing advancement and successful implementation of Industry 4.0 technologies, address emerging challenges, and explore new possibilities for enhanced productivity, sustainability, and human well-being in the digital era. Results from research can shed light on the potential, problems, and barriers related to the implementation of Industry 4.0. This knowledge can be used by policymakers to create rules and laws that address the impediments found, encourage innovation, and aid in the adoption of Industry 4.0 technology. For instance, policy may emphasize boosting skill-development initiatives, offering financial incentives for technology adoption, or establishing favourable regulatory environments.

During implementation, the implications may change from organization to organization due to the availability of technological features and geographical location. Establishing and following norms of behaviour and ethical principles specifically for the implementation of Industry 4.0 and including ethics education and training programs for staff members involved in the deployment of Industry 4.0 can foster responsible and sustainable implementation of Industry 4.0 technologies, ensuring that the benefits are shared equitably and the potential harms are minimized. Organizations may systematically remove the barriers and improve the odds of a successful deployment of Industry 4.0 technology by leveraging the insights generated from TISM modeling to inform practical actions. It enables businesses to adopt a focused and organized strategy, minimizing implementation risks and leveraging the advantages of Industry 4.0 adoption.

7.3. Limitations and future scope

The opinion and expertise of experts are crucial to TISM modeling. The results of the analysis can be impacted by the choice of experts and their biases. There may be subjectivity in the assessment of links and interdependence among barriers due to the diverse perspectives, backgrounds, and experiences of different specialists. The main goal of TISM modeling is to examine the connections and interdependencies between the identified obstacles. It might not, however, fully reflect the complexity of the implementation context. The modeling framework may not sufficiently account for elements like company culture, difficulties unique to a given industry, and outside influences. Since TISM modeling does not offer quantitative measurements of the interactions between barriers, it mostly concentrates on qualitative analysis. To determine links between items, TISM mainly relies on subjective and expert judgment. When multiple experts are involved, this subjectivity might introduce biases and produce diverse results. This can make it difficult to rank and evaluate barriers fairly because it is difficult to measure the strength or influence of the relationships.

When undertaking TISM modeling, it is crucial to take a rigorous and open approach in order to reduce these restrictions and inherent biases. A wide group of specialists should be included, the outcomes should be validated through numerous iterations, the assumptions made should be rigorously examined, and different viewpoints should be taken into account. TISM is a tool that may be used to assess organizational structures, pinpoint the critical elements that affect an organization’s effectiveness, and create effective management plans. It can support organizations in streamlining their procedures and assets. TISM can be used to research how new technologies are adopted into existing systems. In order to successfully integrate technology, it can assist in recognizing potential difficulties and roadblocks. In the future, more such barriers could be identified, and a statistical validation could also be performed to make the model more competitive. Furthermore, using TISM modeling in conjunction with other quantitative and qualitative techniques can help provide a more thorough knowledge of the implementation problems and potential fixes. By addressing these points, organizations will be better able to understand the deployment of Industry 4.0 and create more successful adoption plans for the technology. New research gaps could appear as Industry 4.0 continues to develop, offering chances for more research and development.

8. Conclusion

Industry 4.0 technology helps manufacturing firms cope with current challenges by adopting flexible strategies supported by smart technologies. These technologies help increase the pace and innovation of the manufacturing process and focus on improving customer satisfaction. This research work focuses on identifying and prioritizing the barriers that hinder the implementation of Industry 4.0 in manufacturing organizations. 10 Barriers that are crucial in the deployment of Industry 4.0 technologies were identified from the literature and reviewed by a team of experts. The relationship between these barriers was analysed by the TISM and Fuzzy MICMAC approaches. Based on the analysis, the barriers of IT infrastructure, Lack of cyber physical systems, and Improper Communication models occupy the top level of the hierarchy, while the barrier of lack of Top Management Commitment occupies the bottom level of the TISM hierarchy. Based on the results obtained from the Fuzzy MICMAC approach, clustering of barriers was performed. The barriers, namely, IT infrastructure, Lack of cyber physical systems, and Improper Communication models, were identified as the most dependent barriers, and the lack of Top Management Commitment was identified as the most driving barrier. To successfully implement Industry 4.0 technologies, a firm must concentrate on the driving barriers to systematically control and remove the dependent barriers. The use of TISM modeling enables the identification of key obstacles that significantly affect the implementation of Industry 4.0 techniques.

Funding statement

The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number (RGP2/48/44). Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R237), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. This study is supported via funding from Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).

Data availability statement

The data used in this article was not collected from any public repository. The data collected as responses for this study was collected from individuals working in the case organization.

CRediT authorship contribution statement

R. Ben Ruben: Conceptualization. C. Rajendran: Data curation. R. Saravana Ram: Formal analysis. Fadoua Kouki: Investigation. Haya Mesfer Alshahrani: Methodology. Mohammed Assiri: Validation.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used in this article was not collected from any public repository. The data collected as responses for this study was collected from individuals working in the case organization.


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