Analysis and evaluation of Indian industrial system requirements and barriers affect during implementation of Industry 4.0 technologies

In recent years, competition among the Indian manufacturing industries (IMI) has increased enormously in the global market. The current uncertainty in the market context is characterised and governed by the customised requirements of the customers. Thus, the manufacturing system in the industries should be capable of adapting the parameters like flexibility in scalability, variety, agility, system responsiveness, inter-connectivity, automatic data exchange with communication among the manufacturing systems, transparency and human–machine interaction, which are the main components and principles of Industry 4.0 (I4.0). Thus, adopting I4.0 plays a vital role to corroborate its long-term survival in the global marketplace. However, very few research work considerations contribute towards the issues induced during the adoption of I4.0 in manufacturing industries. This paper aims to minimise the gap between the existing Industrial System Requirements (ISRs) and the challenges faced during implementing I4.0 technologies in existing Industries. The identified ISRs and barriers were evaluated and analysed based on the data set collected from a questionnaire-based survey. Fuzzy multi-criteria analysis is conducted to identify the most weighted ISRs and barriers and ranked them concerning their importance. Furthermore, the inter-item correlation between both of them is analysed. This research work offers the researchers, practitioners, and industrialists an opportunity to formulate multi-criteria decision making (MCDM) problems through numerous case studies and prioritise the top barriers, system requirements and the inter-relationship shared between them.


Introduction
Today, the survival of a manufacturing industry depends on its flexibility, agility, quick responsiveness to fluctuating market demand, scalability, variability, mass customisation as per the preference of the customer, excellent quality and post-sale service [1][2][3][4]. These factors are driven by data sharing and monitoring, data transparency and connectivity among the manufacturing systems without any barriers [5]. 'Industrie 4.0' (I4.0) or the Fourth Industrial Revolution is the integration and interconnection of machines and robots, sensors and actuators, basic physical and software systems, interoperability, logistics and storage systems, realtime data transfer and data transparency, customers and people, man to machine interaction, market and other economic sectors, standardisation of production process, information and communication technologies, cyber-physical systems, Internet of things, big data and cloud computing which are interconnected among themselves and communicate with one other in their social communication platform with the help of wired or wireless networks [6][7][8][9][10][11][12]. It integrates the feedback loop from the machines, systems, sensors, cloud storage, inventory stock, customers, workers, employees, logistics, management, market demand with the support of Internet connectivity to monitor the production, sales and services continuously as well as storing the data for continuous improvement in productivity [13].
In the emerging global economy, for the long-term survival of the manufacturing industries to remain intact in the global market competition, there is a need for serious upgradation of the existing manufacturing system to the smart factory, which is termed the fourth industrial revolution. As of now, the I4.0 technologies are still in the research and development phase, mostly confined to the implementation phase, because of some unavoidable driving barriers faced during its adoption in the current manufacturing industries [8,9,[14][15][16].
Few papers point out the barriers that affect the implementation of I4.0 technologies in existing industries. However, the literature is silent on addressing the gap between the requirements of existing Indian Industries in the context of I4.0 and the difficulties encountered while implementing I4.0 technologies. As a result, research interest grew in the I4.0 implementation strategy. This paper explores and identifies various current Industrial System Requirements (ISRs) and the barriers to implement I4.0 in existing manufacturing systems from the literature review and interaction with numerous industry experts. This study examines to answer the following research questions (RQs): 1. What are the major requirements of the existing industrial system and issues faced by the industrial management team and the experts while implementing I4.0 technologies in Indian manufacturing industries? 2. How can the identified ISRs and the barriers to implementing I4.0 technologies be prioritised as per their importance? 3. How can the identified ISRs and the barriers be related to each other in implementing I4.0 technologies? First, the existing ISRs in Indian manufacturing industries (IMI) were identified through an exhaustive literature review and finalised 13 requirements (such as reducing cost, increasing flexibility in scalability and variability, mass customisation etc., as elaborated in Sect. 5.1) by interviewing and taking opinions of the industrial experts.
Similarly, in the next phase, the implementation issues or the implementing barriers faced by IMI (such as inadequate digital culture, implementation cost, etc.) were identified through the literature review. With a detailed discussion, many industry experts and academicians currently working in smart factory technologies, 18 barriers that affect I4.0 implementation were finalised from their opinions.
Then for quantifying and evaluating the identified ISRs and barriers, a questionnaire survey was conducted where the expert's opinions and ratings were collected, especially those working on I4.0 and its core technologies in industries, research organisations and academicians. Then, the collected linguistic data were analysed and classified into barriers and adaptation factors. This paper analyses the collected data set to identify the most weighted ISR factors and barrier that affect the implementation of I4.0 through evaluation. It is obtained from the fuzzy multi-criteria analysis. Next, correlation analysis between these ISRs and the barriers of I4 was analysed by IBM SPSS software version 25. The structural layout of this paper is organised as follows. Theoretical background about I4.0, some of its core components and the existing Indian manufacturing scenario in the context of I4.0 are presented in Sects. 2 and 3. The literature survey related to this work is given in Sect. 4. The research methodology used in the present work is presented in Sect. 5. Data analysis, evaluations and results are presented in Sect. 6. Discussions and implications of the findings are presented in Sect. 7. The conclusion and direction of future work are presented in Sect. 8.

Theoretical background
The first industrial revolution began at the end of the eighteenth century, represented by mechanical production plants based on water and steam power, in which the products were made in a family workshop by craftsmen and their apprentices; the second industrial revolution started at the beginning of the twentieth century which brought a paradigm of mass labour production based on electrical energy to produce a large volume of standardised products with division of labour and specialised skills at a low unit cost; the third industrial revolution began in the 1970s with the characteristics of automation based on electronics, computers applications and information technology, where the manufacturing evolved from mass production to mass customisation, with the help of programmable machines to produce standardised products with some degree of flexibility at the production line and sub-assembly or final assembly [13,[17][18][19][20][21][22][23][24][25][26]; and the fourth industrial revolution which is Industry 4.0 has emerged recently over the last few years [19,27,28]. It has emerged as a promising technology framework that can be used for integrating and extending manufacturing processes at both intra-organisational and inter-organisational levels where machine to machine communication, real-time data transparency, human to machine interaction with the help of information and communication technology (ICT), Internet of things (IoT), smart factory, cyber-physical systems (CPS) and cloud computing is integrated for manufacturing. The core concept of Industry 4.0 is the integration of the basic physical system and the software system, integration with other branches and economic sectors, integration with other industries and industry types [10,11]. Industry 4.0, otherwise known as smart manufacturing and cognitive manufacturing, offers new manufacturing firms opportunities to analyse and use design, production, sourcing, and inventory data to realise their modernisation vision [29].
In I4.0, the entire industrial production system is transformed and digitalised by merging the traditional production process with the internet and information and communication technologies (ICT) infrastructures [30]. In I4.0, the Industrial IoT (IIoT) is the primary tool that applies analytical models and data science to analyse real-time data sharing, real-time monitoring, also uses smart devices to sense, capture, measure and transfer data from multiple machines, manufacturing processes and its systems, and finally, reconfigure and automates the manufacturing systems as per the requirement of the customer [28]. It combines the data from intelligent sensors with artificial intelligence and performs data analytics, optimising the manufacturing process in real-time [27]. It consists of Industrial Wireless Networks (IWNs), internet, machines, equipment, networks, cloud storage and computing. In this system, the customers can customise and personalise their products to be manufactured from its end. The customised data are transmitted to the industrial cloud and manufacturing plant via wired or wireless networks. Based on these received data, the manufacturer integrates the design for the product and then optimises production and simulates it. It also manages and monitors the production process and diagnosis to produce the required products efficiently. With self-optimisation analysis features using the real-time data, and self-directed decision-making capability, machines, cloud computing, and equipment adapt and reconfigure to automatically analyse for performance prediction in real-time for improving performance as well as predicting the failure and maintenance in machines system and tools [31][32][33].
In I4.0, cyber-physical system (CPS) is an entirely datadriven application that inter-connects the virtual space with physical components systems through integrated computing, complex algorithms, sensors data, embedded systems, communication, data analytics and storage capabilities with the help of complex networks and Internet [34,6]. It collaborates planning, analysis, modelling, design and implementation, and maintenance in the manufacturing process more securely with information exchange among embedded computer systems. CPS maximises efficiency and industrial growth, modifies workforce performance, minimises errors, and produces higher-quality products with lower costs. Interoperability is the critical factor of Industry 4.0, enabling the two systems or machines to communicate, cooperate and understand each other, and make decisions by exchanging data and knowledge without human interference [27].

Indian manufacturing industries and Industry 4.0
At present, the concept of Industry 4.0 is still in the developing stage in India. It needs proper empirical research and analysis, awareness, and practice. The statistical data have been surveyed and reviewed through various sources from government websites, world bank, government financial reports, yearly manufacturing reports, Indian brand equity foundation reports and various news articles to analyse India's present manufacturing scenario, its performance, capacity utilisation, exports, growing demand and measures to meet the demand of the customer.
Recently, India's manufacturing sector has witnessed strong growth over the past few years, as the estimated gross value added is up to USD 390.43 billion and is projected to grow more up to USD 398 billion [35]. It is observed that the growth of the manufacturing sector of India is 77.63% in terms of the Index of Industrial Production (IIP), which is slightly higher than that of mining and electricity production [36]. This not only shows the production levels of the industries but also specifies the potential for future investments. As per the latest review, capacity utilisation in India's manufacturing sector is around 74.8% in the second quarter of 2018-2019. In the same period, the average new demands of customers also increased as the manufacturing units grew 26.1% compared to the previous years. India's merchandise export performance witnessed 9.52% growth to reach USD 271 billion. It also reflects India's emergence to compete with other countries worldwide market scenario [37].
Based on market context and uncertainty in global performance, motivate further research work in adapting I4.0 in the existing manufacturing industries associated with the fastest emerging economy. Today the leading countries have already geared up and started implementing advanced technologies and Internet and Information Technology on a large scale in their existing systems. India is currently focusing on implementing its new policies and improving its infrastructure [38].
At present, Industry 3.0 is not entirely implemented throughout the manufacturing sector of India as per the industrial reports from an audit and advisory firm KPMG in association with All India Management Association, and many sectors, especially the SMEs, are still in the postelectrification phase, that uses basic machinery systems for production and lacks core technology. India's existing manufacturing industry is still a resource consumption and labour-intensive type, and it resides at the industrial chain's low end. The integration of physical systems on cyber platforms, the basic premise of I4.0 in these industries, are still at its early stages [39]. However, many automotive and its parts manufacturing industries, consumer electrical and electronics manufacturing industries, health care and pharmaceutical industries, solution and technology providing industries, original equipment and machines manufacturing industries and some other product-based industries like cement, garment, food processing, solar, are currently on complete and partial automation. They have robotic assembly lines, automatic handling systems, conveyors, automated guided vehicles (AGVs), automatic storage and retrieval systems, advanced manufacturing equipment and tools. Nevertheless, it lacks the major benefits of smart technologies, data-driven features, and interconnectivity that can be fulfilled by implementing I4.0 technologies [40]. Recently, there has been a growing demand for adapting technologies such as the Internet of things (IoT), cloud computing, big data analysis, additive manufacturing in these industries. Digitalisation and full transparency of the logistics and supply chain, automated business process, cyber-physical network, horizontal and vertical integration, customer-centric, value chain, and mass customisation in Indian industries are still in the early stages due to their complex structural form and limited investment and policies [41]. At present, it requires extensive research, innovative solutions, vision, motivation and organisational strategies for its sustainability. Thus, the upgradation is necessary to compete with the aggressive competition and long-term survival in the global market.
The Government of India policies continuously pushes to adopt modern and smart technology and digital transformation by collaborating with more FDI for domestic production. The national manufacturing policy also predicts that the GDP share in manufacturing increases to 25% by 2021 [37]. However, there is an increment in R&D funding, not compared to match the developed countries (USA, Germany and Japan) [42]. Policies like Samarth Udyog Bharat 4.0, an Industry 4.0 initiative for IMI launched by the Ministry of Heavy Industry and Public Enterprises, Smart Cities projects enabling the Internet of Things Platform, and Digital India policies in 2018 were formed and governed research and development in existing manufacturing environments. The rate of adoption of I4.0 are expected to be highest in industries such as utilities, manufacturing, automotive and transportation and logistics are expected [43].

Literature review
'Industrie 4.0', also known as 'Industry 4.0' or the Fourth Industrial Revolution, was initially introduced during the Hannover Fair in 2011 and was officially declared in 2013. It is a German strategic initiative to take a ground-breaking role in industries that are currently revolutionising the manufacturing sector by integrating with modern technologies and connectivity, and quick adaptability of manufacturing systems to provide maximum output as per the demand with efficient utilisation of resources along with maintaining better product quality [8,9]. Since then, several types of research have been conducted in recent years on the enabling technologies of I4.0 such as IoT, artificial intelligence (AI), CPS, cyber security, universal standardisation, horizontal and vertical integration, ICT, cloud computing, man-machine interactions integration into systems. Nevertheless, major research works are yet to be done on industrial integration, data privacy, implementation of the strategy (organisational and managerial), empirical validation and real-time testing of the I4.0 technologies, real-time studies on ERP, smart devices, smart sensors, reconfiguration of tools and equipment, machine-machine communications, blockchain, CPS, data science and data analytics [44][45][46][47][48]. The recent challenges faced while adopting I4.0 technologies and related publications were reviewed from the last five years. The relevant literature and their major findings are shown in Table 1.
Zhou et al. [26] identified various challenges for implementing I4.0 paradigms, such as developing smart devices, the construction of the network environment, big data analysis and digital production. They proposed strategic planning, including constructing a CPS network and discussed two significant themes based on the smart factory and intelligent production. Lu [27] identified various research challenges Zhou et al. [26] Various challenges are discussed to implement the I4.0 paradigms. Strategic planning is proposed, such as the building of a CPS network and other smart manufacturing applications. 2 Lu [27] Identifies various research issues and the conceptual framework for interoperability in the implementation of I4.0. 3 Kamble et al. [8] Potential barriers to industry 4.0 adaptation are identified and discussed. Identifies and classifies the significant barriers, revealing its direct and indirect effects on the I4.0 adoption. 4 Luthra and Mangla [15] Various challenges to adopt I4.0 technologies in IMI were identified. These were evaluated and prioritised potential challenges for supply chain sustainability. 5 Moktadir et al.
[23] Several challenges for implementing I4.0 were identified and evaluated using the best-worst method to identify the most weighted challenges and ranked in decreasing order along with a case study. 6 Zhang et al. [49] Strategic decisions were suggested to adopt German 'I4.0' in Chinese manufacturing industries. 7 Tortorella and Fettermann [50] Examines the relationship between lean production practices and the implementation of Industry 4.0. 8 Kamble et al. [51] Investigates the direct and indirect effect of I4.0 technologies, lean manufacturing practice, sustainable organisational performance and their relationship in manufacturing industries. 9 Pasi et al. [40] Presents I4.0 enabling technologies and their impact on sustainability in Indian manufacturing industries. related to Industry 4.0 and proposed a conceptual framework on interoperability. Potential barriers to adapt Industry 4.0 were identified and analysed by [9], and the relationships among them were developed using interpretive structural modelling (ISM) and fuzzy MICMAC methodology in the Indian manufacturing context with the help of experts. This work helps to identify and classify the significant barriers, revealing each identified barrier's direct and indirect effects on the I4.0 adoption. Various challenges to adopt I4.0 technologies in IMI were identified by Luthra and Mangla [15]. These were evaluated and prioritised for supply chain sustainability through surveys and ratings from the experts, and analysed through explanatory factor analysis and analytical hierarchy process tools. It also helps the industrialists to incorporate environmental protection and directs to eliminate the potential challenges in adopting I4.0 technologies for the sustainability of the supply chain. Several challenges for implementing I4.0 were identified and evaluated by Moktadir et al.
[23] using the best-worst method. A multi-criteria decision-making method was implemented by them to identify the most weighted challenges, and those challenges were ranked in decreasing order with the help of a case study. This results in addressing the challenges for the industrialists and practitioners to build up specific strategies for implementing I4.0 technologies. Therefore, the objective of this paper is to emphasise the implementing barriers from another perspective, in the context of IMI requiring more empirical evaluation and analysis. Most of the findings are confined to identify the challenges faced during the implementation through surveys and validating them through various research tools. Strategic decisions were suggested by [49] to adopt German 'I4.0' in Chinese manufacturing industries, which provided a path for advanced manufacturing industrial development with clear goals and practical measures. Suggestions were provided to materialise the transformation and upgradation of the present manufacturing scenario to I4.0. The relationship between lean production practices and the implementation of Industry 4.0 was examined by Tortorella and Fettermann [50], in which relevant data were collected through a questionnaire survey from various industries. Clustering tools are used to find the relationship within the collected database, LP practices and I4.0. Kamble et al. [51] investigated the direct and indirect effects of I4.0 technologies, lean manufacturing practice, sustainable organisational performance, and their relationship based on survey data collected. This paper also provides empirical validation, which is a critical factor for the implication of I4.0 technologies in existing manufacturing industries.
The above literature points out the implementation strategy, inter-item relationship, challenges during implementation, enabling factors, effects of I4.0 technologies with different manufacturing practices on various industries of the world. As per their perspective, very little literature has been addressed regarding Indian manufacturers. The relevant literature and their major findings are shown in Table 1. This paper addresses the issues and findings of the existing ISRs required in existing IMI and the challenges these industries face while implementing in Indian industries. Implementing 4.0 in IMI is critical; however, the literature is silent on how each ISR is affected by barriers while implementing I4.0 technologies. More research needs to be addressed to connect the bridge and the relationship between the ISRs and the barriers affecting the implementation of I4.0 technologies.
In this paper, the ISRs of I4.0 and implementation barriers are identified and ranked as per the weights obtained from the calculation of the rating provided by the respondent. This is followed by the inter-relationship shared among them in the context of Industries in India. The uniqueness of this paper has two aspects. The first is applying a soft computing tool (i.e. fuzzy technique) to do the multi-criteria decision analysis (fuzzy multi-criteria decision analysis) to the collected vague response data, finding the weights, and ranking the ISRs and the implementing barriers as per the weights. The second is conducting an inter-item correlation analysis between each ISR and each barrier of I4.0 to obtain the relationship shared between them. The concept is novel and needs extensive understanding from the theoretical, practical and managerial aspects of the ethical implications of approaching I4.0 technologies through various strategical approaches in existing manufacturing industries. There is a need to adapt and determine the customer's fluctuating demands, inter-connectivity, big data, mass customisation, transparency, and agility in the production process.

Research methodology
This section identifies the factors, questionnaire development and collection of data through a survey. At first, the existing ISRs and Barriers to adapt Industry 4.0 technologies in IMI are identified and finalised. A survey-based approach is considered, where the industrial experts provide their valuable ratings and feedbacks as per the designed questionnaire. The ratings are collected as a linguistic database where further analysis and evaluation of the identified ISRs and Barriers are carried out in the following sub-sections.

Identification of existing ISRs
The crucial number of ISRs were identified and finalised after reviewing the literature [13,16,24,33,6,[52][53][54][55][56][57][58] and interviewing industrial experts. The experts were interviewed to finalise the 13 identified ISRs with some key modifications [8,9]. All of the experts considered are from the top industries of India. These experts are highly experienced (have an average work experience of 20 years) and have adequate knowledge in production and industrial management.
Moreover, few of them were from research and development of the solution providing companies, who work on providing various technical, infrastructure and IoT solutions to Indian industries. The identified ISRs are for the original equipment manufacturers (OEMs), automotive manufacturing industries and consumer electronics manufacturing industries in India. These manufacturing sectors are currently in Industry 3.0 phase. They have automation features and advanced manufacturing technologies. They produce different varieties of products in the same factory. The experts believe that the identified ISRs are currently required in existing IMI as there has been a growing demand for smart technologies and features in the systems due to peer pressure on aggressive market competition and its long-term survival. Thus, the upgradation to smart manufacturing systems from Industry 3.0 is achievable rather than switching from the industries currently on the second industrial revolution [59]. These identified factors are the core parameters of Industry 4.0, and their application is highly required in existing IMI. These ISRs are the critical elements to improve productivity, implement innovative ideas, access and analyse the performance measure, and eliminate complexities virtually before final implementation. The industries need to adapt these 13 ISRs as listed below for their long-term survival and compete in the global market: 1. Reducing cost (A1) 2. Increase flexibility in scalability and variety (A2) 3. Increasing system responsiveness (A3) 4. Improving inter-connectivity among all the systems (A4) 5. Automatic data exchange and communication (A5) 6. Transparency through real-time data monitoring and exchange (A6) 7. Human-machine interaction (A7) 8. Mass customisation (A8) 9. Achieving greater efficiency (A9) 10. Improving productivity (A10) 11. Higher resource utilisation (A11) 12. Increasing global business (A12) 13. Reducing the lead time of production (A13)

Identification of barriers to adapt Industry 4.0
Similar to the process of identifying ISRs mentioned in Sect. 5.1, the barriers of I4.0 implementation were identified. In addition to it After going through the various research papers, related books and interviewing experts for their valuable inputs, the following 18 barriers faced while implementing Industry 4.0 in IMI are identified and finalised [8-11, 14-16, 23, 43, 51, 60, 61] 1. Lack of understanding and knowledge (C1) 2. Inadequate digital culture (C2) 3. Employee flexibility is required to learn and adapt (C3) 4. Implementation cost (C4) 5. Virtualisation and dynamic Integration (C5) 6. Integration of physical systems with cyber systems (C6) 7. Lack of proper global standards and reference architecture (C7) 8. Uniform standards for tools and equipment, systems, languages, networks, data services and reference architecture like design and selection (C8) 9. Quick reconfiguration of manufacturing systems, assembly systems, inspection and material handling systems (C9) 10. Real-time full data sharing and monitoring among various units, customers, suppliers, logistics and a few selected data sharing with other industries (C10) 11. Government policies and support (C11) 12. Data and Information security (C12) 13. Privacy protection (C13) 14. Clearly defined investment returns and economic benefits of digital investment (C14) 15. Uncertainty in the interests of industry 4.0 (C15) 16. Several complex legal issues (C16) 17. Lack of Integration of technology (C17) 18. Data quality and big data (C18) The identified ISRs and barriers are briefly described in Tables 2 and 3, respectively.

Questionnaire development
A set of elaborate questionnaires was designed initially as per the ISRs and barriers that affect industry 4.0 implementation in IMI. Two types of rating, each on a five-point Likert scale, were used. The points on the first scale are very low (VL), low (L), medium (M), high (H) and very high (VH), whereas those on the second scale are strongly disagree (1), disagree (2), neither agree nor disagree (3), agree (4) and strongly agree (5) as shown in Table 4. A five-point Likert scale was used to increase the response rate and response quality and help the respondents to respond to the questionnaire easily. As the response data obtained is highly uncertain and vague, the best mathematical simplification and approximation of such complex functions of the data set is trapezoidal fuzzy functions [62]. Thus, trapezoidal fuzzy numbers were considered in this methodology according to which the questionnaire is designed for the survey. Later on, the questionnaire was modified and tweaked as per the requirements, such as to receive qualitative data with less ambiguity for accurate analytical results. Finally, a set of 46 questions were designed, structured and finalised for the survey, which would take about 20 min for the experts to answers the questions. Some questions were related to the details of respondents like their work experience, department, sector, educational qualifications, organisation name. There was also an open-ended question for them to give their remarks if any.
The respondents were also asked some other questions like how much they are aware of Industry 4.0, its core technologies, and the first thing that comes to mind upon hearing about its core technologies and implementation. There were also few questions related to sectors in an organisation that could be benefitted from the application of Industry 4.0 and how much they believe that the factors of Industry 4.0 would help their organisations.
It was focused mainly on the identified factors that are considered as system requirements of Industry 4.0, such as reducing cost, increase flexibility in scalability and variety, increasing system responsiveness, improving inter-connectivity among all the systems, automatic data exchange and communication, transparency through realtime data monitoring and exchange, human-machine interaction, mass customisation, achieving greater efficiency, improving productivity, higher resource utilisation, increasing global business and reducing the lead time of production. Some questions were made mandatory without which the respondent could not submit the form, which eradicates the risk of incomplete datasets; hence, all the questionnaires can be used for analysis [51]. Participation in this survey was kept voluntary for the respondents.

Survey and data collection
For conducting the survey, the link to the survey form was initially sent to more than 300 respondents through emails, The efficient utilisation of resources, automation, data analytics and management faster reconfiguring manufacturing, the interconnection of various systems to make the decision and act at an instant, reducing lead time, and operating costs lead to the reduction of cost of manufacturing. A2 Continuous feedback from flexible market demand, the I4.0 systems, its database and components are interconnected with networks, leads to ramp up or down the production, and various customised products on a single manufacturing line as per the market requirement. A3 The application of quick reconfiguring tools, components, hardware systems and software enhance the system to respond quickly to the fluctuating demand, identifying the error and new introduction of the product with the help of interconnected networks. A4 Data sharing, transparency, and vertical and horizontal integration of systems in cyberspace improve the communication between systems without a human-machine interface and continuous monitoring on the production line for easier identification of faults and continuous improvements. A5 As the systems are interconnected through networks, these can make their own decision and share the data to the right place at the right time without any human interference. A6 The data collected from sensors, components, devices of the manufacturing systems to the user end through feedback are continuously monitored for improving and upgrading the existing production process and for the next launch. These multiple data and analyses in real-time assist the manufacturing systems to know the customer, their acquisition and predicting their type of demand and understanding the behaviour towards the products. A7 The real-time data helps the human operators for more accessible interactions with the systems to minimise errors by predictive analysis features and make the systems pro-active. A8 It allows the manufacturing systems to produce customised products as per the customer desire in the same production line without hampering the real productivity and operational efficiency. A9 With the application of data analytics to the full range of data collected from the systems, various performance measures, productivity, bottleneck issues, delay, downtime, lead time are continuously optimised with various computing tools. This leads to improving productivity. A10 With the application of advanced continuous monitoring process and data analytics, the chances of system breakdown, prediction for maintenance of tools and equipment, predictive analysis of the failure of the components, proper resource management leads to improving productivity. A11 The data analytics and data mining applications not only on production process but it is expanded to before production phase such as costumer's order without any hurdle, receiving raw materials from the source, its supply chain and logistics, storage and inventory, material handling, to after production phase such as supply chain, logistics and delivery to the customers. Everything is interconnected and kept on live tracking and monitoring. These data are analysed and optimised for continuous improvement, predicting any issues and failures in pre-and post-production phases, resulting in higher resource utilisation from end to end. A12 Today's aggressively competitive market needs automation in the business process for the survival and growth of industries. Automation of the entire business process through the Internet of things to handle complex business processes and fluctuating markets and services. In addition to it also enables customer-centric value creation in the global market. A13 Machine to machine and system to system interaction through networks and predictive analytics leads to reducing lead time in production. Digitalisation refers to the interconnection of all the manufacturing systems, design and development, logistics, market prediction, customer demand and feedback through wired or wireless networks where data are kept transparent and hassle-free, which is challenging to achieve in existing manufacturing industries and hence acts as a barrier to adapt I4.0 technologies. C3 As I4.0 is the latest technology and keeps upgrading daily to get robust and is entirely based on data analysis, big data, connectivity, and information technology applications, the employees need to upgrade their skills quickly to get acquainted and give their inputs efficient IoT solutions. Most employees find upgrading themselves to multi-skilled a challenging task and uncertain about its future implications, which creates disruptions and fear of losing jobs. Thus, it acts as a barrier to adapt to I4.0 technologies. C4 Implementing I4.0 requires high investment in technical aspects and re-tooling aspects; moreover, industrialists mainly belonging to small and medium scale enterprises are unsure about its business model for its long-term survival, which acts as a barrier to adapt I4.0 technologies. C5 It refers to the interconnection of end-to-end digital integration of the manufacturing process and complete transparency in data sharing. From these databases, virtual simulation and optimisation are processed continuously with real-time data, complex networks and tools to improve production efficiency and simultaneously reconfiguring the manufacturing systems as required.
And implementing this in existing MSMEs acts as a barrier to adapt I4.0 technologies, as traditional methods are only used for optimisation. C6 Many complexities are generated to design and integrate the numerous traditional manufacturing systems and components via complex coding and software technologies and interconnect them to cyberspace. This creates barriers in adapting I4.0 technologies in technological and implemental aspects. C7 It refers to global industry standard and reference architecture as it is highly likely for the universal language of the production process to keep updated with the latest technology, customisation needed, demands, prerequisites of customers and sharing information. Due to a lack of standardisation, it acts as a barrier to adapt I4.0 technologies. C8 Lack of uniform standards for tools and equipment, systems, languages, networks, data services and reference architecture like design and selection, supply chain services for more accessible communication and data transparency between organisations leads to a barrier to adapting I4.0 technologies. C9 Due to a lack of quick reconfiguration technologies in existing manufacturing systems and their sub-components and systems, especially in MSMEs, it acts as a potential barrier to adapt I4.0 technologies. C10 Real-time full data sharing and monitoring among various units, customers, suppliers, logistics and a few selected data sharing with other industries is still a novel paradigm. Moreover, the complex programming and modelling of massive data clusters generate difficulties without accessing the actual data from the systems and components. Lack of transparency in systems acts as a potential barrier to adapt I4.0 technologies. C11 The Government takes some initiatives in bringing the policies to implement and modernise existing MSMEs. However, it's still insufficient to implement successfully due to uncertain business model and returns and reluctance to spend a fortune; hence, it acts as a barrier to adapting I4.0 technologies. C12 There is a significant concern regarding data and information security of confidential industrial data on cyberspace due to fear of cyber-attack resulting in mass exposure of vulnerable data on the public domain. Thus, it acts as a barrier to adapt to I4.0 technologies. C13 Data privacy is also crucial; managing massive data, protecting the individual's critical information, an organisation like bank accounts, cards, identity, address, contact number without any leaks and misuse, is the most challenging chore in cybersecurity in a secured server. Due to this threat, it acts as a barrier to adapt I4.0 technologies. C14 As I4.0 is novel and its application is still in the development stage, there is uncertainty in benefits and financial profits from the business models after implementation, which requires robust empirical research with numerous real-time case studies. Thus, it acts as a barrier to adapt I4.0 technologies. C15 Uncertainty in I4.0 due to its low vision and implementation strategy and many MSMEs are unaware and unsure about this technology and hence reluctant to adopt. C16 Digitalisation creates legal issues on data privacy, security, artificial intelligence. Strict law regulations should be taken into account to prevent data and privacy infringements. This acts as a barrier to adapt I4.0 technologies. C17 Lack of technology integration due to complexities in operational technologies, roadmaps, and the physical system to a cybersystem creates a barrier to adapting I4.0 technologies. C18 Data storage, maintenance, and assessing humongous data without compromising on its quality is challenging as these data collected from each component, sensors, products, manufacturing systems, inspecting equipment, market and sales, and customer feedback generates enormous data. This big data helps the systems predict and make decisions from the feedback, requiring high-quality data. This acts as a barrier to adapt I4.0 technologies.
LinkedIn direct messages, text messages, social platforms and personal contact in the first month. At first, the survey was confined to the manufacturing industries across major cities in India. Then, it was expanded to service-based and solution-based industries and finally to all types of organisations. One covering letter was attached with the form requesting the respondents to fill it up only if they know or work in Industry 4.0 technologies. The respondents were also given a limited time frame of about five weeks to submit their responses. From the initial survey, only 30 complete responses were received after the end of one month. The pilot Reliability test for the first ten and then 30 responses were analysed in SPSS for the received data to check whether the questionnaire set is applicable and robust for surveying the next phase or not. Cronbach's Alpha values came out to be 0.90 and 0.91 for the first ten and 30 responses, respectively. As both values are more than 0.70, the obtained results are satisfactory enough to carry on the ongoing survey without modifying the questionnaire [8,9]. In the next phase, 200 respondents were sent through secondary sources such as industrial websites, industry 4.0 forums, institute alumni, google searches, industrial sites and articles, newspaper articles and other sources. Then, in the next month, the questionnaires were sent to subsequent 400 respondents, counting 50 respondents each week for the next two months through the same modes and telephonic interview. Multiple reminder emails, telephonic calls, and text messages were sent to the non-respondents till responses were obtained. After numerous reminders and discussions about this survey with some respondents, a total of 224 responses was received finally at the end of 6 months. The response rate is 24.8% which is sufficient to analyse the research work. All these responses were structured, categorised and noted into an excel sheet database for each respondent separately [63].
The respondents were from various departments and designations so that there should not be any biases in the survey, such as working as Sales Personnel, CEO

Data analysis and results
In this section, the pilot data analysis was conducted, and necessary tests like the KMO test and Bartlett's test were run as prerequisites for validating the data set and checking the legitimacy of the identified factors. Then, descriptive statistics analysis was conducted to check the significance of the identified system requirement factors and barriers. Then, fuzzy techniques were used to evaluate and prioritise the identified ISRs and the barriers. Finally, an inter-item correlation analysis was conducted among them.

Initial statistical analysis
The statistical data have been initially surveyed and collected through various sources from government websites,  (5) (0.7, 0.8, 0.9, 1.0) Fig. 1 Timeline of the survey response received world bank, government financial reports, yearly manufacturing reports, Indian brand equity foundation reports and various news articles to analyse the modern manufacturing environment, its performance, capacity utilisation, exports, growing demand and measures to meet the demand of the customers. These data help make decisions and remarks for the market context's current uncertainty based on the customers' demands and variable production.
In the next step, the collected data's pilot analysis was conducted to know about the respondents' backgrounds, educational qualifications, working experience and industrial backgrounds. Figure 1 shows the survey's timeline and the responses received from the respondents of various sectors. Figure 2 shows the type of organisation to which the respondents belong. It characterises the type of industries in which the respondents are currently working. About 76% of the respondents were from various private sectors and MNCs throughout the country, and a little more than 8% were from educational and public sectors combined. Most of the respondents are from product-based sectors that are about 29%, the next 25% are from service-based sectors, and the rest belong to IT, R&D and educational sectors. Figure 3 shows how much the respondents are aware of I4.0 and its core technologies. It is observed that the maximum number of respondents, i.e. about 67% of them, are highly aware, and about 27% of them are somewhat aware, and very few of them are less aware of I4.0 technologies. Figure 4 shows the respondents' educational background. It is observed that the maximum number of respondents, i.e. about 53% are postgraduates and 39% are graduates. Figure 5 represents the respondents' years of working experience ranging from one year to 50 years. From the pilot analysis, it is observed that most of the respondents belonging to private sectors and MNCs are currently working on I4.0 and its core technologies.

Necessity analysis
The following necessity analysis was conducted using IBM SPSS 25 to validate the legitimacy of the collected data. Any future analysis and evaluation can be conducted, relying on   Table 5 shows that the Cronbach alpha value is 0.881. As this value is above 0.70, the appropriateness of survey data is ensured. So, it can be analysed further without modification and reframing the questionnaire [64].
Kaiser-Meyer-Olkin (KMO) analysis is performed to measure the proportion of variance in factors identified and quantified for analysis due to the underlying factors' presence. The KMO proportion value must be lower [65,64,51,15]. Table 5 shows that the KMO proportion obtained is 0.872. As this value is greater than 0.5, the concerned data can be considered meritorious for factor analysis, and the identified factors are legit.
Bartlett's test of sphericity is also analysed to identify the correlation matrices of ISRs and barriers. As the p value of the obtained results is less than 0.01, factor analysis can be used on the data set in future works [15,66,67].
Descriptive statistics analysis was carried out for both ISRs and barriers, and the results are shown in Figs. 6 and 7. It is observed that the resulting mean value for both barriers and ISRs stands out to be higher than 3. Thus, it is concluded that the data sets collected are significant and can be used for various future analyses [15,66].

Multi-criteria analysis
The respondents working in the current Indian manufacturing industries believe that the identified ISRs and the barriers to adapt Industry 4.0 technologies would play a vital role in their respective organisations [23]. The identified factors are rated and evaluated to identify the most dominant barrier affecting Industry 4.0 in their organisations and the most dominant existing ISR that the respondents believe would benefit their organisations. The number of ISRs and barriers identified in the present analysis are 13 and 18, respectively, which are quite large. So, the calculation is tedious and time-consuming. Hence, all the pairwise methods were avoided [68]. Moreover, these identified barriers and ISR may contain several conflicting and intangible criteria, and the ratings from the respondents are in linguistic forms of subjective data set. Thus, for analysing the linguistic variables, each criterion is needed to be quantified for calculation. Computing tools like the fuzzy technique are generally used to represent vague information with the help of the membership function and fuzzy numbers and effectively reduce vagueness in the responses from the survey data [69]. Thus, to narrow down the selection process and get a more realistic result in decision making, the fuzzy multi-criteria decision analysis tool is quite suitable for obtaining the weightage of each criterion in this study [62]. These weights can be ranked accordingly in identifying the most dominant criterion.
As shown in Fig. 8, five fuzzy sets are used to collect the ratings from the respondents through the questionnaires. All the ratings were collected and categorised for efficient use of the data for analysis. These five fuzzy sets are numerically expressed as trapezoidal fuzzy numbers (TrFNs), as shown in Table 4.   Similarly, all the ISR ratings are converted into fuzzy numbers, generating a fuzzy matrix. Next, all the ratings are aggregated using the mathematical expressions given in the following sub-section.
Defuzzification is the process of finding an equivalent single crisp value for a fuzzy set. Thus, the aggregated fuzzy weights are to be defuzzified using a suitable defuzzification method. The centre of the area (COA) method for defuzzification is applied here to find the crisp weights of ISRs and the barriers. The obtained results are plotted form of a column chart in Figs. 9 and 10, respectively.
The mathematical explanation for the COA method of defuzzification of the fuzzy weights for the barrier is given below: Similarly, defuzzification of the fuzzy weights for the ISR is presented in the following relations:   Fig. 10. Using expression (2), the crisp values of ISRs are obtained and plotted in Fig. 9. Each of the crisp values is arranged in descending order and tabulated in Tables 8 and 9.

Results of multi-criteria analysis
All the identified barriers and ICRs are ranked as per their weights obtained with the fuzzy multi-criteria analysis. All the identified barriers and industrial system requirements are vital in their respective means. However, this study's main area of interest is confined to the top three factors per their rankings in the multi-criteria analysis, which motivates the researchers to focus on these dominant factors and find effective solutions for initiating implementation. This will help the top management to make strategic decisions and approaches for initiating the implementation process in their respective organisation using top-ranked factors. In this paper, the top three dominant criteria are given more priority than the rest because industrial management experts, solution providers, researchers and practitioners can focus on the initial phase during the implementation of I4.0 technologies in their existing industrial systems and then giving priority to the next as per their rankings. The initial phase of implementing Industry 4.0 technologies mostly depends on effective solutions to the top dominant factors, as obtained in Tables 8 and 9.
The top three existing Industrial systems requirements that are highly required in existing Indian manufacturing industries are discussed below: 1. Table 9 shows that A6 (transparency through real-time data monitoring and exchange) is the most dominant ISR. Industrial experts believe that it is highly required in the existing manufacturing industries. As in existing Indian manufacturing systems, there is no proper transparency in data collection or any information shared among the systems, and human interference increases, causing bottleneck issues, delays, defects in parts and products. So, the data collected from sensors, components, devices of the manufacturing systems, human operators and tool systems need to be interconnected with a communicating medium to provide continuous feedback and monitoring from the real-time data during manufacturing. This helps to improve and update the existing production process and send instructions to use the updated data in the next production batch. These multiple data and analyses in real-time assist the manufacturing systems in knowing the customer, their acquisition and predicting their type of demand and understanding the product's behaviour, which is essential in existing manufacturing scenarios.
2. This is followed by the ISR factor A5 (automatic data exchange and communication). The respondents from various organisations believe that because of real-time data transparency through interconnected networks, it is advisable to look at the fluctuating and aggressive market competition and the systems to communicate with each other before making decisions without any human intervention. These systems automatically receive and deliver the right data to the right place at the right time. It saves a generous amount of time and ultimately improves productivity. As the systems are interconnected through networks, these can make their own decision and share the data to the right place at the right time without any human interference.
3. The 3rd most essential system requirement is the factor A10 (improving productivity). In general, every industrial expert wants his current productivity rate to be improved through traditional optimisation methods, industrial management and other techniques, or by implementing advanced technology, re-tooling to the latest equipment and hardware, and upgrading their existing systems. With the application of I4.0 technologies such as advanced continuous monitoring process, machine to machine communication, real-time data sharing, data analytics and digital twin, the chances of system breakdown, prediction for maintenance of tools and equipment, predictive analysis of the failure of the components, proper resource management leads to improving productivity. It is highly required in current manufacturing industries.
The top three dominant barriers that affect Industry 4.0 and its core technologies implementation in the present manufacturing industries are discussed below:    Table 8 shows that C3 (employee flexibility required to learn and adapt) is the most dominant barrier that affects the implementation of I4.0 technologies in the Indian manufacturing industries. I4.0 is the latest technology consisting of digitalisation of traditional systems, connectivity, data-centric, information and technology, internet of things and data-driven. Before the industrial system upgradation, the current employee should adopt the latest skills and latest technology-driven software and hardware. Thus, the employees need to upgrade their skills, learn data analysis, languages, get acquainted with the latest technologies to monitor the new systems, and give their valuable inputs for efficient internet of things solutions. Most employees working in a traditional environment find difficulties and are hesitant while upgrading themselves to multi-skills. The organisation's employer finds it challenging to motivate the experienced employees, especially those who are reluctant to adopt the upgradation due to uncertainty about its future implications, thus creating disruptions and fear of losing their jobs. So, the difference in opinions among the organisation's officials, employees, contractors, and management becomes a major challenge to adapt I4.0 technologies in IMI. This challenge needs to be addressed and prioritised to develop a proper solution for adapting I4.0 technologies.
2. The next dominant barrier is C6 (Integrating physical systems with cyber systems). As discussed in Sects. 2 and 3, cyber-physical system is an entirely data-driven application that inter-connects the virtual space with physical components of the systems through integrated computing, complex algorithms, smart sensors data, embedded systems, communication, big data, data analytics and storage capabilities with the help of complex networks and internet. Most IMI, especially micro, small and medium enterprise (MSMEs), is still in the post-second industrial revolution. So, the upgradation of conventional systems and investing capital in these enterprises is a major challenge. Large enterprises such as automobile manufacturers, OEM, electronics goods and gadgets manufacturers are likely to adapt I4.0 technology. Nevertheless, still end to end digital integration, real-time data sharing, installation and upgradation of smart sensors, smart actuators, new software and hardware in existing manufacturing systems without complete production held-up is also challenging. Major research work needs to be carried out for providing a robust standard solution for implementing cyber systems and integrating them into physical systems in manufacturing industries.
3. The 3rd most dominant barrier is C10 (real-time full data sharing and monitoring). The data sharing occurs among various units, customers, suppliers, logistics and few selected other industries. The main concern is the huge storage of big data, continuous analysis, and virtual simulation that is digital twin using real-time data to improve efficiency and predict defects and machine tool failure. Cybersecurity, data privacy, and continuous monitoring of shared real-time data create a huge challenge to implement I4.0 technologies in existing manufacturing systems. Extensive and empirical research works need to be carried out, especially by the solution-based consultancy currently working on this issue.

Correlation analysis
The linguistic data set of all the 224 respondents for ISRs and the barriers were initially converted to numerical variables for conducting analysis in SPSS software. Each variable represents the value of the ratings provided by respondents from the questionnaire. The correlation analysis gives how each variable of ISRs and the barriers are related to each other and shows what type of relationship is shared with them. The inter-item correlation analysis is performed for barriers 'C' and ISR 'A' separately to investigate the linear relationship between them and observe how closely each variable of ISR shares a relationship to each barrier individually. All the results of the analysis are tabulated in Tables 10, 11 and 12. The primary analysis is focused on the correlation between barriers and systems requirements of Industry 4.0 and drawing out conclusions from it. The correlation coefficient ranges from − 1.0 (a perfect negative correlation) to + 1 (a perfect positive correlation). The more the value gets closure to − 1.0 or + 1.0, the stronger the relationship between the inter-item variables. As this value gets closer to 1, the relationship among them is stronger. Conversely, if this value is closer towards 0, the corresponding relationship is weaker [67]. The degree of correlation is termed as 'higher' when its coefficient is above 0.75, 'moderate' when it ranges between 0.50 to 0.75, 'lower' when it ranges between 0.25 and 0.50 and 'negligible' for values in the range 0 to 0.25 in both positive and negative coefficient values [65]. If the relationship shared between the barriers and ISRs is positive, then the variable's value is directly related. Thus, if the value of one variable increases, it impacts the corresponding variable's value to increase. This results in identifying the variables like barriers that are more closely related to the ISRs. Various research directions can be introduced. Several hypotheses can be drawn from each relationship, which will help the industrial management implement the I4.0 technologies in IMI.

Results of correlation analysis
For better understanding, all the correlation values are highlighted in separate colours in terms of higher, moderate, lower, negligible and negative degree of correlation. Each representation of the coloured cell are mentioned below in each tables. Since the variable of ISRs and barriers when correlated with the same variable with each other its result is always obtained as '1'; hence, it is not considered in the analysis. It is quite lengthy to explain every coefficient of both barriers and ISRs. So, one coefficient selected randomly each from Tables 10-12 has been discussed below.  A1  A2  A3  A4  A5  A6  A7  A8  A9  A10  A11  A12  A13   A1  1 Table 10, the coefficient of correlation of the industrial system requirement factor A6 (transparency through realtime data monitoring and exchange) and the factor A10 (improving productivity) is 0.6265. Thus, the variables are positively correlated. If the value of the variable A6 tends to increase, the value of variable A10 would also increase. In a practical case, if the system were interconnected from where the real-time data could be monitored and exchanged with data transparency, it affects by increasing the value of the variable A10. That means it would increase productivity through continuous monitoring of real-time feedback. In this way, coefficients of all variables are related. The more the value of the coefficient, the more the variables are related to each other. In Table 10, it is found that all the variables of ISRs are positively correlated. There is only one correlation that has a higher degree of correlation (i.e. 0.75-1.00) between ISR A9 (Achieving greater efficiency) and the ISR A10 (Improving productivity) whose value is 0.8240 which is the maximum among all correlation values. Importance can be given the higher degree of correlation for finding various paradigms that different practitioners and solution providers can refer to prioritise the system requirements in IMI. Accordingly, finding effective solutions to implement these obtained requirements successfully in existing manufacturing systems. In this table, 98 coefficient values are of moderate degree of correlations (i.e. 05-0.75) and 56 coefficient values are of lower degree of correlations (i.e. 0.25-0.5). Highest value of coefficient in moderate degree of correlation is between A3 and A4, that is 0.709. Similar conclusions can be derived for the obtained correlated values for the rest of the variables having moderate degree and lower degree of correlation. Subsequently, priority to be given to these correlation as per the decreasing order of the degree of correlations. Table 11, it is found that all the variables of barriers are positively correlated. There are no higher degrees of correlation between any two barriers. However, 4 coefficient values have moderate degree of correlation the barriers C12 (data and information security) and C13 (privacy protection), and barrier 14 (clearly defined investment returns and economic benefits of digital investment) and C15 (uncertainty in the interests of Industry 4.0). The highest values among the moderate degree of correlation is obtained between C12 and C13 Referring to a subsequent higher degree of correlations between the barriers, several framework models and maturity models can be designed and developed as per the existing IMI scenario. Next priorities can be provided to the rest of the obtained moderate degree of correlation barriers, and similar conclusions can be obtained.

Similarly, in
3. In Table 12, it is observed that all the coefficients are not positively correlated. There is a prior requirement to give importance to the positive correlations. In this table, it is found that there is no higher degree of correlation nor or moderate degree of correlations exists between the ISRs and the barriers. Thus, to obtain the next important correlation between them, the lower degree of correlations between them is considered from this table. There are seven lower degrees of correlation between them, that is, [A3 (increasing system responsiveness) and C9 (quick reconfiguration of manufacturing systems, assembly systems, inspection and material handling systems); A13 (reducing the lead time of production) and C6 (integration of physical systems with cyber systems); A9 (achieving greater efficiency) and C6 (integration of physical systems with cyber systems); A12 (increasing global business) and C9 (quick reconfiguration of manufacturing systems, assembly systems, inspection and material handling systems); A11 (higher resource utilisation) C3; A4 (improving inter-connectivity among all the systems) and C6 (integration of physical systems with cyber systems); and A11 (higher resource utilisation) and C6 (integration of physical systems with cyber systems)]. These seven lower degrees of correlation between the ISRs and the barriers can be referred to and given the highest importance by IMI, practitioners, researchers, solution providers in the initial phase for further research work. Here, the importance of a uniform standardise reference architecture comes to the surface for successful implementation. Moreover, it motivates to find some effective solutions, designing and developing related framework models and maturity models to fulfil the requirements of IMI and the subsequent barriers caused during the implementation of Industry 4.0 technologies in their organisations.
4. Out of these seven top most coefficient values, the A3-C9 have the highest coefficient values. Thus, more priority can be given to this ISR and barrier while developing implementation strategies. It is also observed that the ISRs A4, A9, A11 and A13 corresponds to more times with the barrier C6 as per the highest value of coefficient relation between them. This shows the importance of the barrier C6, caused while integrating into existing manufacturing systems and the relation with these four ISRs as per the analysis through the ratings provided by the industrial experts. This barrier, along with the four ISR whose connection can be merged to form a conceptual model while developing implementation strategies and various dimensions of the maturity model, readiness models at different stages of IMI 5. All the negatively correlated coefficients are marked with an underline, as shown in Table 12. This negative correlation determines that if one variable increases, the corresponding variable decreases. It helps to learn the type of relations shared between the ISRs and barriers that the researchers, practitioners, and industrialists can give the least importance for analysis and implementation. For example, the ISR A2 (increase flexibility in scalability and variety) and the barrier C1 (lack of understanding and knowledge of Industry 4.0 technologies) are negatively related. If the importance of the ISR increases, then simultaneously, this barrier's value decreases and vice versa.

Discussion and implications
This research work tried to answer the research questions pointed out at the beginning of this paper. This study identifies the major requirements of today's Indian manufacturing systems from the literature with the help of industry experts who believe it is currently required in existing manufacturing systems and identifies the barriers that affect the implementation of I4.0 technologies in their existing manufacturing systems. Many ISRs and barriers have been pointed out in the previous works reported in the literature for various industrial sectors of their respective countries separately, their industrial systems and their strategies to implement I4.0 technologies. Many solution-based organisations have identified the ISRs in their respective yearly reports as per their perspectives for different organisations. The ISRs and the challenges faced by IMI during I4.0 implementation have been briefly described in Tables 2 and 3. Some other challenges, such as lack of managerial support and their vision and varying skill gap from one factory to another, come to the surface.
This study is a survey-based approach from the Indian industrial expert's point of view regarding their existing manufacturing systems. The finding of this study is to identify the ISRs of IMI and ranked them as per their importance from the survey, and ranking the identified barriers that caused during implementing I4.0 technologies in IMI's system from the survey. This ranking provides a direction for researchers, practitioners, solution providers, industrial management teams, and experts to narrow their ambiguities and shift their initial focus to the obtained dominant factors.
This study motivates the researchers, practitioners, solution providers for carrying out several research work in this area, digging deep into the causes and effects of the obtained dominant barriers along with the dominant ISR to minimise the gap while implementing and however making a proper organisational strategy for the effective implementation of I4.0 technologies in IMI.
Indian industries are still at the early stage of implementation of I4.0 technologies. As discussed in Sect. 3, Industry 3.0 technologies are not implemented entirely in all the manufacturing industries of India. Most industries, especially SMEs, are still in post electrification phase, currently using basic manufacturing technologies and mostly resource consumption and labour-intensive types. However, there are currently many fully automated and partially automated industries in India, including automotive, consumer electrical and electronics, health care and pharmaceutical, original equipment and machines manufacturing, and other product-based industries like cement, garment, food processing and solar which are running under latest technologies. Yet, complete digital transformation, connectivity and upgradation related to smart technologies is still at its early phase in most of the Indian industries due to the implementation barriers.
There are few similarities of implementation barriers faced by Indian industries and other foreign industries like privacy protection, lack of uniform standard and reference architecture for implementation, intra and inter-organisation data sharing and transparency, horizontal and vertical integration, and integration of physical systems into cyberspace [25,49,[74][75][76]. In addition, industry size and type, having relatively capital intensive creates a huge impact on implementing I4.0 technologies as they are capable and advanced in adapting the latest technologies in their existing systems.
The main differences of the barriers during the implementation of I4 technologies between the industry in India and other places are the huge gap of core technology, including IT Infrastructure, connectivity and digitalisation of physical systems. Few components, smart sensors and other devices are not readily available in India, which need to be shipped from other countries. The second main difference is the employees' skill gap. Workers and employees in other countries are mostly multi-skilled, and they continuously upgrade their skills from time to time as per the requirement. For example, in India, sufficient workers are available for a particular job, so there is a minimal requirement of multiskilled labours in most industries. So, most workers are skilled for a particular job or two and do that task efficiently. Thus, for digital transformation, employees need to upgrade their skills quickly, adapt to the latest technologies, learn languages, coding and several software to get themselves familiarised and give their inputs for a proper employee value chain.
Other differences in the barriers Indian and other industries face are research and development investments. As per the Global Research and Development Expenditure Fact Sheet Report, the USA spends $675.5 billion in current PPP dollars, while China at $525 billion, Japan at $173 billion and Germany at $147 billion, which are the top manufacturing giants in the world [77]. As per the Research and Development Statistics, the Government of India spends PPP $47.2 billion on Research and Development, which is significantly lower than the countries mentioned above [78]. This creates a huge impact on technological advancement and the upgradation of existing manufacturing industries in India. With this, the government policies of India for initiating and investing for implementing I4.0 technologies are up to a certain limit compared to the above countries. This results in a lack of motivation, research interests in both Industry R&D facilities and academics for significant breakthroughs and innovation for transforming Indian Industries.
The competition among the Indian industries are growing, and the customers' requirements and expectations are increasing for more customised products and services. Thus, the demand for adopting modern technologies in existing manufacturing systems is also growing. However, to fulfil these requirements, the Indian industries need to adapt the smart technologies in their existing systems, where the implementation challenges come to the surface, hence creating a large gap between ISRs and the implementation barriers.
Few researchers have prioritised challenges using different techniques as per their perspectives in the past. [15], pointed out and evaluated 18 challenges of I4.0 initiatives using the AHP method for sustainability of supply chains in the Indian context, and priority of the challenges were identified. They obtained 'organisational challenges' as the topmost priority, which includes the sub-challenges such as 'financial constraints', 'low management support and dedication', 'reluctant behaviour towards Industry 4.0', 'poor company digital operations vision and mission', 'lack of competency in adopting/applying new business models' and 'low understanding on Industry 4.0 implications'.
[23], identified ten challenges in environmental protection and control in I4.0 implementation in Bangladeshi industries. Out of these ten challenges, severe and less severe challenges were determined using the best worst method. The most severe challenge is 'lack of technological infrastructure', and the least severe is 'environmental side effects'. [8,9], presented 12 adoption barriers of I4.0 in IMI, analysed through interpretive structural modelling (ISM) technic using fuzzy MICMAC analysis to obtain the driving and dependence relationship between the critical barriers. From this analysis, they found that the challenge 'lack of clear comprehension about the Internet of things benefit' is the first-level challenge followed by 'high implementation cost'.
However, in the present study, a total of 13 ISRs have been identified and ranked in the context of IMIs by applying fuzzy multi-criteria decision analysis from which the 'transparency through real-time data monitoring and exchange' has been found to be the most dominant ISR. In addition, 18 barriers faced by IMIs during the implementation of I4.0 technologies in their existing systems were identified, and they were ranked by applying fuzzy multi-criteria decision analysis from which the 'employee flexibility required to learn and adapt' is observed to be the most dominant barrier that affects the implementation of I4.0 technologies.
Few research works and solution provider's articles and annual reports address the existing system requirements based on the survey of industry experts' opinions, especially in IMIs. There have been hardly any studies reported in the past on the relationship between the existing ISRs and barriers in IMI, which creates a gap between the actual current requirements of the industries and the challenges faced while implementing the I4.0 technologies.
So, to address this gap between existing ISRs and barriers, the inter-item correlation analysis has been conducted between each factor of ISR with each factor of barrier to find how closely the ISRs are related to the barriers and vice versa, as explained in Sect. 6.4.1. It determines the type of relationship shared between each factor of ISR with the rest of the barriers, like how the coefficient of each factor of ISRs will affect the coefficient of barriers if they are positively related or negatively related. Therefore, these correlations values obtained in the analysis demonstrate how each system requirement is affected by the implementing barriers. The gap between the existing ISRs and the barriers can be minimised with the obtained values of degree of correlation between each ISR and each barrier. This will help to prioritise system requirement factors and the challenges faced while initiating the implementation process in the existing manufacturing Industries. It helps to narrow down the search space for the industrial experts, decision-makers, solution providers, and researchers to focus on the topmost degree of correlated factors, which are silent in previous findings, especially in the context of IMI. These degrees of correlation can be directly or indirectly used as a referring tool and developing various strategic approaches, maturity models, and architecture models by practitioners and researchers to develop and propose various models and approaches for implementing I4.0 as technologies per the existing system requirements IMI.
In this study, there is no higher degree of correlation between ISRs and barriers, so the second most priority can be given to the factors with the moderate degree of correlation, next to the lower degree of correlation. If the value of one variable increases, then the second corresponding variable also increases and vice versa. So, the positively correlated coefficient factors are given the highest importance, as explained in Sect. 6.4.1. For example, the respondent feels that this particular ISR is an essential factor that should be present in the existing IMI. If its importance increases, it also increases the value of the corresponding correlated barrier that affects I4.0 adaptation and minimises the gap between them.
As per the results obtained for the correlation between ISRs and barriers explained in Sect. 6.4.1, having a lower degree of correlation can be considered for initiating a referring tool for practitioners, solution providers, researchers, and industrialists. Relations and conclusions can be drawn on how systems requirements are affected by implementing barriers and vice versa and focusing on specific ISRs and the barriers, having positive correlation. All the negatively correlated are to be given the least importance. As per the seven, positively correlated coefficient having lower degree of correlations can be used to develop various architectural framework models, conceptual models, maturity models, readiness models and strategical approaches for the Indian industries for implementing Industry 4.0 technologies in their organisation effectively. In addition, a different approach can be developed for the highest coefficient values of correlation between ISRs and barriers. The barrier with higher coefficient values and similar corresponding ISRs on the same row, as shown in Table 12, can be grouped, the hypothesis can be drawn based on their relations shared. This grouping model can vary from industry to industry in India as there are no uniform standards and cross-industry information sharing. So, to initiate uniform standards, especially for Indian industries, grouping the ISR or the barriers based on the findings can be used by practitioners, solution providers and researchers in different dimensions of maturity models while developing implementation strategies of I4.0 in IMI.
This study delivers practical and managerial implications, especially in automobile industries, OEM industries and electronics goods and gadgets manufacturing industries, on prioritising the importance of the implementation barriers and the ISRs of existing IMI that need to be worked upon while implementing I4.0 technologies based on the identified the highest correlated ISR and barriers. This study helps to develop various organisational strategic approaches by the top management level, methods to motivate the employees, upgrading their skills to minimise the skill gap from the digitalisation, IoT, and integration during implementation. With the clear insights and potential of I4.0 and its broad vision, it motivates the decisionmakers and the industrial experts in taking strategic decisions and during vertical and horizontal integrations of I4.0 technologies in their existing systems based on these findings. This study's findings can contribute to developing some practical guidelines for the industrial experts for proposing solution-based architecture models for implementing I4.0 technologies. This architecture can be used as a standardised reference model by the emerging IMIs for adopting I4.0 technologies in their existing organisations.
The theoretical implication of the study is to work upon the identified 18 barriers and 13 ISRs along with their weights from which various implication framework models and multi-criteria decision-making problems with various case studies can be developed, which can be used in any organisation. This study gives a theoretical background of I4.0, its enabling factors, and the present scenario of IMI. The present researchers can explore I4.0 implementation and collaborate with other researchers to build a scientific collaboration or partnership with the solution providers from various perspectives of the challenges based on obtained dominant ISRs, dominant barriers and the highest correlated ISR and barriers. Based on these findings of the relationship between barriers and ISRs, the academic institute can set up a dedicated I4.0 laboratory for empirical analysis.
This study also enables social implications for solving various social problems smartly, disruptions of losing jobs, skill upgradation of employees, challenges in knowledge and information sharing, its inflow and outflow across different departments and the sectors, managing regional inequalities in India. Integrating physical space into cyber space enables people to learn and adapt the latest application of smart technology, creating awareness among society about its products, its accessibility, motivating to use smart technologies, creating social chains, and simultaneously adding values to society.

Conclusion
The present research work identifies the most weighted barriers that affect Industry 4.0 implementation and the current ISR necessary for the existing manufacturing systems. This paper points out the relevant areas and narrows them down by minimising the gap between existing ISRs and the barriers affecting implementation and prioritising the importance of carrying out the research work to implement I4.0 technologies. At first, the necessity analyses such as the KMO, Bartlett's test of sphericity and descriptive statistics analysis were conducted to check the legitimateness of the identified barriers and ISR data set. It provides a guideline to conduct factor analysis, which could be considered in future works. In the next, multi-criteria analysis conducted in the fuzzy environment aids the decision-makers and industrialists to identify and address this highest weighted ISR and barrier. All the ISRs and barriers are arranged in decreasing order to give weightage for future research during implementation.
As per the ranking, the most dominant implementing barrier is C3 (employee flexibility required to learn and adapt) where as the most dominant ISR is A6 (transparency through real-time data monitoring and exchange). The top three dominant barriers and ISR were obtained, providing a roadmap to the researchers, practitioners, and solution providers to initiate research and execution of I4.0 technologies adoption. The obtained weights of the ISRs and the barriers obtained in this paper can also solve several MCDM problems in many manufacturing industries seeking to adopt smart technologies. Lastly, the inter-item correlation between the ISRs and the implementing barriers of Industry 4.0 was analysed, which determines the relationship they shared. The most positively correlated variables between ISRs and barriers is ISR A3 (increasing system responsiveness) and barrier C9 (quick reconfiguration of manufacturing systems, assembly systems, inspection and material handling systems). Although it has a moderate degree of correlation between them, the rest of the remaining six correlated variables can also be used as a referring tool for strategic implementation of smart technologies and a gateway for minimising the gap between ISRs and barriers I4.0 implementation. In the future, based on these analyses and findings, the authors seek to work on an architectural framework model that can be designed and developed in the context of the implementation barrier and ISR factors for adapting Industry 4.0 in IMI. It can be used as a tool for evaluation in numerous case studies.
Author contribution Both the authors mentioned in this manuscript contributed equally to selecting research tools, performing analysis, writing and evaluating this research work.

Funding Not Applicable.
Availability of data and material The dataset was collected from a questionnaire-based survey as mentioned in Sects. 5.3 and 5.4, respectively.
Code availability Not applicable.

Declarations
We wish to submit an original research article entitled 'Analysis and evaluation of Indian industrial system requirements and barriers affect during implementation of Industry 4.0 technologies' for consideration by The International Journal of Advanced Manufacturing Technology. We confirm that this work is original and has not been published elsewhere, nor it is currently considered for publication elsewhere.
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Conflict of interest
The authors whose names are listed in this manuscript certify that they have NO conflict of interest also NO affiliations with or involvement in any organisation or entity with any financial in-terest (such as honoraria; educational grants; participation in speakers' bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.