Integrating industry 4.0 in manufacturing: overcoming challenges and optimizing processes (case studies)

Abstract

Purpose

This paper aims to identify potential barriers to Industry 4.0 adoption for manufacturers and examine the changes that must be made to production processes to implement Industry 4.0 successfully. It aims to develop technology by assisting with the successful implementation of Industry 4.0 in the manufacturing process by using smart system techniques.

Design/methodology/approach

Multiple case studies are used in this paper by using the smart system and Matlab, and semi-structured interviews are used to collect qualitative data.

Findings

Standardization, management support, skills, and costs have been cited as challenges for most businesses. Most businesses struggle with data interoperability. Complexity, information security, scalability, and network externalities provide challenges for some businesses. Environmental concerns are less likely to affect businesses with higher degrees of maturity. Additionally, it enables the Technical Director’s expertise to participate in the measurement using ambiguous input and output using language phrases. The outcomes of the numerous tests conducted on the approaches are extensively studied in the provided method.

Originality/value

In this research, a multiple-case study aims to carry out a thorough investigation of the issue in its actual setting.

Citation

Fekrisari, M. and Kantola, J. (2024), "Integrating industry 4.0 in manufacturing: overcoming challenges and optimizing processes (case studies)", The TQM Journal, Vol. 36 No. 9, pp. 347-370. https://doi.org/10.1108/TQM-12-2023-0411

1. Introduction

Due to rapid changes in the market environment caused by globalization and technological breakthroughs, modern manufacturing companies are under increasing pressure to increase competitiveness through cost-effective manufacturing methods. Adamson et al. (2017). This necessitates a shift towards Industry 4.0 technologies such as automation and robotics, particularly for small and medium-sized enterprises (SMEs) within the European Union (EU) seeking to strengthen their market position (Heilala, 2022). Moreover, as competition evolves from individual companies to integrated supply chains (Lambert and Cooper, 2000; Christopher, 2011).

Industry 4.0, which is defined by the incorporation of cutting-edge technologies like IoT, artificial intelligence, and cloud computing into production processes, is the most recent wave of industrial change. The business environment of industrial companies is being significantly disrupted by this integration, creating both opportunities and challenges (Büchi et al., 2020).

Industry 4.0 offers three benefits: reducing operational costs, improving efficiency and generating additional income (Geissbauer et al., 2016). However, organizations face the challenge of determining how I4.0 technologies support existing processes (Ganzarain and Errasti, 2016), and the best way is to take advantage of its benefits (Liao et al., 2017).

Organizations often tend to give priority to the implementation of technology before establishing a clear understanding of the organizational and environmental requirements of I4.0 (Senna et al., 2022).

The first step in developing the strategy is to assess how organizations are prepared to adopt Industry 4.0 technologies (Antony et al., 2021; Krishnan et al., 2021). The maturity assessment model is one of the most common tools for understanding the level of digital readiness of businesses (Felch et al., 2019). These tools help companies understand their development and progress.

In the highly competitive world of today, manufacturing companies face a variety of dynamic difficulties. Combining Industry 4.0 with lean Six Sigma improvement approaches has become a prominent strategy for achieving organizational excellence in recent years (Samanta et al., 2023).

Machines in a production network can now share and analyze data to produce insightful findings. Information can be produced quickly and in great amounts. A manufacturing network’s interconnected equipment, systems, and nodes produce more data than ever before. All nodes in the production system gather and share data, which is then analyzed to develop knowledge. Machines independently converse and collaborate as opposed to functioning alone (Manesh et al., 2020).

Small and medium-sized enterprises (SMEs) recognize the impact of industry 4.0 and integrate digital technologies into their processes, increasing productivity (profits or market positions) or supporting supply chains to meet the needs of Industry 4.0 development (Müller et al., 2018; Horvath and Szabo, 2019; Masood and Sonntag, 2020; Uygun and Aydin, 2021; Singh et al., 2022).

The most common obstacles for small and medium-sized enterprises to adopt digital technology include limited financial resources and unclear economic benefits, cultural challenges ranging from management lack of support to employee resistance, lack of competent and technical employees, inadequate IT infrastructure, technical obstacles such as lack of standards and uncertainty of system reliability, and legal concerns about data security (Orzes et al., 2018).

Many researchers point out the gap between the actual implementation of Industry 4.0 in the workplace and the research topic of Industry 4.0. Industry 4.0 does not have a universally accepted definition, but is usually based on the concept of a “smart factory” where each machine is connected and able to interact. Some authors argue that additive manufacturing, flexible automation and simulation are also part of Industry 4.0. (Larsson and Wollin, 2020). Moreover, existing research on the development of maturity models for I4.0 is extensive, but it has two limitations. First, these maturity models are hardly generalized because they are constructed for specific organizational contexts, specific technologies (e.g. artificial intelligence, Internet of Things) or the industrial sector (De Jesus and Lima, 2020; Onyeme and Liyanage, 2022). Secondly, they are unrelated to the theory of technology adoption (Santos and Martinho, 2020). Therefore, maturity models have been criticized for presenting oversimplified reality and often lack evidence of support (Colli et al., 2018).

According to Schmidt et al. (2015), many companies are adopting a set of new technologies such as Cyber-Physical Systems (CPS), Internet of Things (IoT), Robotics, Big Data (BD), Cloud Manufacturing (CM), and Augmented Reality (AR) to improve their products and processes and increase the efficiency and productivity of their production. As a result, in this research, we selected CPS, IoT, Big Data, Cloud Manufacturing, and AR as the core inputs.

The objectives of this paper are to contribute to the success of the introduction of Industry 4.0 into manufacturing processes and thus to technological progress, and identify issues associated with implementing Industry 4.0 into reality.

This paper is aimed at contributing to the successful adoption of Industry 4.0 in the manufacturing process and therefore to contributing to technological progress. The paper will be guided by the following research questions.

RQ1.

What are the difficulties in implementing Industry 4.0 in industrial processes?

RQ2.

How will the implementation of Industry 4.0 require changes to current manufacturing techniques?

RQ3.

Is it possible to estimate the level of Industry 4.0 adoption?

This study’s central motive is to find obstacles to the implementation of Industry 4.0, and the adaptation of manufacturing processes to implement Industry 4.0 successfully in Finnish, Polish, and Iranian Companies.

The structure of this paper is as follows: Section 2 discusses the literature review; Section 3 introduces Rogers' model, the Technological-Organizational-Environmental framework, and the Fuzzy Mamdani method; Section 4 presents the discussion and results related to Industry 4.0; and Section 5 offers conclusions and suggestions for future research.

2. Literature review

The term “industry 4.0” refers to the fourth industrial revolution, which is defined by the use of cutting-edge technologies in the manufacturing sector, including the Internet of Things (IoT), artificial intelligence (AI), cloud computing, and big data analytics. Real-time data capture, analysis, and sharing are made possible by these technologies throughout the whole supply chain, from raw material suppliers to the end of the supply chain. Production processes could significantly enhance productivity as a result of Industry 4.0’s transformation of analog manufacturing systems. Although there isn’t a widely accepted definition of Industry 4.0 yet, it concentrates on creating a smart factory where all systems and equipment are connected and in constant communication.

Industry 4.0 merges information and communications technologies with industrial technology, according to Zhou Liu and Zhou (2015). Therefore, it can be said that for modern manufacturing enterprises to keep their competitive advantage, the idea of Industry 4.0 must be successfully implemented.

Concerns about security and privacy seem to be widely acknowledged as a significant barrier to IoT adoption (Agrawal and Lal Das, 2011; Hassini and Bahroun, 2017; Haddud et al., 2017; Lee and Lee, 2015).

According to studies, there are difficulties in implementing Industry 4.0. The implementation of a Smart Factory faces three main challenges: Firstly, the large number and type of IoT devices, secondly, the wide range of data exchange, and thirdly, the establishment and maintenance of reliable cloud platforms (Lee et al., 2017).

The identified issue of IoT system incompatibility can undermine decision-making effectiveness. Another report suggests that the primary external obstacle to Industry 4.0 adoption is the scarcity of skilled personnel, while internally, the main challenge is the high cost of adoption (Tortorella and Fettermann, 2017).

Numerous studies have highlighted obstacles to adopting Industry 4.0. However, no prior research directly addressing the difficulties of incorporating Industry 4.0 in the production process was identified. Manufacturing is particularly significant to Industry 4.0 since it has the potential to improve manufacturing process efficiency, safety, and environmental sustainability (Strozzi et al., 2017).

Manufacturers must undertake considerable organizational changes to adopt Industry 4.0 technologies. Our assessment of the literature, however, showed that neither a complete investigation nor an accurate identification of the difficulties connected with integrating this technology into industrial processes had been done. Given that the fourth industrial revolution is already in progress, businesses must get ready in this way to keep a competitive edge in the marketplace. (Pereira and Romero, 2017). It is essential to understand the difficulties involved in implementing Industry 4.0 technology to adequately prepare for these developments.

Pereira and Romero (2017) Examining the effects of Industry 4.0, considerable changes to design, processes, operations, and services across the entire value chain have been observed. The adoption of Industry 4.0 will also have a big impact on management methods and the type of jobs that will be available in the future. New business model development will also be facilitated.

Studies haven’t focused on the changes that new manufacturing technology causes to manufacturing processes enough. The authors recommend a closer examination of the difficulties encountered during the implementation of smart factories (Strozzi et al., 2017).

According to the study, research has not paid enough attention to the organizational aspects of adopting smart factories. Though conceptually studied, the managerial issues and evolving requirements have not been exhaustively investigated in real-world contexts (Kannengiesser and Muller, 2018).

As a result, it is not yet clear how fundamental processes should be adjusted. So, due to a lack of research in this field, organizations are not prepared to implement the concept of Industry 4.0. Therefore, it is necessary to investigate how manufacturing processes need to adapt to adopt Industry 4.0. As Pereira and Romero noted in 2017: “As a company, it is important to recognize the main implications of Industry 4.0 adoption.” In addition, the authors point out that, unlike the previous industrial revolutions, the fourth industrial revolution is expected. This enables companies to prepare for this transformation.

Advanced digital services enable suppliers to establish and maintain complex process-oriented and outcomes-oriented business relationships with key customers (Paiola and Gebauer, 2020).

Industry 4.0 entails adopting a radical new approach to business, rather than merely implementing incremental changes to improve business performance (Sreedharan et al., 2019).

Industry 4.0 will impact organizations' relationships with the environment, communities, value chains, and humans. Future research should focus on designing organizational strategies that account for these changing relationships. (Sony and Naik, 2020).

Industry 4.0 necessitates additional personnel skills and capabilities. This includes ICT knowledge, multidisciplinary abilities, and unique personality traits due to its digital nature. Veile et al. (2020)

The term Industry 4.0 is an “umbrella term for a new industrial paradigm” and it consists of Cyber-Physical System (CPS), Internet of Things (IoT), Internet of Services (IoS), Robotics, Big Data, Cloud Manufacturing, and Augmented Reality (Pereira and Romero 2017).

With significant advancements in modern technology and computing, CPS has become a key development in Industry 4.0. CPS can be utilized as a factor to coordinate and carry out activities and operations as well as a factor to combine the physical world with the virtual (Monostori et al., 2016). In industrial production, cyber-physical systems are highly relevant. Improving production methods through CPS can positively impact the ecological balance (Singh, 2021). The most significant development of Industry 4.0 in terms of computer science and information technology might be regarded as CPS. CPS can be used to coordinate operations, control processes, and integrate physical and virtual environments (Monostori et al., 2016). According to Pereira and Romero (2017), CPS is a term that refers to “innovative technologies that enable the management of interconnected systems through the integration of their physical and computational environments.” Additionally, these systems can provide and make use of data obtaining and processing (Monostori et al., 2016). Due to the market’s complexity and rapid change, supply chain companies face numerous difficulties in today’s global economy. To keep up with the rapid change in customer demands, sustainable supply chains become essential. Based on the reviews, it is clear that manufacturing organizations need to accelerate the process of turning their attention to sustainability and leverage technology like “The Internet of Things (IoT)” to achieve their objectives. The fourth industrial revolution, commonly known as Industry 4.0, includes the Internet of Things (IoT), which is a crucial component. Performance can be enhanced by using it to monitor production systems in both manufacturing and services. By making it simpler to track and manage various processes, this technology opens up fresh and creative manufacturing options (Khan and Javaid. 2022).

Big data involves gathering data from several sources, including sensors. Businesses can improve their decision-making processes and production procedures by studying this data (Escobar et al., 2021).

The definition of cloud manufacturing is an intelligent network and production strategy that incorporates cloud computing to handle the growing demands for increased product personalization, global collaboration, innovations that require a high level of knowledge, and market reaction flexibility. Customers can obtain the services they need to sustain a product throughout its entire existence by having access to a shared resource where data are virtualized and managed flexibly and efficiently in cloud manufacturing (Ren et al., 2017).

Augmented reality (AR), a creative and practical approach to assisting in the improvement of industrial processes, models’ issues before these methods are put into place. Applications for AR are implemented using hardware and software, such as head-mounted precision trackers and monitors (Nee et al., 2012). Because AR may be used at any point of the production cycle, AR tools are crucial in all aspects of production (Olsson and Xu, 2018).

Applications of augmented reality (AR) have the potential to address several issues in the industrial setting, including the insufficient training of workers, the lack of information available to them, the disconnect between actual use and intended solutions, and the lack of effective actor-to-actor communication (Reljić et al., 2021)

A smart factory combines cutting-edge technology to effectively satisfy shifting market demands while keeping prices manageable, not one that runs entirely without workers. The development of “smart factories” is moving toward encouraging human-machine collaboration, combining human adaptability with machine reproducibility (Shi et al., 2020). Stentoft et al. (2021) recommend that businesses concentrate on encouraging the adoption of Industry 4.0 rather than just removing obstacles. These three factors – technological maturity and readiness, strategic intent and vision, and organizational culture and change preparedness – are the three main forces at work. Companies may promote the adoption of I4.0 technologies and take advantage of their advantages by addressing each of these factors. Table 1 provides a summary of the challenges that have been faced from 2012 to 2022.

Adopting Industry 4.0 technologies becomes a necessity, not just a strategic choice for organizational survival and growth. However, the successful implementation of Industry 4.0 involves overcoming significant challenges and necessitates substantial organizational change.

In this study proposed maturity model for Industry 4.0 Adoption.A maturity model has been developed to categorize case companies, reflecting how Industry 4.0 adoption levels likely influence the challenges they face. This model is part of the theoretical contribution of the paper and can be used for analysis to consider the challenges faced by companies in various phases of maturity. The four maturity levels are beginners, intermediates, experienced and experts. In addition, we use the Technology-Organization-Environment (TOE) framework (Tornatzky et al., 1990) to identify and characterize the barriers to the adoption of industry 4.0.

3. Methodology

3.1 Rogers model

The innovation diffusion model that Rogers (2003) established in 1962 has been slightly expanded and enhanced over the past 40 years due to new developments in theory and research. Rogers’s theory aims to explain how, why, and for how long new technologies, products, and/or ideas spread. In the theory of the diffusion of innovation, Rogers identifies four elements influencing the diffusion of new technologies: innovation, communication channels, time, and the social systems within which new technologies operate. Five groups of adopters are outlined: early adopters, early majority, late majority, and laggards. Figure 1 illustrates how adopters can be divided into groups based on their innovativeness and shows which level each adopter belongs to. Based on when people accept innovations, Figure 1 categorizes the innovativeness variable into five groups. Innovation is the introduction of a novel concept or item into an existing social system. Social systems refer to the settings in which innovation spread occurs, and they can be considered as a collection of interconnected parts that cooperate to achieve a shared objective. Individuals can play various roles within these social systems, and opinion leaders inform and advise other participants in the social system about innovations. (Rogers, 2003) The theory is an effective and essential tool for understanding the process of adopting various innovations and ideas due to its broad scope. However, Rogers' model overlooks the time dimension.

3.2 Technological-organizational-environmental model

Tornatzky et al. (1990) suggest the TOE (technological-organizational-environmental) paradigm to understand the acceptance of technological advancements. This model takes three aspects of adopting technology into account: the history of the organization, the history of the technology, and environmental influences. Figure 2 illustrates how these three factors affect the outcome of choosing to adopt innovative ways and strategies (Baker, 2012).

3.3 Fuzzy model

Fuzzy logic is frequently utilized to modify information in modeling (Jang et al., 1997; Zadeh, 1965) and to address a variety of real-world problems. Some of the benefits of fuzzy logic include translating user language and/or experience into a numerical value and producing acceptable outcomes based on ambiguous or uncertain data. Instead of attempting to automatically describe a system, it focuses on addressing problems using if-then logic (Zadeh, 1965).

The aim of this Mamdani FIS in this research is to determine the proportion of businesses that have implemented Industry 4.0 after determining the maturity level for each company. There is significant uncertainty, necessitating a tool to model it. In this research, fuzzy inference is employed for this purpose.

Here, a Fuzzy Inference System assigns different qualifiers to the parameters and the Matlab Mamdani FIS Toolbox is the interface that is used to simulate the process. Detailed steps and illustrations of this process are provided in the Appendix.

Mamdani Fuzzy’s model to measure Industry 4.0 is shown in Figure 3.

This study aims to carefully conduct semi-structured interviews, taking into account potential risks to the validity and reliability of the information gathered. This involves making sure the interviewee comprehends the interview’s goal and picks settings that are comfortable for them. The researchers also intend to apply cultural reflexivity given their varied cultural origins to reduce misunderstandings and misinterpretations. They also want to corroborate their data and interpretations with the interview subjects to increase the accuracy of their findings.

4. Result

4.1 Research model

The proposed maturity model, the proposed adoption process, and the theoretical contributions are all incorporated into the theory, which provides the theoretical foundation for the study. Conversely, empiricism in this study refers to the data collected from three different case firms, encompassing four components, as part of a multiple case study. The collected empirical data are next scrutinized in light of the theoretical concepts to address the research questions. The research starts by evaluating the state of Industry 4.0 technology development inside each instance company.

To address the first research question, the study then examines the challenges faced in adopting Industry 4.0, both within each case and across all cases. Additionally, the study explores how conventional manufacturing processes have been adapted within each case and across cases to answer the second research question.

4.2 First case study

The Iranian IT company is a business that specializes in offering cutting-edge software and technological solutions for a variety of industry sectors. They have embraced Industry 4.0 technology to improve the effectiveness of their manufacturing processes.

This case company has decided to adopt Industry 4.0 to maintain and enhance the business’s competitive position in the industry, find new prospects, and achieve a competitive edge. improved quality, lower prices, fewer labor-intensive tasks, and more overall productivity are the goals. The business also designs and builds test solutions for its customers that can communicate with their ERP systems.

4.2.1 Analysis case study

4.2.1.1 Model of maturity, innovation, and adoption process

The company Solutions realizes that Podiot’s native platform is a perfect fit for their requirements. The platform’s ability to provide the necessary infrastructure for all known IoT services, aligning precisely with the diverse needs of their clients.

The architectural structure and technologies used in Padiot allow the implementation and use of this product in different industries and dimensions, Padiot has the necessary flexibility to meet the needs and demands of the customer. This platform has all the necessary features, including managing, collecting, and analyzing data from various devices and sensors, regardless of the type of application and user space, on a large scale and securely. On the Padiot platform, a digital twin is created for each connected device, bridging the physical and virtual worlds. This twin is a digital replica of the device that connects the physical world and the virtual world like a bridge and maintains the status of the device throughout its lifetime.

As a result, the IoT can be classified as level 4 (innovators) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

Cloud Manufacturing Currently, the company AI Group is working on five main areas including recommender, customer behavioral analysis, natural language processing, time series forecasting, and machine vision and service development. The developed services of IFA include recommender service, market portfolio analysis, natural language processing, intelligent search, time series analysis, network, and graph analysis. Ayfa is now ready to provide its services for customers in a variety of fields such as banks and financial institutions, tourism and hotels, insurance, exchange, health, the manufacturing industry, retail, research canter’s, academia, etc. In addition to the services developed and personalized for specific purposes in the aforementioned areas, some of the services are available to all customers in any field of business.

Cloud manufacturing was successfully integrated as an Industry 4.0 technology. As a result, Cloud manufacturing can be classified as level 4 (innovators) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

This case has not yet made use of augmented reality. Augmented Reality Glasses or Displays are not being used, it is clear that AR in this situation falls under Rogers' innovativeness classification of Level 1 (laggards) in the maturity model and in the decision stage of the adoption decision process.

CPS is not used in a company. As a result, the CPS can be classified as level 1 (Laggards) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

The company uses big data analytics to improve the way it develops software. The business acquires better insights into the preferences, behavior, and demands of its customers by analyzing massive databases. Moreover, to foresee future trends and client needs in the IT sector. As a result, the Big data can be classified as level 3 (early majority) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model. Overall, the case company’s maturity can be rated as level 4 (expert), which according to Rogers' innovativeness classification places it in the late majority of Industry 4.0 adoption. Table 2 provides a summary of the maturity and adoption in the First Case (see Table 3).

The results are shown in the following Table using the aforementioned parameters as Mamdani FIS’s inputs to calculate Industry4.0.

The table shows that using the aforementioned variables as inputs, the appropriate industry 4.0 was calculated to be 50%.

4.2.2 Problems with implementing industry 4.0

4.2.2.1 Technological context

• (1)

  Complexity

Complexity, according to Rogers (2003), is the degree of difficulty in comprehending and using innovation. According to this assessment, complexity is the most significant technological problem in First Case. Technical complexity can present considerable challenges in the IT sector. Data collection, storage, and transfer from lower-level control systems to MES and SCADA applications are the main duties of Fantoring application. If Fantoring has problems, data loss, and disruptions can impair decision-making and system performance as a whole. Through data collection and analysis, Fantoring also helps to promote manufacturing process transparency by facilitating effective monitoring and problem identification. Without this openness, possible inefficiencies could develop. For Fantoring to be successfully implemented and seamlessly integrated with industrial automation systems, complexity must be addressed.

• (2)

  Data Challenges

Data problems include volume, diversity, velocity, authenticity, volatility, quality, discovery, according to Zicari and Akerkar (2014). Data in Industry 4.0 comes in various formats, such as structured, unstructured, and semi-structured data. Integrating and analyzing data from diverse sources can be complex, and Fanap may need advanced data integration techniques and tools.

• (3)

  Information Security

Because IoT devices are resource-constrained and there are more attackers, Elkhodr et al. (2016) contend that security and privacy concerns are a significant roadblock to IoT adoption. Since IoT devices have global access and connections, Case 1. A-Production mentioned that anyone can access them. The storing of data in cloud drives, which is vulnerable to hacking, is another information security issue that Big Data faces similar to IoT.

4.2.2.2 Organizational context

• (1)

  Management support

Management support is crucial because Industry 4.0 cannot be implemented without the financial and human resources that management support can give. Additionally, obtaining management support might be seen as a hurdle in and of itself.

• (2)

  Skill

In this situation, employee skills are vital because adopting Industry 4.0 necessitates a variety of talents from workers. In addition, the case company has a very difficult time finding appropriate candidates because of both their location in the region and the size of their organization.

• (3)

  Cost

The biggest issue in this situation, according to the IT manager, is cost because most Industry 4.0 equipment, is rather pricey. Additional expenses include the price of the needed IT infrastructure and the cost of data storage.

4.2.2.3 Environmental context

• (1)

  Competitive pressure

Interviews indicate that the case company does not feel challenged by competitive pressure.

• (2)

  Network externalities

It is possible to see network externalities as a catalyst of Industry 4.0 rather than an obstacle. The competition also has an impact on their client base. The ranked challenges for the First case are shown in Table 4.

4.3 Second case study

The Finnish company specializes in various types of aerial lifts, including telescopic boom lifts, articulated boom lifts, scissor lifts, and trailer-mounted lifts. The business is renowned for creating reliable, safe, and high-quality aerial lifts that adhere to rules and regulations. To increase the efficiency and safety of workers using their equipment, they put a priority on innovative design, toughness, and user-friendly features.

The case company decided to improve overall agility and reaction speed in all functions. As well they are going to improve their productivity and speed up the relevant information exchange between different work phases of their production process. The company believes that implementing Industry 4.0 can lead to being more agile compared to our competitors.

4.3.1 Analysis case study

4.3.1.1 Model of maturity, innovation, and adoption process

The company uses robotics in welding, changing workpieces in machining and turning centers, and the company is also investing in painting robots. They also use an FMS welding system including several robots and conveyors and the whole system is controlled by computer-based algorithms. They use intelligent sensors and actuators in their production machinery to provide real-time data collecting and manufacturing process monitoring. This information can be used to optimize production parameters and promptly spot any possible problems or deviations. As a result, the CPS can be classified as level 4 (Innovators) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

The company does not use real IoT even if they collect data for example from machine tools in order to carry out preventive maintenance work. That data is still collected manually, with human oversight required for maintenance decisions. As a result, the IOT can be classified as level 1 (Laggards) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

The company utilizes big data; it collects data from their products which are on the field and used by their customers. Data consists of information from different sensors (for example pressure, length, angle, etc.) and location information.

Concerning the production, they collect continuously the welding parameters from all our laser and MAG welding machines. In the machining department, the company collects 24/7 status information from the machining and turning center. Status data shows whether the machine is running or not, whether are there any failures, etc. As a result, the big data can be classified as level 3 (Late Majority) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

The company does not use the Cloud Manufacturing concept. Instead of that, they have plenty of “traditional” subcontractor – main contractor relations. As a result, the Big data can be classified as level 1 (Laggards) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

So far, the company has used AR just for customer training and simulation purposes. They have considered using it also to help maintenance and repair of equipment. the AR can be classified as level 2 (Late majority) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model. Table 5 summarizes maturity and adoption in the Second Case (see Table 6).

The results are shown in the following Table using the aforementioned parameters as Mamdani FIS’s inputs to calculate Industry4.0.

The table shows that using the aforementioned variables as inputs, the appropriate industry 4.0 was calculated to be 50%.

4.3.2 Problems with implementing industry 4.0

4.3.2.1 Technological context

• (1)

  Data challenges

In this situation, the correctness of data is challenging for companies because to do their tasks, Robots may need to combine data from numerous sources or systems to perform their tasks, which can be challenging. It can be difficult to integrate many data sources, and errors or discrepancies in the data can affect how accurately the robot does its tasks.

• (2)

  Information security

Hackers could disclose information on the security of the company’s computer networks, including firewalls, intrusion detection, and prevention systems.

• (3)

  Standardization

In this situation, standardization is the key problem. It is challenging to achieve standardization because projects are the main focus of the company’s operations. As a result, the interviewees claimed that it is challenging to implement technology like robots, particularly given their demand for quality.

4.3.2.2 Organizational context

• (1)

  Costs

Costs are a major issue in this situation because most Industry 4.0 equipment, including robots, is rather pricey. Additional expenses include the price of the needed IT infrastructure and the cost of data storage.

• (2)

  Management Support

According to the interview, management support can do a lot to drive Industry 4.0 adoption. It can also help to create employee support; it may take time to convince employees since they perceive new technologies as a threat.

• (3)

  Skills

Employees need to be trained to deal with Industry 4.0, according to the interview. The training’s subject matter is determined by the jobs that each employee is assigned. However, digital training techniques like e-learning may be useful in this situation. The main hurdle that the company’s clients encounter is the inability to find operators for welding and bending. Automation and robots will help to resolve this problem.

4.3.2.3 Environmental context

• (1)

  Network externalities

Frambach and Schillewaert (2002) refer to the quantity of businesses that use innovation, including competitors, suppliers, customers, and other businesses (such as the government). Due to their desire to keep their production procedures hidden from their clients, the company saw network externalities as a challenge. Table 7 displays the rated challenges for the Second Case study.

4.4 Third case study

The third case study focuses on a Polish company that creates equipment for industrial printing. They concentrate on designing and producing specialized printing presses, printers, or other tools used in industrial printing applications.

The case company aims to enhance data analysis options. The company believes that implementing Industry 4.0 can lead to Increased productivity and efficiency, and cost reduction.

4.4.1 Analysis case study

4.4.1.1 Model of maturity, innovation, and adoption process

The company does not utilize CPS. IoT sensors were installed on the equipment in the factory to collect temperature, vibration, energy consumption, and operating data. The machines in this factory are continuously having data collected by the IoT sensors. They can monitor the functionality and state of each piece of equipment in real-time. The company has access to data on energy use collected by IoT sensors, and identifies energy consumption patterns and increases its machines' energy effectiveness in energy consumption and increase the energy effectiveness of its machines. They can spot energy waste reduction opportunities, modify operational settings, and implement energy-saving strategies, resulting in cost savings and environmental sustainability. As a result, the IOT can be classified as level 4 (Innovators) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model.

They did not use Big data and cloud manufacturing. Integrating augmented reality technology with IoT sensors for machine diagnosis service. Their goal is to improve physical defect knowledge and enable more effective and precise remote troubleshooting by fusing IoT data with augmented reality. AR can be classified as level 3 (Late Majority) in terms of the adoption choice process' decision step, Rogers' classification of innovativeness, and the maturity model. Table 8 provides a summary of maturity and adoption in case third.

The results are shown in the following Table using the aforementioned parameters as Mamdani FIS’s inputs to calculate Industry4.0.

Table 9 shows that using the aforementioned variables as inputs, the appropriate Industry 4.0 was calculated to be 14.9%.

4.4.2 Problems with implementing industry 4.0

4.4.2.1 Technological context

• (1)

  Data Management

Data volume and data diversity are the company’s two key data problems.

4.4.2.2 Organizational context

• (1)

  Management support

Costs and divisional support are two critical aspects of management support. When a business wants to embrace innovation, it needs the cooperation and integration of the entire organization, as well as support from all departments. This is particularly challenging for new technological developments like Industry 4.0.

• (2)

  Cost

Costs are the biggest issue here because devices like robotics, AI, and AR are relatively pricey. This issue is significantly more important for SMEs than it is for larger.

• (3)

  Skill

According to the interview, the lack of Industry 4.0 understanding among employees makes the skills challenge particularly significant.

4.4.2.3 Environmental context

• (1)

  Network externalities

Due to their desire to keep their production methods hidden from their clients, A Production identified network externalities as a challenge. Table 10 displays the rated challenges for the Third Case study.

5. Discussion

The current work on the maturity model presents several shortcomings. Firstly, there are no empirical studies on the development of mature models, and the focus is not only on predictive models, but also on descriptive models (Elibal and Ozeylan, 2020; Rafael et al., 2020). Second, existing research is a disconnect from the theory of technology adoption (Santos and Martinho, 2020), which limits its generalization and explanation power. Consequently, research has highlighted the absence of models aimed at several industrial sectors (Çınar et al., 2021; Gökalp et al., 2021; Santos and Martinho, 2020; Zoubek et al., 2021). To fill this gap in our study, experimental research to collect data on the development and implementation of maturity models. This model is used to analyze the maturity of individual case-based companies as a basis for further analysis. The model was then used to analyze the maturity of the case companies participating in the interview in relation to the empirical data collected during the interview. In addition, the maturity model allowed the researchers in this paper to translate qualitative data obtained during interviews into quantitative maturity data. This allows you to easily compare the maturity levels between different case companies. As a result, the maturity model can be considered to be a comprehensive and transparent instrument for assessing the maturity levels of the respective companies adopting Industry 4.0.

The results of this study show that the barriers related to data and management in Tables 3, 6 and 9 have highest ranked. Similar studies have concluded that the lack of data and knowledge management systems is the most important barrier to the adoption of I4.0 technologies which is corroborated by our findings (Barros et al., 2017; Kamble et al., 2018; Karadayi-Usta, 2019; Stentoft and Rajkumar, 2020).

5.1 Conclusion

The study aims to identify and analyze the potential challenges of the adoption of Industry 4.0 and the adjustments to conventional manufacturing processes. The findings shall be useful, enabling managers to better understand the adoption obstacles for Industry 4.0 and take appropriate action to ensure smooth adoption. The researchers proposed a maturity model for introducing Industry 4.0 into manufacturing processes as part of their theoretical contribution. The model was used to analyze the maturity of the participating case companies. This model contains key industrial 4.0 technologies derived from the literature review carried out for this study. These key technologies were then operationalized in the reference framework to clarify how to measure the adoption of these technologies.

Regarding the first research question, this study has demonstrated that companies must overcome organizational, technological, and environmental obstacles when implementing Industry 4.0. The majority of the case companies (2 out of 3 companies) faced difficulties such as standardization, management support, skills, and costs. All companies faced data challenges and network externalities. Two companies face information security and standardization. Therefore, it can be said the most critical challenges are standardization, management support, skills, and costs.

Regarding second question, it was demonstrated that Industry 4.0 will impact production infrastructure, including IT systems, equipment, and layouts. Additionally, this study’s findings indicate that the majority of those interviewees do not think that Industry 4.0 will significantly change current production practices.

The challenges of adopting Industry 4.0 include the need to invest in new technologies, the need to train employees, and the need to change business processes. The requirements for adopting Industry 4.0 include the need for a clear vision, the need for strong leadership, and the need for a culture of innovation. The implementation of Industry 4.0 involves utilizing Fuzzy inference to model uncertainty. This approach effectively deals with uncertain and ambiguous data by leveraging the expertise and knowledge of users within the system.

5.2 Theoretical contribution and managerial implications

This paper contains theoretical contributions in theoretical frameworks and analysis. First, the researchers conducted a literature review to summarize the challenges identified by previous research for the adoption of Industry 4.0. In addition, researchers propose their own processes for adopting technological innovation, based on models proposed by other researchers. Furthermore, the researchers propose a maturity model for industry 4.0, which also represents a theoretical contribution.

Regarding the first research question, the challenges resulting from the review of literature were then operationalized by asking interviewees what challenges they considered to be the most important for their organizations. The analysis contributes to theory by providing a comprehensive view of the most important challenges in the adoption of Industry 4.0 based on empirical data.

With respect to the second research question, the researchers have been able to build three themes that reflect the changes required by industry 4.0 from conventional manufacturing processes. These clusters contribute to theory by identifying the requisites and changes in manufacturing processes induced by Industry 4.0. However, since there are no studies in this area, as discussed earlier in this paper, it is difficult to use theory in the analysis of the second research problem.

With respect to the managerial implications of this paper, it can be said that the results help managers understand the challenges of adopting Industry 4.0. Recognizing these challenges allows managers to react to them and facilitate the smooth adoption of Industry 4.0. The results of the second research question provide an overview of the requirements for industrial 4.0 adoption and its implications. The findings of this paper can therefore be summarized as supporting managers in the decision-making process of the innovation adoption process. This paper contains valuable theoretical contributions and management implications.

5.3 Limitations and suggestions for future research

A high number of cases and interviews can reduce the influence of potential participants' mistakes and prejudices. However, interviewees were in other countries and had communicated with them via email so it was difficult for some parts to understand the terms used in Industry 4.0 challenges.

Further research also may be conducted below.

1. To demonstrate the value that Industry 4.0 creates from an economic, environmental, and social standpoint, it may be useful to develop a business case for sustainability.

2. The FIS models can be utilized with various language qualifiers and phrases. The use of defuzzification techniques and various but appropriate membership functions is also an option.

3. Another important point to consider is how to determine weights for each attribute. This can be done through the methods in the context of multi-criteria decision-making (MCDM).

Figures

Figure 1

Adopter categorization based on innovativeness

Figure 2

The technology–organization–environment framework

Figure 3

Industry 4.0 using Mamdani FIS

Table 1

Challenges of technologies in Industry 4.0

Authors	Year	Title	Challenges
Oluyisola et al.	2022	Designing and developing smart production planning and control systems in the industry 4.0 era: a methodology and case study	Specific companies use various methods of production planning and system improvement to improve processes, including Just-in-Time (JIT) and the use of integrated information systems, including ERP and lean Six Sigma, but there are few operational challenges, including lack of knowledge and strategic planning for the deployment of these systems at SMEs' level
Ejsmont et al.	2020	Towards “Lean Industry 4.0”–Current trends and future perspectives	Despite the competitive advantages of the I4.0 technology, challenges such as limited support from senior management, the lack of awareness among stakeholders, the lack of knowledge of lean and IE4.0 concepts, the cost of acquiring new technologies, and the relatively high integration of lean and I4.0 make manufacturing unsuitable for investment
Liu et al.	2017	Cyber-Physical Systems (CPS)	Lack of physical resources as well as a comprehensive theoretical framework for networks. errors in data collection, measurement noise, environmental interference, and failure in the integrated framework calculation process. issues with matching, robustness, efficiency, and the capacity for growth. machine robustness, a real-time system’s abstraction, a service modality for its component parts, and science engineering. organizing complex dynamic interactions between physical systems and network systems for researcher CPS. the requirement for additional investigation and study on CPS to become more useful
Hussain	2016	The Internet of Things: A review of enabling technologies, challenges, and open research issues	Quality of service (QoS), interoperability, scalability, heterogeneity, and privacy
Han et al.	2016	Security Challenges for the Public Utilization of Cloud Computing in Internet of Things (IoT)	Privacy and security
Kaur and Mir	2015	Quality of Service Issues and Challenges in Internet of Things	Scalability, effectiveness, safety, and interoperability. Accessibility and credibility. difficulties with information, processes, and management
Nasser and Tariq	2015	Big data analytics: A literature review paper	capacity, diversity, speed, precision, volatility, modality, detection in data
Sivarajah et al.	2017	Critical analysis of Big Data challenges and analytical methods	challenges with information, processes, and management, as well as with security, privacy, the legal system, and ethics
Sadiku et al.	2014	“Challenges and Opportunities of Cloud Computing”	Security and privacy, data delays, lack of consistency, entrusting personal information to a third party
Adamson et al.	2017	Cloud manufacturing: A review	Lack of tracking service despite sophisticated product design high cost of subcontracting issues with matching resources, sharing resources, and a lack of resources
Nee et al.	2012	Augmented Reality (AR)	Precision, registration, latency issues, and interface technology
Wang et al.	2016	Challenges and Opportunities of Smart Factory	The ability to make wise decisions and negotiate. the generation and analysis of unique big data. protection of digital assets

Source(s): Own illustration

Table 2

Summary of maturity and adoption

Technology	Maturity level	Description	Adoption stage
CPS (Cyber-Physical Systems)	Level 1	Are not in use	Problem Definition Stage/Laggards
IoT (Internet of Things)	Level 4	Implemented Podiot’s to meet the needs and demands of the customer	Routinizing is a stage of realization (at the individual level)./Innovators
Big Data	Level 3	to improve and develop software	Understanding: Step of implementation/Early Majority
CM (Cloud Manufacturing)	Level 4	Implement natural language processing, intelligent search, time series analysis, network, and graph analysis	Routinizing is a stage of realization (at the individual level)./Innovators
AR (Augmented Reality)	Level 1	Are not use	Problem Definition Stage/Laggards
Smart Factory	Level 3	Level 3 Some use of CPS, IoT, Big Data, CM, AR, and AI	Adoption decision: decision step (Organizational level)/Late Majority
Industry 4.0	Level 3	Level 3: some parts of all Industry 4.0 technologies are in use	Adoption decision: decision step (Organizational level)/Late Majority

Source(s): Own illustration

Table 3

Industry 4.0 measurement, Mamdani intelligent system results

Input parameters	Linguistic term	Input value	Output parameters	Output linguistic term	Defuzzied Output Value
CPS	Low	10	Industry 4.0	Medium	50
IOT	High	100
Big data	Average	50
CM	High	90
AR	Low	10

Source(s): Own illustration

Table 4

Ranked challenges for first case

TOE/Ranking	1	2	3
Technological	Complexity	Data challenges	Information Security
Organizational	Management support	Skill	Cost
Environmental	Network externalities	–	–

Source(s): Own illustration

Table 5

Summary of maturity and adoption

Technology	Maturity level	Description	Adoption stage
CPS (Cyber-Physical Systems)	Level 4	Automation enables comprehensive control over systems an	Routinizing is a stage of realization (at the individual level)./Innovators
IoT (Internet of Things)	Level 1	Manual data collecting	Problem Definition Stage/Laggards
Big Data	Level 3	comprehensive collecting and analysis of digital data	Understanding: Step of implementation/Early Majority
CM (Cloud Manufacturing)	Level 1	Cloud solutions are not present	Problem Definition Stage/Laggards
AR (Augmented Reality)	Level 2	Augmented Reality Glasses or Displays used for the customer training and simulation purposes	Problem Definition Stage/Late Majority
Smart Factory	Level 3	Some parts of CPS, Big data, AR, and AI are in use	Adoption decision: decision step (Organizational level)/Late Majority
Industry 4.0	Level 3	Level 3: some parts of all Industry 4.0 technologies are in use	Adoption Decision Stage: organizational level/Late Majority

Source(s): Own illustration

Table 6

Industry 4.0 measurement, Mamdani intelligent system results

Input parameters	Linguistic term	Input value	Output parameters	Output linguistic term	Deffuzified Output Value
CPS	High	85	Industry 4.0	Medium	50
IOT	Low	12
Big data	Medium	55
CM	Low	15
AR	Low	15

Source(s): Own illustration

Table 7

Ranked challenges for second case

TOE/Ranking	1	2	3
Technological	issues with data	standardized challenge	Information security
Organizational	Management support	Costs	Skills
Environmental	Network Externalities	–	–

Source(s): Own illustration

Table 8

Summary of maturity and adoption

Technology	Maturity level	Description	Adoption stage
CPS (Cyber-Physical Systems)	Level 1	Are not in use	Problem Definition Stage/Laggards
IoT (Internet of Things)	Level 4	Systems and machines are integrated	Routinizing is a stage of realization (at the individual level)./Innovators
Big Data	Level 1	When necessary, such as during sampling for quality control, data is manually collected	Problem Definition Stage/Laggards
CM (Cloud Manufacturing)	Level 1	Are not in use	Problem Definition Stage/Laggards
AR (Augmented Reality)	Level 3	For the entire process, augmented reality is used	Understanding: Step of implementation/Early Majority
Smart Factory	Level 2	CPS, IoT, Big Data, CM, AR, and AI in some of its components are in use	Problem Definition Stage/Late Majority
Industry 4.0	Level 2	some areas of all industries In use are 4.0 technologies	Adoption decision: decision step (Organizational level)/Late Majority

Source(s): Own illustration

Table 9

Industry 4.0 measurement, Mamdani intelligent system results

Input parameters	Linguistic term	Input value	Output parameters	Output linguistic term	Deffuzified Output Value
CPS	Low	10	Industry 4.0	Low	14.9
IOT	High	80
Big data	Low	15
CM	Low	10
AR	Medium	50

Source(s): Own illustration

Table 10

Ranked challenges for fourth case

TOE/Ranking	1	2	3
Technological	Data Management	–	–
Organizational	Management support	Cost	Skill
Environmental	Network externalities	–	–

Source(s): Own illustration

Appendix Fuzzy model

The Mamdani Fuzzy Inference Model is one of the fuzzy models chosen for creating the proposed IS approaches. It is a broad platform that enables examining and defining data from the user’s perspective to correctly identify the processes with immeasurable parameters. Additionally, it makes hazy judgments and assumes the true user’s control situation. In general, this kind of approach makes ambiguous verbal phrases fully functional by incorporating them into the system.

The Five Industry 4.0 inputs have a gauss2mf membership function and their range is from (0–100). The Industry 4.0 output also has a gauss2mf membership function and its range is from (0–1).