111 Send Orders for Reprints to reprints@benthamscience.net Recent Advances in Computer Science and Communications, 2022, 15, 111-130 RESEARCH ARTICLE     Assessment of Risks for Successful Implementation of Industry 4.0 Rimalini Gadekar1,*, Bijan Sarkar2 and Ashish Gadekar3 Recent Advances in Computer Scienceand Communications 1 Mechanical Engineering Department, Government Polytechnic, Gondia, Maharashtra, India; 2Production Engineering Department, Jadavpur University Kolkata, West Bengal, India; 3Faculty of Management, Amity Institute of Higher Education, Ebene, Mauritius Abstract: Purpose: The transformation happening globally, though referred to by different names and nomenclatures, the overall objective to inspire digitalization and smart practices by reducing human intervention and enhancing machine intelligence to take on the global manufacturing and production to another level of excellence is a proven fact now. However, earlier research has been found lacking in the strategic approach to evaluate and analyze the I4.0 adoption-related risks for its implementation. This ultimately deprived organizations of a multitude of the benefits of I4.0 adoption. This research proposes a systematic methodology for understanding and evaluating the most evident risks in the context of I4.0 implementation. ARTICLE HISTORY Received: November 03, 2019 Revised: April 29, 2020 Accepted: July 14, 2020 DOI: 10.2174/2666255813999200928215915 Design/Methodology/Approach: The research is mainly based on the inputs from experts/consultants along with robust literature review and researcher’s experience in the area of risk handling. The MCDM methods used for investigation and assessment are Fuzzy AHP and Fuzzy TOPSIS. The outcomes of the study are further validated through sensitivity analysis and real-world scenario. Results: Technical and Information Technology (IT) risks are found to be on the top of the priority list, which needs urgent attention while embarking on I4.0 adoption in the industry, and the most important criteria, which needed urgent attention was Information Security. The paper has also developed the ‘Industry 4.0 Risks Iceberg model’ and systematically categorized the challenges into 5 dimensions for easy assessment and analysis. Practical Implications: This systematic and holistic study of the I4.0 associated risks can be used to find the most critical and crucial risks based on which the strategies and policies may be modified to harness the best of I4.0. This will not only ensure the returns on investment but also will build trust in the system. The research would be very beneficial to managers, academicians, researchers, and technocrats who would be involved in I4.0 implementation. Keywords: Industry 4.0, risks management, sustainability, sensitivity analysis, fuzzy AHP, fuzzy TOPSIS, multi-criteria decision-making method (MCDM). 1. INTRODUCTION I4.0 is being discussed and deliberated since the year 2011, on every global platform directly or indirectly because of its inevitable nature, after been introduced the first time during the Hannover fair in Germany. Hence, it is imperative to understand how prepared we are to harness the best from the adoption of I4.0. The vision of I4.0 is to create an ecosystem, where machines will do the maximum of the tasks with a minimum of intervention from a human. This means the amalgamation of technologies like Big Data Analytics (BDA), Internet of Things (IoT), Industrial Internet of Things (IIoT), Artificial Intelligence (AI), Cyber-Physical System (CPS), and Augmented Reality/Virtual Reality *Address correspondence to this author at the Mechanical Engineering Department, Government Polytechnic, Gondia, Maharashtra, India; E-mail: raggpn@gmail.com 2666-2566/22 $65.00+.00 (AR/VR) is expected to lead the overall transformation. The smart industrial environment created out of the I4.0 vision is not only transforming the manufacturing and service world through continuous disruption and innovation but also challenging the traditional industrial practices which do not have much significance and relevance in the fast, flexible, dynamic and volatile industrial world. This can be referred to as just the beginning of I4.0 the 4th Industrial Revolution as it is referred to sometimes, which has the potential to address and meet all the present and future expectations [1-4]. In the last few decades, technological disruptions systematically and strategically have revolutionized the manufacturing practices to extreme levels, breaking all the age-old limits. The glimpses of these technological advancements can be experienced through a shorter product life cycle, higher industrial performance standards than ever, efficient and effective use of the resources, and the sophisticated ap© 2022 Bentham Science Publishers 112 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Gadekar et al. proach to customer care giving rise to the development of new business models which are more customer and environment-friendly [5] than ever before. tives, criteria, and understanding of critical nature and application of Multi-Criteria Decision Making Method (MCDM) relevant to the research problem considered for the study. This revolution on one side seems to have very promising and aspiring possibilities for a bright and happening future, while on the other hand, the challenges are numerous. Only continuous research and innovation will foster rapid development and nurture the ability to withstand these challenges [6]. In this paper, the researcher has discussed and analyzed the risks identified as crucial and critical for I4.0 implement ation. Firstly the researcher created an all-inclusive list of the micro and macro risks based on the literature review. The list further was optimized by the intervention of the experts in the field of I4.0 to make it precise and specific, thereby leaving the five most critical and significant risks, which were adopted for the study. These risks were meticulously analyzed and assessed conceptually by studying the specific nature and, characteristics before considering them for the next level assessment. A similar process was followed to identify the ten criteria to assess the five critical and significant risks identified above. These criteria were selected to reflect the capacity and capability of the organizations in terms of resource availability and intrinsic ability to avert the risks and adopt I4.0. The detailed information on the risks and criteria is elaborated in section 3.5 and 3.6. The experience of the large companies until the date shows that I4.0 has all the possibilities to create a high-value manufacturing system, by the right use of their huge capacity and capabilities [7]. Hence, the impact on this section of value creators and contributors to Gross Domestic Product (GDP) needs to be studied and attended. This research is a holistic attempt to develop a robust framework, which will streamline the efforts to achieve the I4.0 objectives by assessing and evaluating various risks, impacting the adoption of vision I4.0, [2, 7, 8]. This paper provides an overview of the key risks, companies may encounter while adopting I4.0 vision. The risk identification, assessment, and evaluation for any transformation and transition are most important [7]. The thorough assessment of the risks not only reduces the chance of project failures but also equips the strategists with riskmitigating plans. In order to have a sustainable implementation of I4.0, it is very much necessary to pay attention to risks like Economic, Social, Legal and Political, Ecological, Technical, and Information Technology (IT). This will help to sustain the inevitable changes caused by the transformation of the new industrial age [2, 7]. Hence, assessing and evaluating these risks in the same framework and determining the most critical risk is also essential and considered as one of the prerequisites while adopting the vision of the I4.0 transformations. The robustness and the critical nature of the literature are reflected through the quality and quantity of the references cited and referred. The researcher referred to the electronic databases like EBSCO, Web of Science, SCOPUS, and Science Direct over the 2010-2020 timeframe period, by the publishers Emerald, Springer, Taylor and Francis, and IEEE, etc., to reach out to 203 research papers, by searching the key terms like “Industry 4.0 implications”, “Industry 4.0” and “sustainability”, “Industry 4.0” and “risk management”, “Industry 4.0” and “social dimensions”, “Industry 4.0” and “readiness assessment”, “Industry 4.0” and “framework”, “Industry 4.0” and “challenges”, “Industry 4.0” and “technologies”, “Smart Manufacturing” and “Smart Factory” as a primary source of knowledge and information. The documents from non-English language magazines, editorials, and non-peer-reviewed journals were excluded. After the preliminary screening, 153 research papers were found relevant to the study. The screening criteria were based on the sources of publication, degree of relevance, the period (2010–2020), type of the study, and methods used. The majority of these papers (102) were from refereed journals and the rest (51) were from academic peer-reviewed journals, conference proceedings, and book chapters. The review of 153 papers led to fine-tuning the choice of alterna- A Hybrid MCDM approach was used to identify the most crucial risk out of five risks, by using the Fuzzy Analytical Hierarchy Process (FAHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) methods as it fully addressed the critical needs of the problem. This paper has nine sections. The first section elaborates on the importance of the study on risk management in I4.0 implementation. The rest of the paper has been organized in sections 2 to 9. Section 2 explains the ‘Industry 4.0 Risks Iceberg Model’, while section 3 is about the comprehensive literature review highlighting the different dimensions of I4.0 knowledge and applications. In section 4, the methodology is elaborated in the right contexts. Section 5 deals with testing and validating the risks assessment model by applying it to the company which is aspiring to adopt I4.0 through a systematic process of transition and transformation. Section 6 presents results and findings, section 7 presents sensitivity analysis, section 8 presents discussion, and section 9 presents conclusion. 2. INDUSTRY 4.0 RISKS ICEBERG MODEL The iceberg model is one of the simplest and most preferred research tools to graphically present the disproportionate inputs and outputs, as is evident from Fig. (1). This model also helps to logically demonstrate what is seen on the surface is an outcome, and what is generally unseen in terms of resources and efforts are the impacting factors [9]. The ‘Industry 4.0 Risks Iceberg Model’ is the outcome of a detailed survey, explorations, and exhaustive experience of the researchers in the field of digitalization and smart technologies. The set of challenges and risks are numerous. The concerns responsible for the prominent risks are placed in the downside of the iceberg and not always directly visible. Whereas the prominent risks placed on the tip of the iceberg directly affects the business decisions. The risks are visible but the concerns are not always visible [10]. Mostly the stakeholders and governments are in a confused state of mind because of the absence of global standards and lack of clarity. Even though the risks seem enormous and Risk Assessment and Implementation of Industry 4.0 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 dreadful at the first sight, with the continuous evolution of knowledge for handling I4.0 risks, the day is not far, when companies will be aggressively thinking of adopting I4.0 [11]. Economic Risks Ecological Risks Social Risks Legal and Political Risks Technical and IT Risks High level of uncertainty Dynamic demand Huge capital investment Lack of the data handling capability Human skills Cyber security Poor quality of the data available Poor research and development 113 because of its very specific area applications like Tradebased Money Laundering behavior detection which is building trust in using the I4.0 technology-based solutions [15]. Mostly the researchers have explored five research heads, i.e., I4.0 Concept, Technologies used in I4.0, I4.0 Sustainability, Human-Machine communication, and MachineMachine communication [16, 17], leaving huge scope for the current study, focused on, ‘Industry 4.0 Risks Framework’. It is interesting to note that the most adopted technologies, those changing the industrial ways of doing business, are IoT and Information and Communication Technology (ICT), because of their awareness and ease to use [18]. The paper will serve the purpose of simplifying, streamlining, and formulating the ways and means not only to create awareness and understanding but also to mitigate the I4.0 risks by applying the findings so that I4.0 adoption becomes a reality soon. Political will Unclear economic conditions 3.2. Industry 4.0 and MCDM in Indian Scenario Technological adoption Low awareness of vision of I4.0 implications Poor digital awareness Lack of global standards Lack of management support and vision Reluctance to change Technological compatibility Lack of supporting infrastructure Fig. (1). Industry 4.0 risks iceberg model. (A higher resolution / colour version of this figure is available in the electronic copy of the article). 3. LITERATURE REVIEW This literature review is an attempt to understand and critically synthesize, assess, analyze, evaluate, and apply the existing knowledge in the present study. The literature review is arranged in four sections. The first section highlights the perspective of I4.0 as a revolution in the manufacturing and production sector. While the second section is focused on the current status of I4.0 adoption in India. The third section is dedicated to exploring the risks framework for I4.0 implementation in the existing industrial setup and the fourth section is referring to the extraction of the research gap. 3.1. Industry 4.0 The majority of the systematic and scientific research happening in Engineering and Technology in recent years is paving the path for future trends and developments [12]. In that case, the use of emerging technologies applications like big data, cloud computing, and IoT in the manufacturing process are the real game-changer [13]. As a result, global businesses today are evolving at the fastest pace than ever, willingly or forcefully. The companies are moving from traditional practices to smart practices, to meet the numerous challenges like volatile demand, frequently changing business model, fierce competition and economic ups and downs, etc. [14]. The vision of I4.0 states that the technology-driven transformation should benefit people's lives than just establishing policies, strategies, frameworks, structures, and layouts. Machine learning technology is also becoming popular, The majority of the Indian industries perceive that the adoption of I4.0 vision may not be of any worth in the absence of clear, standards, and frameworks. Even though the missing link is evident, few large-scale visionary industries have made up their mind to embark on smart manufacturing practices, fully or partially as they perceive the impact otherwise. Few first movers can be seen in various sectors like Automobile, Pharmaceuticals, Information Technology, etc. who are leading the change [9] giving a big boost to specific sectors in India. In this regards, a study conducted by Kamble et al. [16] which is based on two methods namely Interpretive Structural Modeling (ISM) and Fuzzy MICMAC (Matriced’ Impacts Croise´s Multiplication Applique´e a´ un Classement) reflects true situation prevailing in India regarding the implementation of I4.0 [17]. It seems the I4.0 vision of India, ‘Make in India’ is picking up. The extensive survey conducted by Luthra et al. in the Indian manufacturing supply chain sector, during 2018, to study the main challenges for the adoption of I4.0 revealed that there are 18 main challenges. In addition to this, the researcher has explored the crucial challenges as it showed the relationship with the risks being analyzed. The developed, framework is presented in Fig. (8) section 8. Another exhaustive study conducted by Vaidya and others in 2018 [20] reported that I4.0 implementation is based on the degree of adoption of nine technologies like Business Data Analytics, System Integration, Simulation, Cybersecurity, IIoT, CPS, Cloud computing, Additive manufacturing, and AR. Indian industries must adopt Industry 4.0 technologies [21] specifically cloud computing practices as their top priority to further harness the growth of Industry 4.0 vision [22]. This substantiated significantly that the Indian manufacturing industries are seriously aspiring to surmount challenges on the way to conquer the I4.0 vision at the earliest by thoughtfully devising robust methods and methodologies [17]. Gawankar [23] has also emphasized the large scale investment in BDA to make the Indian retail sector supply chain more agile and resilient. 114 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Gadekar et al. Table 1. Issues addressed and not addressed by earlier researchers. Sr Title Refs. Issues Addressed Intervention used Issues not Addressed • Assessed following Risks • agement A fuzzy based risk evaluation 1 model for industry 4.0 transition process. Manufacturing process man- [6] • Operations methods and tools, • Machines and manufacturing Technologies, • Human sources • Machine environments • Interval Type-2 Fuzzy AHP • Hesitant Fuzzy TOPSIS, MCDM sidered izational adoption of industry 4.0 based on multi-criteria decision techniques • Sensitivity analysis not considered. • Risk as an alternative not con- An assessment model for organ2 • Sustainable Risk assessment • Criteria selected are limited • Assessed preparedness/ maturity to [24] adopt I4.0 through the self-assessment analytical model. • AHP • TOPSIS • FAHP and FTOPSIS method to deal with uncertainty and ambiguity in the decision-making process, not considered. • Eco-friendly business practices and awareness assessment is not Identifying key success factors 3 of sustainability in supply chain management for industry 4.0 using DEMATEL method [30] 4 • Developed a sustainable risk framework [2] • Provided guidelines about the risk that ---------------- may occur during the transition stage towards I4.0. • Surveyed current researches on financial Machine learning methods for systemic risk analysis in financial sectors [25] systemic risk assessment and measurement with machine learning methods. ---------------- 6 review identifying the current trends and future perspectives [16] economic sustainability and environ- dependence power of barriers to adopting industry 4.0 in the Indian manufacturing industry ---------------- mental protection as an important research aspect of I4.0 • Scope to apply MCDM tech[17] • Barriers to I4.0 Implementation identified. • ISM • Fuzzy MICMAC operations management perspective with fuzzy DEMATEL (Table 1) Contd…. niques not considered. • Comparative study of different manufacturing organizations not considered. • This study is limited to the hightech sector. It could be extended fourth industrial revolution and proposing a road map from an considered. • Validation of the framework to achieve a sustainable outcome is missing. Analyzing workforce 4.0 in the 8 Big data analysis, Policies related to data science. the proposed framework is not Analysis of the driving and 7 ed to a Data-driven approach, • An empirical study to validate work. • Identified process safety and automaton, the risks and their evaluation. • Investigating interdependencies of risks using MCDM methods. • Further research can be extend- • Developed a sustainable I4.0 frameSustainable industry 4.0 framework: A systematic literature considered. • FARE, CRITIC method, the • Quantitative information about in the context of I4.0. established manufacturers 5 contributors to the success of I4.0 • DEMATEL Best worst method also could be used. Development of a risk framework for industry 4.0 in the context of sustainability for • IoT environment are identified as key [11] • Developed a structural model to identify • Fuzzy the criteria for workforce selection in the DEMATEL I4.0 environment. to the other sectors. • CRITIC method, the Best worst method could be tried. Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Risk Assessment and Implementation of Industry 4.0 Sr Title Refs. Issues Addressed Intervention used 115 Issues not Addressed • Advance digital technologies in manufacturing companies are investigated and Evaluation of advanced digital 9 technologies in manufacturing companies: Hybrid fuzzy MCDM approach [26] their contribution in context to I4.0 is • FAHP evaluated. • PROMETHEE • Other MCDM like EDAS, MABAC could be tried. • The use of digital technologies • According to this study digitalization in in customer follow-up and inter- supply chain management contribute the action as an alternative could be considered. most to the production principles. • Key drivers of I4.0 i.e., Cybersecurity, risk management and Industry 4.0 through organiza10 11 tional interoperability perspective: a multicriteria decision analysis Aspects of risk management implementation for industry 4.0 • The case discussed the automotive sup[27] ply chain. • DEMATEL • PROMETHEE sidered. • FARE, CRITIC method, the Best worst method also could be tried. [28] • Proposed design of the framework for risk management in I4.0 implementation ---------------- • Six clustering algorithms applied to Evaluation of clustering algo- • Three MCDM methods rank the cluster[29] using MCDM methods • TOPSIS ing algorithms based on eleven perfor- • DEA mance criteria • VIKOR • Rankings obtained by three methods are • No risk evaluation • No assessment of the framework • How to deal with conflicting three financial risk data sets. 12 rithms for financial risk analysis network connectivity not con- rankings obtained by MCDM methods to find a compromise solution need to explore. • The large size of datasets can be considered for precise findings. not the same. Hence, a strong framework is a must to support the sustainable implementation of I4.0 [30]. Most of the researchers have been found researching the basic needs of the I4.0 adoption in general. Very rarely the research has addressed the specific and precise needs of the industries aspiring to adopt I4.0. Table 1 has summarized broadly the issues addressed and not addressed to date by the researchers in the present context. This table has been a key reference to lay down the foundation of this specific research. 3.3. Risks for Industry 4.0 Implementation Risks identification, assessment, and evaluation are key to any successful technology transfer and change management. If the companies are exposed to this knowledge, the adoption of I4.0 will be faster and smoother. This will also help companies to reorganize, restructure, and reorient policies to achieve the new benchmarks without any turbulence [6]. The risks in the transition phase could be Economic, Ecological, Social, Technical and IT, and Political and Legal risks [2]. 3.4. Research Gap and Highlights After conducting the detailed literature review, researchers concluded that there are very limited publications highlighting the risks related issues being faced by companies while embarking on the I4.0 vision, thereby leaving the scope for new research that will come up with an analytical model/framework to help decision-makers, who in the absence of academic research were afraid of stepping forward to form a part of the digital revolution. This research is unique as, the researcher has mitigated the earlier research limitations by involving the experts, researchers, and policymakers, in order to provide the most appropriate, feasible, optimal, and viable solution. Expert profile details may be referred to in section 5, Table 3. The criteria chosen by Colak and others [6] were further extended by adding two more crucial criteria to make the study robust and insightful, i.e., a) Flexibility and b) cost, details explained in section 3.6 in context with the Indian manufacturing sector. Birkel and others in 2019 [2] provided a model of the risks for the implementation of I4.0 but did not evaluate the priority of risks in order to help the policymakers. The present research focuses on the crucial aspects of ranking the risks to help the academician, industry practitioners, I4.0 consultants, policymakers, researchers, etc. for supporting their decision-making capability to handle I4.0 related risks successfully in their business. This research is novel as it precisely focuses on the internal and external risks management approach for I4.0 implementation. In this paper, the researcher has used the PEEST model for selecting the critical and pertinent risks as an alternative, details explained in Section 4.1. These alternatives further were ranked on the basis of selected criteria, explained in section 3.6, using the Hybrid Fuzzy method (FAHP and FTOPSIS). This approach of measuring and ranking the risks according to its critical nature and importance will help to optimize the resources to avert the negative impact of risks, effectively and efficiently. The higher the impact, the better will be the ranking in the list, indicating that the best risk should be urgently attended. Sensitivity analysis is carried 116 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 out further to check the robustness of the findings. Details are explained in section 7. 3.5. Risks Categories Considered for the Study 3.5.1. Economic Risks Economic risks comprise the fear of losing the investment if the idea does not yield the desired outcomes in terms of the set objectives [8]. Industries always are not very keen on investing in marketing research to know the market well. This has been one of the drawbacks of the traditional business models, which made our businesses susceptible to failures [2]. Assessment of economic risks will largely immunize the system of debacles and also ensure the effective usage of company resources like data management, IT infrastructure building, Artificial Intelligence (AI), Networking, CPS, Robotics, and Cloud computing [31]. This risk could be one of the most decisive and deceptive if not methodologically accounted. Investment-related to expansion, upgradation of the existing resources always attracts a huge cost [32] which may lead to an irreparable dent on the balance sheet of the company if not addressed within limits [7]. Gadekar et al. As a result, the old machines which could not be upgraded to the level of I4.0 standards may contribute to the waste management problems [33]. Radiation emitted by the mobile towers, wireless networks contribute to the adverse effects on humans, animals and bird’s health. Can we afford further harm to our treasured environment, while we are already experiencing the worst effects of the massive industrialization in the past (mainly 1st, 2nd, and 3rd industrial revolution)? We need a much-balanced approach [2]. 3.5.5. Technical and Information Technology (IT) Risks This is one of the biggest challenges most of the SME and even large-scale industries are afraid of [32]. Companies will have to change from the traditional success model to a more complex and challenging business model, which will be all-time exposed to threats like, Cyber Attack, Cyber Security, Data theft, Data loss, Data Privacy, Technology disruption, etc. [32]. Hence, this risk is also one of the most important to be evaluated [2, 13]. Fig. (2) is the logical presentation of the risks framework for a sustainable change of I4.0. The sub risks shown in the diagram give the microlevel understanding of the possible impact on the last entity in the chain. 3.5.2. Social Risks Most of the governments globally are struggling when it comes to the rate of unemployment and social balancing among the nationals [32]. The change is always perceived to harm many and benefit a few unless people are made aware of the consequences in an organized manner [8]. As the I4.0 phenomenon is spreading all over the world, the fear of losing the job is rising. This is not just limited to the low skilled jobs, as they may be easily automated, but the repetitive jobs like planning, decision making also may be handed over to AR/VR, Cloud computing, IoT, CPS, and Machine Learning (ML) systems, to make them more productive, effective and compliant to I4.0 vision [11, 31]. This has already built-up the stress in the companies as well as the societies, leading to the uncertainty on the adoption of the I4.0 vision [2, 7]. 3.6. Criteria Considered for the Study 1) Information Security (C1): It consists of privacy and limited access to information, which is an important concern. It is expected that the confidential information access should be given to only authorized and concerned employees based on the need, use, and value of the information. 2) Integrity (C2): It means maintaining significant reliability and validity of the resources and transaction practices when it comes to the exchange of data and other valuable resources that may impact the functioning of the business. 3) Availability (C3): It means the required information, resources should be available for the authorized person as and when needed in ready to use manner by putting in minimum efforts. 4) Quality (C4): It can be defined as the ability to meet expectations of the I4.0 compliant system. The efficient quality of information system, connectivity, collaboration, and integration among the peripherals of I4.0 is key to the successful implementation of the I4.0 system. 5) Performance (C5): It is an important criterion that will help to measure and define the gaps between present and desired systems when the I4.0 is implemented. It is the function of the productivity of people and other resources in an organization. It is also the ability of the existing infrastructure to become self-organized and I4.0 compliant. 6) Design (C6): The design of the product, workplace layout, policies, strategies, and organizational hierarchy is also a crucial factor for handling risks related to I4.0 implementation in companies [34]. 3.5.3. Legal and Political Risks Political support to the overall vision and mission of I4.0 will positively influence the propagation of the idea faster and wider. This will also help to develop favorable infrastructure to make the local companies more competitive in local as well as the global environment [31]. Before we take a plunge into the smart world of manufacturing, we must deliberate on the creation of guidelines for data protection, cybersecurity, work environment, and creation of a legal framework to resolve any issues arising as a part of smart business. Otherwise, this will give rise to major concerns and risks issues in the coming times [2]. 3.5.4. Ecological Risks It is very evident from the existing knowledge, that the data centers will need an enormous amount of energy to remain alive and functional all the time [8]. This means the rate of energy production must be increased. At the same time, companies will have to sideline the old technology and machines, which might be inefficient and unproductive [7]. Risk Assessment and Implementation of Industry 4.0 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Economic Risks A1 117 • Massive Capital Investment • Volume and time of investment • Shift in ways of doing business • Extreme competition from all the players in the market • External Support • Fear of losing the job • Drastic changes in organizational structure and leadership reliability. • Internal resistance to change • Up skilling of existing employees as a part of retention policy. • Business related stress and uncertainty • Lack of skilled manpower Social Risks A2 Legal and Political Risks A3 Ecological Risks A4 • Policies related to infrastructural support in terms of energy, cyber security,networking etc. • Laws related to enabling technologies of I4.0 and other resources. • Further deterioration in the quality of environment • Disposal of electronics waste • Increase in pollution • Technology Transfer • Setting the standards • Susceptible to cyber attack • Data Protection • Data Privacy • Third party data handling • Third party collaboration Technical and IT Risks A5 Fig. (2). Mind map for risks framework of sustainable I4.0. (A higher resolution / colour version of this figure is available in the electronic copy of the article). 7) 8) Infrastructure (C7): It signifies the electronic hardware and software which give rise to the seamless integration between machine-human and machine-machine networks all over the company. The strength of the infrastructure could be defined by speed, reliability, realtime data collection, decision making, and capability to provide efficient and effective solutions to the endusers. Interoperability (C8): It is the measure of the technical compliance and feasibility of the devices when they are connected to other devices. The devices which perform at the same level of efficiency, when connected with different devices are said to be interoperable. Such de- vices make a positive impact on overall system performance. 9) Flexibility (C9): I4.0 offers more attention to discovering the methods for improving the flexibility of production processes and trying to convert a fixed production line system into a self-configurated assembly line [35, 36, 37]. 10) Cost (C10): It comprises the capital investment associated with hardware like sensors, networking, servers interface, and the software needed to set up the communication system. The software could be application software, operating system, networking software and 118 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 • • • • Degree of IT security Data security Data privacy Verification, processing and validation of data collected from various resources Gadekar et al. • Accuracy and completeness of information. • Standerdised processing methods • Data integrity • Trustworthiness of data or resources • Resources for I4.0 complient system • Information to the concerned person as and when required. • Network connectivity • • • • Information Security Integrity Availability Quality • Integration and connectivity • Machine and IoT devices • CPS,BDA,AI,ML etc. Functioning. • Product • Process • Business model complient to I4.0 • • • • • Communication mechanism • Interconnectivity between the system • Ability of systems to connect and work in a coordinated and self-sufficient manner. • Integration of the classical systems with the modern models. Performance Design Infrastructure • Response of process ,machine etc. to the envirnment change of the system • Response to the disturbances and failures • Capabity to accommodate last minut changes, • Responsiveness to changing customer demand • • • • • • Flexibility Cost Equipment, Machines Connectivity and networking Self organising IoT and CPS devices. Data acquisition Data processing Infrastructure Networking and connectivity Interoperability Operation Transition Maintinance Technical Support Training Development of Infrastructure Fig. (3). Criteria and sub criteria. other special software to run the required application [20, 36-39]. The company must invest to design and develop a sound capacity to make the machine to machine and machine to human interaction possible [40]. Fig. (3) shows the ten criteria and sub-criteria used in the research to evaluate the five risks identified as crucial to Industry 4.0 adoption. 4. METHODOLOGY This research is carried out in a company, that is very keen to imbibe I4.0 practices and trends from the Automobile sector in India. The details regarding the company and the focus group profile are explained for more clarity in section 5. The researcher’s objective is to define, synthesize, assess, evaluate, and analyze the risks the company may be prone to, on the journey to become I4.0 compliant. The research also assessed the risks handling capacity and capability of the company. Fig. (4) is the systematic representation of the steps followed in the research. 4.1. PEEST Model for Industry 4.0 Risks Identification Every industrial success is prone to external and internal environmental conditions. PEEST model framework has been frequently used to analyze macro-environment elements, i.e., Political, Economic, Ecological, Social, and Technical. Past research and experience have demonstrated that success and failure depend on the ability of a company to sense the opportunity or threats before they make an impact [41]. The PEEST model is a powerful tool being used extensively by companies to keep track of macro-environment forces [42]. Fig. (5) shows the elements of the PEEST model in the current context. This model has a dual advantage, firstly it helps to understand the external environment, and secondly, allows analyzing the ability of the organization to handle the risks at its best. This model application is crucial in reaching decisions like business development, diversification, warehouse selection, and implementation of long-term strategies in business [10]. The accuracy of the PEEST model is found to increase by involving experts and working professionals Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Risk Assessment and Implementation of Industry 4.0 119 Preparatory Phase Knowledge Hub of Industry 4.0 D1 D2 D3 Literature Review for Risks Assessment for Industry 4.0 Implementation Finalization of Alternatives Finalization of Criteria Framework Preparation for Decision Hirarchy N Approve Decision Hierarchy Y Fuzzy AHP Phase Construction of Pairwise Comparison Matrix Y using FAHP (Step 1) Computation of Criteria Weights Using Geometric Mean Method (Step 2 & 3) Check Consistency Ratio (step 4 & 5) Consistency Ration < 0.1 (Step 5) N Y Assigning Ratio to the Alternatives w.r.t. criteria (Step 6) Fuzzy TOPSIS Phase Construct Aggregate Fuzzy Decision Matrix (Step 7) Construct Normalized Fuzzy Decision Matrix (Step 8) Construct Weighted Normalized Fuzzy Decision Matrix (Step 9) Calculate the distance of each alternative from FPIS and FNIS (Step 10 & 11) Compute the closeness coefficient ratio (Step 12) Analysis Phase Assigning ranking to the alternatives (Step 13) Sensitivity Analysis and its Interpretation Discussion and Conclusion Fig. (4). Research methodology framework. in the field, which has been well adapted in the current study. 4.2. Fuzzy Multi-Criteria Decision-Making Approach (MCDM) The real-world decision-making problems are very complex in nature. Hence, MCDM methods are best suited to address these problems where situations are subjected to some conflicting, ambiguous and confusing objectives which are not precisely known to the decision-maker. As a result, the MCDM methods are the first choice of researchers and scientists to solve such complex and complicated problems. The MCDM became smarter by the fusion with Fuzzy set theory to form a Fuzzy Multi-Criterion Decision-Making Method (FMCDM). The specialty of the method is the ability to deal with the situation where insufficient and uncertain knowledge and information is inevitable. 4.3. Fuzzy MCDM Problem Formulation Decision matrix for the MCDM problem with m alternatives {A1, A2 ,… ,Am} which is assessed by applying n criteria {C1, C2… Cn} to those alternatives can be represented as follows: 120 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Gadekar et al. Legal and Political Risks Technical and IT Risks Economical Risks I4.0 Implementation Risks Ecological Risks Social Risks Fig. (5). PEEST Model for Industry 4. 0 Implementation Risks. (A higher resolution / colour version of this figure is available in the electronic copy of the article). Table 2. Linguistic scales and corresponding triangular fuzzy number. The Fuzzy Scale of Relative Importance for Pairwise Comparison Matrix for Criteria Linguistic Scale for Ratings to Alternative and Criteria Term Crisp Number Fuzzy number Term Equivalent Fuzzy Number Equal (E) 1 1,1,1 Very Low (VL) 1,1,3 Moderate(M) 3 2,3,4 Low (L) 1,3,5 Strong(S) 5 4,5,6 Average(A) 3,5,7 Very Strong (VS) 7 6,7,8 High (H) 5,7,9 Extremely Strong (ES) 9 9,9,9 Very High (VH) 7,9,9 Intermediate values (IV) 2,4,6,8 (1,2,3),(3,4,5),(5,6,7), (7,8,9)




















































 th (1) Where the value of the i alternative with respect to j criterion is denoted by xij . 4.4.1. The Steps Followed for FAHP by Geometric Mean [48-50] Step 1: Construct a fuzzy pairwise comparison matrix. th A fuzzy scale of relative importance for the pairwise comparison matrix along with the equivalent fuzzy numbers to the crisp values is expressed in Table 2. 4.4. Fuzzy AHP Satty [43] introduced the first AHP approach which is mostly used to determine criteria weights [44, 45]. In reallife situations, decision-makers express their judgment in linguistic form. AHP uses a crisp number, which is insufficient and imperfect due to uncertainty and vagueness of the judgment of decision-makers. To overcome this drawback of AHP, fuzzy logic is introduced into a pairwise comparison of the AHP process proposed by Buckley [46] and Nădăban [47]. Using Table 2, the linguistic term assigned by each decision-maker to the pairwise comparison between criteria is converted into equivalent triangular fuzzy numbers Step 2: The geometric mean of fuzzy comparison values of each criterion is calculated [48-50]. Step 3: The fuzzy weight of each criterion is calculated. Step 4: Obtained fuzzy weights are fuzzy numbers that are to be defuzzified into crisp numbers to check consistency ratio through various defuzzification methods i.e. Linguistic method, Centroid Method (COA Method), Graded Mean Integration Representation (GMIR) method, Median or Signed-distance or Area of compensation method. Out of all of them, the Center of Area method is mostly the preferred method. Step 5: Consistency check should be done for the preference of weights of the criteria allotted by the decisionmakers to prove the validation of weight allotment. The con- Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Risk Assessment and Implementation of Industry 4.0 sistency ratio (CR) should be less than 0.1 [2, 19]. Once the consistency check is performed on the weights obtained by FAHP, and it is less than 0.1, then the weights obtained are considered valid. 4.5. Fuzzy TOPSIS Weights calculated by the FAHP method are used as input for further calculations by FTOPSIS Method [52]. TOPSIS was first developed by C.L. Hwang [53]. It is based on the principle to select the alternative which has the shortest distance from Positive Ideal Solution (PIS) and farthest distance from Negative Ideal Solution. (NIS), i.e., maximizes the benefit criteria and minimizes cost criteria [55]. 121 Chen [11] proposed a vertex method to calculate the distance between two triangular FNs. If

























are two triangular fuzzy numbers, then the distance between them is calculated by Eqs. 8 and 9.






Let,









































,















(8) (9) and be the distance from each alternative Ai Where to the FPIS, and to the FNIS, respectively.





Step 12: For each alternative Ai,, CCi computes the Closeness Coefficient as follows: FTOPSIS technique uses linguistic variables to evaluate the ratings of criteria and alternatives which is very convenient for decision-makers to express their judgments. Using Fuzzy TOPSIS, Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) are calculated as per the procedure described below. Step 13: Ranking of alternatives done with the highest closeness coefficient as the best alternative. 4.5.1. The Procedure of FTOPSIS 5. CASE STUDY Step 6: Ratings to the alternatives and criteria and corresponding fuzzy scale are assigned as presented in Table 2. The company chosen for the study to test the developed analytical hybrid fuzzy framework (Fig. 4) is one of the highly successful, large scale companies in Maharashtra, India. The company was established in the pre-independence era and has gone through many ups and downs. It is one of the largest automobile manufacturing companies in India. The company decided to embrace the I4.0 vision to sustain itself in a globally competitive market. The volatile demand in the dynamic market conditions has made the business highly unpredictable and sensitive to global dynamics. Hence, the company decided to adopt vision I4.0 to meet the challenges of the new era by embarking on digitalization and smart practices in manufacturing. Automobile assembly, Engine shop, Transmission shop, Hydraulic shop, and Machine shop are the important modules of the company. All these modules are simultaneously working on making the company I4.0 compliant by implementing the emerging new technologies of I4.0 i.e. IIoT, CPS, Cloud computing, AI, ML, AR/VR, 3D printing, Robotics, Digitalization of various manufacturing activities, data acquisition, and business analytics. Step 7: Calculate the aggregated fuzzy ratings for alternatives and the aggregated fuzzy weights for criteria. The aggregated fuzzy rating












of ith alternative w.r.t. the jth criterion is calculated by following formulae.































(2) Step 8: Normalized fuzzy decision matrix calculated by following formulae. The normalized fuzzy decision matrix is



 . Where,








a


















(3)











































(4)
Step 9: The weighted normalized fuzzy decision matrix is calculated by the following formulae





, where







(5) Step 10: The Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) is calculated as follows: FPIS =






















where







(6) FNIS=






















where







(7)

Step 11: The distance from each alternative to the FPIS and to the FNIS is computed using the formulae.













(10) The selection of the criteria for the evaluation of specific alternatives was an outcome of the rigorous and laborious process which included expert interviews (Delphi Method) and discussions along with industry visits and other means of data collection. The researcher prepared a list of 33 experts, who had the required skillset and qualification to be on the expert’s panel. Based on the level of expertise, the projects and consultancies delivered by these experts, the three most suitable were selected on the panel of experts for the discussion and other valuable inputs. The expert’s contribution and involvement in fostering the vision of I4.0, as a changemaker in respective areas of expertise have been key to guide the research. These experts have studied I4.0 in-depth and accumulated a wealth of Industrial experiences and Managerial expertise and are very keen on implementing I4.0 in their respective departments of the company. The expert’s competency mapping is detailed in Table 3. 122 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Gadekar et al. Table 3. Competency mapping of experts. Traits D1 D2 D3 Gender Male Female Male Age 43 41 39 Education Doctorate Doctorate Master’s Degree Experience 22 17 16 Industry Manufacturing Production Manufacturing Yes Yes Yes Researcher ----- Yes Yes Consultant Yes Yes Yes Global Exposure Yes Yes Yes Knowledge IIoT, BDA, CPS, AI, ML, Cloud Computing, Cybersecurity and AR/VR etc. Even though the organization is keen to go for the implementation of I4.0, the uncertainties and lack of risk evaluation are found to be a big hurdle. As a result, the company’s leadership is scared of the risks issues related to the sustainable implementation of I4.0. This has attracted the attention of researchers; hence the prime focus of the research is defined as synthesize, assess, evaluate, and analyze the risks related to I4.0 implementation. Many researchers have used different MCDM techniques to analyze different facets of I4.0. However, very limited research has addressed the fuzzy aspect of decision-making for risk ranking of I4.0. Hence, very thoughtfully, the choice is made to select FMCDM, i.e., a hybrid approach, FAHP, and FTOPSIS method for further analysis, to address the fuzzy nature of the real-life situation. This method is already discussed and explained in detail in section 4. The fuzzy aspect of investigation and analysis in the current situation makes the research different. Here, the focus is on risk assessment and evaluation while embracing the vision of I4.0. Fig. (6) shows the hierarchy of decision making with criteria and alternatives [43]. To begin with, 35 criteria and 14 alternatives were listed as prospective parameters to cover the I4.0 risks ambit well. The final structure evolved after the series of discussions and deliberations between experts and extensive literature review, where five alternatives and ten criteria (Design, Infrastructure and Cost referred to as non-beneficial criteria and rest are referred to as beneficial criteria, details in section 3.6) were accepted for the study. Criteria weights are calcu- lated by FAHP with the Geometric mean approach, which is explained in section 4.4.1., is used for assigning the weights to the selected criteria. Further pairwise comparison matrix between the criteria is constructed by another round of exhaustive discussion with the experts. They evaluated the criteria based on the scale provided in Table 2. After this, the fuzzified pairwise comparison matrix for criteria is formulated, by decision-makers. The outcome of the Fuzzy AHP process through Geometric Mean is described in steps 2 and 3 of section 4.4.1. Weights of each criterion are defuzzified using step 4 in a crisp number and then consistency check is carried out as mentioned in step 5, shown in Table 4. If any inconsistent evaluation was observed, then the data was reverted to the experts until the consistency ratio obtained was less than 0.1. The consistency ratio obtained was 0.02 which was much lesser than 0.1. It gave the researcher confidence that there was no inconsistency in the judgment of the experts in a pairwise comparison of criteria. After obtaining the criteria weights, the FTOPSIS method was initiated. Experts were consulted again to score the alternatives according to the criteria. Linguistic expressions for alternative and criteria ratings were converted into triangular fuzzy numbers according to the scale presented in Table 2. This was followed by three decision-makers evaluations, aggregated as described in steps 6 and 7 shown in Table 5. Followed by this normalized and weighted normalized fuzzy decision matrix was constructed using steps 8 and 9 as shown in Table 6. The Fuzzy Positive Ideal Solution (FPIS), i.e., select the alternative having the shortest distance from Fuzzy Positive Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Risk Assessment and Implementation of Industry 4.0 123 Information Security Risks Evaluation of Industry 4.0 Implementation While Transition to Industry 4.0 Integrity Availability Economic Risks Quality Social Risks Performance Design Legal and Political Risks Infrastructure Ecological Risks Interoperability Technical and IT Risks Flexibility Cost Criteria Alternatives Fig. (6). Hierarchy of decision making with criteria and alternative. (A higher resolution / colour version of this figure is available in the electronic copy of the article). Table 4. Weights of criteria in triangular fuzzy numbers obtained by FAHP by geometric mean method. List of Criteria Fuzzy Weights List of Criteria Fuzzy Weights Information Security C1 (0.309,0.298,0.270) Design C6 (0.051,0.047,0.045) Integrity C2 (0.120,0.132,0.135) Infrastructure C7 (0.0372,0.037,0.038) Availability C3 (0.106,0.115,0.124) Interoperability C8 (0.049,0.047,0.048 Quality C4 (0.113,0.113,0.121) Flexibility C9 (0.021,0.019,0.025) Performance C5 (0.099,0.099,0.105) Cost C10 (0.093,0.093,0.088) Table 5. Decision matrix by experts in linguistic term for rating criteria and alternatives. Criteria Alternatives C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 D1 H H L VH L H A L A VH D2 H A L A A VH L VH H H D3 A H A L A H A L A H D1 H A VL VH A A L H H H Experts A1 A2 (Table 5) Contd…. 124 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Gadekar et al. Criteria Alternatives C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 D2 A L H H L A VL VH H A D3 A A L H H A L H L L D1 L L H L A A A A H H D2 A A A H A L H L L VH D3 H A A A H A A A H A D1 L A H A A VH VL L L H D2 VH VL L L VH A H A A A D3 L VH VL L L VH VL L L H D1 A VH H A H A VH VH A H D2 A VH VH A A VH H A A VH D3 L A VH VH A A VH VH A VH Experts A3 A4 A5 Table 6. Normalized fuzzy decision matrix. Fuzzy wts. Wj 0.31 0.3 0.27 0.12 0.13 0.14 0.11 0.12 0.13 0.1 0.1 0.11 ----- 0.04 0.04 0.038 0.05 0.05 0.05 0.02 0.02 0.03 0.09 0.09 0.08 Alternatives C1 C2 C3 C4 C7 /Criteria C8 C9 C10 ----- A1 0.52 0.72 A2 0.44 0.68 0.92 0.28 0.52 0.76 0.26 0.41 0.63 0.74 A3 0.36 0.6 0.84 0.28 0.52 0.76 0.41 0.63 0.85 0.39 0.65 0.91 ----- 0.27 0.18 0.13 0.28 0.52 0.76 0.41 0.63 0.85 0.6 0.43 0.36 A4 0.36 0.6 0.76 0.36 0.52 0.76 0.26 0.41 0.63 0.22 0.48 0.74 ----- 0.43 0.33 0.2 A5 0.28 0.52 0.76 0.68 0.92 Table 7. 1 0.6 0.84 1 1 0.19 0.41 0.63 0.48 0.74 0.91 ----- 0.43 0.23 0.158 0.28 0.52 0.76 0.41 0.63 0.85 0.53 0.39 0.33 0.7 0.93 1 1 0.57 0.83 1.17 ----- 1 1 0.33 0.2 0.6 0.84 1.08 0.56 0.78 1 1 0.6 0.42 0.2 0.44 0.68 0.19 0.41 0.63 0.69 0.47 0.36 ----- 0.16 0.12 0.111 0.68 0.92 1 0.33 0.56 0.78 0.47 0.36 0.33 Ranking of alternatives. Alternatives










Rank


Economic Risks A1, 0.591010643 2 Social Risks A2, 0.42116946 3 Legal and Political Risks A3 0.395479765 4 Ecological Risks A4, 0.26392737 5 Technical and IT Risks A5 0.694600356 1 Ideal Solution and Fuzzy Negative Ideal Solution (FNIS), i.e., select the alternative having farthest distance from Fuzzy Negative Ideal Solution were found using step10 and 11. The closeness Coefficient ratio (CCi) for each alternative was calculated using step 12 and shown in Table 7. 6. RESULTS AND FINDINGS The systematic calculations carried out as above clearly state that the five risks (alternatives) identified as the potential threat to the way towards the adoption of I4.0 by SMEs and Large Scale industries have complete relevance and context to the ground reality. Risk Assessment and Implementation of Industry 4.0 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 The research assessed the five risks namely Economic Risks, Social Risks, Legal and Political Risks, Ecological Risks, and Technical and IT Risks on ten crucial criteria such as Information Security, Integrity, Availability, Quality, Performance, Design, Infrastructure, Interoperability, Flexibility and Cost. The expert's engagement was crucial as the primary data was collected from all of them which later was fed to the MCDM techniques to test the model. The extensive literature review was a big help to customize and orient the problem to accommodate the key aspects of risk management in the study. The Technical and IT-related risks made it to the top of the list, seeking the highest attention of the researcher, experts and company personnel, followed by Economic risks. IT infrastructure and technology are considered to be the driver of the I4.0 transition phase. At the same time, Economic risk should not be neglected as it impacts overall business budgeting and resource management. The company must be calculative in achieving the right trade-offs and investment policies to maintain high employee productivity and business operations efficiency. The sensitivity analysis is carried out as the last step to check the robustness of the result obtained. The findings of the sensitivity analysis are elaborated in Fig. (7). 7. SENSITIVITY ANALYSIS Sensitivity analysis is an instrument to check the robustness and sustainable nature of the derived solution in a dynamic environment. The quality and acceptability of the decisions can be established by carrying out the sensitivity analysis. This assures the reliability and stability of the obtained solution. This also helps to calculate the probable impact of minor or major changes in weights of criteria on the overall decision-making system under consideration [55, 56]. In this study, the sensitivity analysis is applied to the final results of FAHP and FTOPSIS. The main aim of this analysis was to confirm the consistency of the ranking obtained by the FTOPSIS method. In total, 45 combinations were analyzed by interchanging the weights between two criteria out of the total ten criteria (2C10) in every iteration [57, 58]. Details are given in Table 8. The weights of the criteria were interchanged in order to assess the impact on the final decision. All 45 combinations of outcomes were critically assessed and analyzed by calculating the Closeness Coefficient ratio (CCi ) using steps 9 to 12 repetitively as mentioned in section 4.5.1 and shown in Table 8. It is evident from the Table 8 and the sensitivity radar diagram Fig. 7 that the base ranking is unchanged for the top rank and bottom rank alternatives through all the iterations. The alternatives A5 and A1 have held the first and second positions, respectively, consistently on the higher side of the ranking, and A4 has been stable on rank 5. Whereas the alternatives A2 and A3 have been exchanging positions 3 and 4 regularly, thereby highlighting their sensitive nature to the change of weights. 125 Key Observations of Sensitivity Analysis: 1) Base ranking of the alternatives using FAHP and FTOPSIS method is A5>A1>A2>A3> A4. The topmost alternative is A5, i.e., Technical and IT Risks. 2) From the Table 8, it is evident that the A5 obtained the topmost rank (1st position), 45 times on all the 45 iterations, i.e., 100% throughout the sensitivity analysis. It reveals the ideas that the experts are persistent, consistent, and insistent in their cognitive process. 3) A1 obtained the 2nd rank, 42 times out of 45 observations, i.e., 93% throughout the sensitivity analysis. The alternative A1 pushed to the 3rd position only 3 times. 4) The alternative A2 remained at the same 3rd position 31 times out of 45 observations, i.e., 68% and was pushed to 2nd positions 2 times and 4th position 12 times. 5) Alternative A3 held the 4th position 32 times out of 45 observations, i.e., 71%. A3 was pushed to 3rd position 11 times, 2nd and 5th position once each. 6) Alternative A4 held the 5th position 43 times out of 45 observations, i.e., 95% and pushed to 3rd and 4th position once each. The radar diagram shown in Fig. (7) is the graphical presentation of Table 8 for quick assimilation of the sensitivity of the alternative to the change of weights of the criteria. The paths for A5, A1, and A4 are consistently maintaining the circular shape without interfering much with the paths of A2 and A3, whose paths are entangled, thereby showing the sensitive nature of the alternatives. It can be concluded from Table 8 and Fig. (7) that the best rank alternative A5 (Technical and IT Risks) is superior and the ranking suggested is confirmed and found to be credible. 8. DISCUSSION The researchers found during the literature review that the risks being studied and analyzed have a close relationship with the challenges of I4.0. Hence, it is imperative to study the challenges and threats simultaneously with risks assessment and analysis. The researchers identified different challenges either in isolation or in association with the different situations that the I4.0 strategists must be aware of while making key decisions. This research paper further classified these challenges into five categories for systematic assessment and analysis. This research paper has distributed the challenges depending upon nature and relevance in five different categories. This categorization is based on Technology, Culture, Legal, Capital, and Leadership. Fig. 8 presents the classified view of the pertinent challenges summarized and divided into five sectors mainly. C3 C4 C5 Closeness Coefficient Ratio C6 C7 C8 C9 C10 CC1 CC2 CC3 CC4 CC5 Alternative ranking Case1 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.591 0.421 0.395 0.264 0.695 A5>A1>A2>A3>A4 Case2 0.122 0.133 0.137 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.584 0.323 0.335 0.240 0.822 A5>A1>A3>A2>A4 Case4 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.315 0.301 0.272 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.527 0.549 0.394 0.203 0.748 A5>A2>A1>A3>A4 Case5 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.315 0.301 0.272 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.486 0.409 0.471 0.328 0.803 A5>A1>A3>A2>A4 Case6 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.315 0.301 0.272 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.668 0.350 0.294 0.411 0.724 A5>A1>A4>A2>A3 Case7 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.315 0.301 0.272 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.590 0.265 0.550 0.370 0.851 A5>A1>A3>A4>A2 Case8 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.315 0.301 0.272 0.022 0.019 0.026 0.095 0.094 0.089 0.446 0.524 0.340 0.179 0.820 A5>A1>A3>A4>A2 Case9 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.315 0.301 0.272 0.095 0.094 0.089 0.580 0.540 0.468 0.191 0.672 A5>A1>A2>A3>A4 Case10 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.315 0.301 0.272 0.638 0.339 0.497 0.304 0.793 A5>A1>A3>A4>A2 Case11 0.315 0.301 0.272 0.107 0.116 0.125 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.576 0.421 0.403 0.263 0.696 A5>A1>A2>A3>A4 Case12 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.122 0.133 0.137 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.592 0.453 0.399 0.260 0.684 A5>A1>A2>A3>A4 Case13 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.122 0.133 0.137 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.588 0.432 0.410 0.278 0.690 A5>A1>A2>A3>A4 Case14 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.122 0.133 0.137 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.642 0.413 0.356 0.302 0.669 A5>A1>A2>A3>A4 Case15 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.122 0.133 0.137 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.615 0.380 0.444 0.290 0.716 A5>A1>A3>A2>A4 Case16 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.122 0.133 0.137 0.022 0.019 0.026 0.095 0.094 0.089 0.565 0.470 0.376 0.228 0.707 A5>A1>A2>A3>A4 Case17 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.122 0.133 0.137 0.095 0.094 0.089 0.624 0.474 0.429 0.234 0.643 A5>A1>A2>A3>A4 Case18 0.315 0.301 0.272 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.122 0.133 0.137 0.633 0.439 0.432 0.253 0.664 A5>A1>A2>A3>A4 Case19 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.598 0.435 0.394 0.263 0.690 A5>A1>A2>A3>A4 Case20 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.107 0.116 0.125 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.596 0.424 0.399 0.271 0.693 A5>A1>A2>A3>A4 Case21 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.107 0.116 0.125 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.639 0.410 0.356 0.290 0.676 A5>A1>A2>A3>A4 Case22 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.107 0.116 0.125 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.617 0.383 0.429 0.280 0.715 A5>A1>A3>A2>A4 Case23 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.107 0.116 0.125 0.022 0.019 0.026 0.095 0.094 0.089 0.576 0.456 0.374 0.231 0.707 A5>A1>A2>A3>A4 Case24 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.107 0.116 0.125 0.095 0.094 0.089 0.627 0.459 0.420 0.236 0.652 A5>A1>A2>A3>A4 Case25 0.315 0.301 0.272 0.122 0.133 0.137 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.107 0.116 0.125 0.632 0.439 0.420 0.246 0.664 A5>A1>A2>A3>A4 Details of sensitivity analysis. Case3 0.122 0.133 0.137 0.107 0.116 0.125 0.315 0.301 0.272 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.410 0.326 0.420 0.226 0.832 A5>A3>A1>A2>A4 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 C2 126 Criteria Weights C1 Table 8. Cases Case26 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.591 0.419 0.397 0.266 0.696 A5>A1>A2>A3>A4 Case27 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.100 0.104 0.114 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.628 0.406 0.360 0.282 0.681 A5>A1>A2>A3>A4 Case28 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.100 0.104 0.114 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.608 0.382 0.425 0.274 0.716 A5>A1>A3>A2>A4 Case29 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.100 0.104 0.114 0.022 0.019 0.026 0.095 0.094 0.089 0.574 0.445 0.378 0.231 0.709 A5>A1>A2>A3>A4 Case30 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.100 0.104 0.114 0.095 0.094 0.089 0.619 0.447 0.419 0.236 0.660 A5>A1>A2>A3>A4 Case31 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.100 0.104 0.114 0.621 0.437 0.418 0.241 0.666 A5>A1>A2>A3>A4 Case32 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.627 0.409 0.360 0.281 0.681 A5>A1>A2>A3>A4 Case33 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.100 0.101 0.106 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.607 0.385 0.425 0.273 0.715 A5>A1>A3>A2>A4 Case34 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.100 0.101 0.106 0.022 0.019 0.026 0.095 0.094 0.089 0.574 0.445 0.378 0.231 0.708 A5>A1>A2>A3>A4 Case35 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.100 0.101 0.106 0.095 0.094 0.089 0.618 0.448 0.418 0.236 0.661 A5>A1>A2>A3>A4 Case36 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.100 0.101 0.106 0.620 0.440 0.418 0.240 0.665 A5>A1>A2>A3>A4 Case37 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.587 0.416 0.410 0.263 0.702 A5>A1>A2>A3>A4 Case38 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.052 0.047 0.045 0.022 0.019 0.026 0.095 0.094 0.089 0.587 0.416 0.410 0.262 0.702 A5>A1>A2>A3>A4 Case39 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.052 0.047 0.045 0.095 0.094 0.089 0.602 0.417 0.424 0.264 0.685 A5>A1>A3>A2>A4 Case40 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.038 0.037 0.038 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.052 0.047 0.045 0.589 0.463 0.418 0.239 0.658 A5>A1>A2>A3>A4 Case41 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.038 0.037 0.038 0.095 0.094 0.089 0.608 0.407 0.416 0.276 0.687 A5>A1>A3>A2>A4 Case43 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.022 0.019 0.026 0.051 0.047 0.048 0.095 0.094 0.089 0.606 0.422 0.409 0.266 0.678 A5>A1>A2>A3>A4 Case44 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.022 0.019 0.026 0.095 0.094 0.089 0.051 0.047 0.048 0.594 0.468 0.403 0.240 0.650 A5>A1>A2>A3>A4 Case45 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.038 0.037 0.038 0.051 0.047 0.048 0.095 0.094 0.089 0.022 0.019 0.026 0.570 0.495 0.385 0.223 0.650 A5>A1>A2>A3>A4 Gadekar et al. Case42 0.315 0.301 0.272 0.122 0.133 0.137 0.107 0.116 0.125 0.100 0.104 0.114 0.100 0.101 0.106 0.052 0.047 0.045 0.051 0.047 0.048 0.022 0.019 0.026 0.095 0.094 0.089 0.038 0.037 0.038 0.591 0.465 0.408 0.244 0.652 A5>A1>A2>A3>A4 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 Risk Assessment and Implementation of Industry 4.0 Sensitivity Analysis CCi A1 CCi A2 CCiA3 Case1 Case45 Case44 0.9 Case43 0.8 Case42 Case41 CCi A4 Case2 Case3 CCi A5 Case4 Case5 Case6 0.7 Case40 Case7 0.6 Case39 Case8 0.5 Case38 Case9 0.4 Case37 Case10 0.3 0.2 Case36 Case11 0.1 Case35 Case12 0 Case13 Case34 Case14 Case33 Case15 Case32 Case16 Case31 Case17 Case30 Case29 Case18 Case28 Case27 Case26 Case25Case24 Case19 Case20 Case21 Case23Case22 Fig. (7). Result of sensitivity analysis. (A higher resolution / colour version of this figure is available in the electronic copy of the article). Fig. (8). Crucial challenges of I4.0. (A higher resolution / colour version of this figure is available in the electronic copy of the article). 127 128 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 The structured approach taken at every step during the research has led the researchers to firmly put forward the finding to the company for discussion and implementation. The critical literature review and the appropriate choice of the methods backed by the expert's inputs have been the base of the overall research. The study considered 5 alternatives and 10 criteria to lay down the structure of the model development process. The researcher carried out a number of iterations before reaching the conclusion, thereby leaving no chance of ambiguity. The findings were further validated and confirmed by experts, and company management. The results for potential implementation are very commendable and optimistic as they have developed through a solid conceptual framework and based on real-world experiences. The concept is in the company strategy framework by now. Gadekar et al. LIST OF ABBREVIATIONS AHP = Analytical Hierarchy Process AI = Artificial Intelligence AR = Augmented Reality BDA = Big Data Analytics CPS = Cyber-Physical System DEA = Data envelopment analysis DEMATEL = Decision making trial and evaluation laboratory FAHP = Fuzzy Analytical Hierarchy Process FTOPSIS = Fuzzy Technique for Order of Preference by Similarity to Ideal Solution The research paper is based on a critical literature review supported by 153 research papers and 3 expert consultations. The key findings like I4.0 risks iceberg model and the systematic categorization of the challenges have added extra value to the paper. The I4.0 enormous potential is highlighted continuously to demonstrate the immense opportunities the companies may harness by adopting I4.0 technologies at the earliest. The research also strongly advocates the balance between the conceptual framework and application framework to finetune the approach while implementing I4.0 technologies. GDP = Gross Domestic Product I4.0 = Industry 4.0 IIoT = Industrial Internet of Things (), IoT = Internet of Things ISM = Interpretive Structural Modeling IT = Information Technology MCDM = Multi-Criteria Decision-Making Method The critical finding that IT and Technology risks should be attended as the top priority, is the vital scientific contribution of this paper along with the model development, to the new knowledge. MICMAC = Matriced’ Impacts Croise´s Multiplication Applique´e a´ un Classement ML = Machine Learning Sensitivity analysis through the radar diagram assures the reliability and robustness of the derived solution. This study reveals that the I4.0 phenomenon has a tremendous potential to impact industry operations. However, to harness the best of I4.0 a systematic and holistic study of the associated risks is a must. This will not only ensure the returns on investment but also will build trust in the system. PROMETHEE = Preference Ranking Organization METHod for Enrichment of Evaluations SMEs = Small and Medium Enterprise VIKOR = VIseKriterijumska Optimizacija I Kompromisno Resenje VR = Virtual Reality CONCLUSION CURRENT & FUTURE DEVELOPMENT The results obtained are validated and tested methodologically before putting it forward to the company for implementation. The research would be very beneficial to managers, academicians, researchers, and technocrats who would be involved in I4.0 implementation. CONSENT FOR PUBLICATION The I4.0 has unlimited opportunities and so are the challenges (Fig. 8). Even though the study is one of its kind and most relevant in the current context, it has a wide scope for future research. The results can be further validated by solving the same problem through different MCDM methods. The current study, though well supported by wide and critical literature review, still is based on the expert's opinion, which is subjected to variation in cognitive, experiential, and local demand and supply dynamics. The size of the group of experts may also affect the outcome. Lastly, the study is focused on only pertinent and most evident risks. Further sub risks/ micro risks and sub-criteria could be included in the analysis to get the micro-level exposure to the study. All data generated or analyzed during this study are included within the article. Not applicable. AVAILABILITY OF DATA AND MATERIALS FUNDING None. CONFLICT OF INTEREST The authors declare no conflict of interest, financial or otherwise. Risk Assessment and Implementation of Industry 4.0 Recent Advances in Computer Science and Communications, 2022, Vol. 15, No. 1 ACKNOWLEDGEMENTS The authors are grateful to the anonymous reviewers for their useful remarks and suggestions, and industrial experts, who want to remain anonymous. The authors would also like to thank the editorial assistance. [16] [17] REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] M. Ben-Daya, E. Hassini and Z. Bahroun, "Internet of things and supply chain management: A literature review", Int. J. Prod. Res., Vol. 57, No. (15-16), pp. 4719-4742, 2019. http://dx.doi.org/10.1080/00207543.2017.1402140 H. S. Birkel, J. W. Veile, J. M. Müller, E. Hartmann and K. I. 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