Nagarajan Deivanayagampillai, Kavikumar Jacob, Gobinath Vellapalayam Manohar, Said Broumi

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Industry 5.0 acceptance is accelerating, but research is still in its infancy, and existing research covers a small subset of context-specific obstacles. This study aims to enumerate all potential obstacles, quantitatively rank them, and assess interdependencies at the organizational level for Industry 5.0 adoption. To achieve this, we thoroughly review the literature, identify obstacles, and investigate causal relationships using a multi-criteria decision-making approach called single value Neutrosophic TODIM. Single-valued Neutrosophic sets (SVNS) ensembles are employed in a real-world setting to deal with uncertainty and indeterminacy. The suggested strategy enables the experts to conduct group decision-making by focusing on ranking the smaller collection of criterion values and the comparison with the decision-making trial and evaluation laboratory method (DEMATEL). According to the findings, the most significant hurdles are expenses and the funding system, capacity scalability, upskilling, and reskilling of human labor. As a result, a comfortable atmosphere is produced for decision-making, enabling the experts to handle an acceptable amount of data while still making choices.


Industry 5.0, Risk mitigation, Neutrosophic sets, Aggregation operators, TODIM, DEMATEL

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Garrett, G., 2021, Council post: How to align industry 5.0 initiatives with your customer experience strategy, Forbes, Available: (last access: 11.02.2023)

Raj, A., Mukherjee, A., de Sousa Jabbour, A.B., Srivastava, S.K., 2022, Supply chain management during and post-covid-19 pandemic: mitigation strategies and practical lessons learned, Journal of Business Research, 142, 1125–1139.

Pillai, S.G., Haldorai, K., Seo, W.S., Kim, W.G., 2021, Covid-19 and hospitality 5.0: Redefining hospitality operations, International Journal of Hospitality Management, 94, 102869.

Eschbach, A., 2021, Council post: How industry 5.0 will transform process manufacturing as we know it, Forbes, Available: (last access: 11.02.2023)

Hagag, A.M., Yousef, L.S., Abdelmaguid, T.F., 2023, Multi-criteria decision-making for machine selection in manufacturing and construction: Recent trends, Mathematics, 11(3), 631.

Darbanhosseiniamirkhiz, M., Wan Ismail, W.K., 2012, Advanced Manufacturing Technology Adoption in SMEs: An integrative model, Journal of technology management & innovation, 7(4), pp. 112–120.

Ungan, M.C., 2007, Manufacturing best practices: Implementation success factors and performance, Journal of Manufacturing Technology Management, 18(3), pp. 333–348.

Waldeck, N.E., Leffakis, Z.M., 2007, HR perceptions and the provision of workforce training in an AMT environment: an empirical study, Omega, 35(2), pp. 161–172.

Wang, J., Wei, G., Lu, M., 2018, TODIM method for multiple attribute group decision making under 2-tuple linguistic neutrosophic environment, Symmetry, 10(10), 486.

Wang, L, Wang, Y.M., Martínez, L., 2020, Fuzzy TODIM method based on alpha-level sets, Expert Systems with Applications, 140, 112899 .

Sachsenmeier, P., 2016, Industry 5.0—the relevance and implications of Bionics and Synthetic Biology, Engineering, 2(2), pp. 225–229.

Madsen, D.O., Berg, T., 2021, An exploratory bibliometric analysis of the birth and emergence of industry 5.0, Applied System Innovation, 4(4), 87.

Carayannis, E.G., Morawska-Jancelewicz, J., 2022, The futures of Europe: Society 5.0 and industry 5.0 as driving forces of future universities, Journal of the Knowledge Economy, 13(4) pp. 3445–3471.

Paschek, A., Mocan, Draghici, A., 2019, Industry 5.0 – the expected impact of next industrial revolution, Thriving on Future Education, Industry, Business and Society, Proceedings of the Make Learn and TIIM International Conference 2019.

Broo, G.D., Kaynak, O., Sait, S.M., 2022, Rethinking engineering education at the age of industry 5.0, Journal of Industrial Information Integration, 25, 100311.

Ciasullo, V., Orciuoli, F., Douglas, A., Palumbo, R., 2022, Putting health 4.0 at the service of society 5.0: exploratory insights from a pilot study, Socio-Economic Planning Sciences, 80, 101163.

Akundi, A., Euresti, D., Luna, S., Ankobiah, W., Lopes, A., Edinbarough, I., 2022, State of industry 5.0—analysis and identification of current research trends, Applied System Innovation, 5(1), 27.

World Economic Forum, 2022, Setting a path to green, resilient and inclusive development, Retrieved 24 April 2022, from. (last access: 11.02.2023)

Abdel-Basset, M., Manogaran, G., Gamal, A., Chang, V., 2020, A novel intelligent medical decision support model based on soft computing and IoT, IEEE Internet of Things Journal, 7(5), pp. 4160-4170.

World Bank, 2021, From COVID-19 crisis response to resilient recovery - saving lives and livelihoods while supporting green, resilient and inclusive development (GRID), Retrieved 24 April 2022, from. (last access: 11.02.2023)

World Economic Forum, 2020, These are the top 10 manufacturing countries in the world, Available: (last access: 11.02.2023)

World Manufacturing Foundation, 2019, Report 2019: skills for the future of manufacturing, Available: (last access: 11.02.2023)

Javaid, M., Haleem, A., Singh, R.P., Haq, M.I., Raina, A., Suman, R., 2020, Industry 5.0: potential applications in covid-19, Journal of Industrial Integration and Management, 5(4), pp. 507–530.

Özdemir, V., Hekim, N., 2018, Birth of industry 5.0: making sense of big data with artificial intelligence, ‘the internet of things’ and next-generation technology policy, OMICS: A Journal of Integrative Biology, 22(1), pp. 65–76.

Nahavandi, S., 2019, Industry 5.0—a human-centric solution, Sustainability, 11(16), 4371.

Demir, K.A., Döven, G., Sezen, B., 2019, Industry 5.0 and human-robot co-working, Procedia Computer Science, 158, pp. 688–695.

Fukuda, K., 2020, Science, technology and innovation ecosystem transformation toward society 5.0, International Journal of Production Economics, 220, 107460

Sharma, M., Kamble, S., Mani, V., Sherawat, R., Belhadi, A., Sharma, V., 2021, Industry 4.0 adoption for sustainability in multi-tier manufacturing supply chain in emerging economies, Journal of Cleaner Production, 281, 125013.

Bartoloni, S., Calò, E., Marinelli, L., Pascucci, F., Dezi, L., Carayannis, E., Revel, G.M., Gregori, G.L., 2022, Towards designing society 5.0 solutions: the new quintuple helix - design thinking approach to technology, Technovation, 113, 102413.

Thakur, P., Sehgal, V.K., 2021, Emerging architecture for heterogeneous smart cyber-physical systems for industry 5.0, Computers & Industrial Engineering, 162, 107750.

Carayannis, E.G., Draper, J., Bhaneja, B., 2021, Towards fusion energy in the industry 5.0 and society 5.0 context: call for a global commission for urgent action on fusion energy, Journal of the Knowledge Economy, 12(4), pp. 1891-1904.

Maddikunta, P.K.R., Pham, Q.-V., Prabadevi, B., Deepa, N., Dev, K., Gadekallu, T.R., Ruby, R., Liyanage, N., 2022, Industry 5.0: A survey on enabling technologies and potential applications, Journal of Industrial Information Integration, 26, 100257 .

Xu, X., Lu, Y., Vogel-Heuser, B., Wang, L., 2021, Industry 4.0 and industry 5.0—inception, conception and perception, Journal of Manufacturing Systems, 61, pp. 530–535.

Wells, J., Camelio, J.A., Williams, C.B., White, J., 2014, Cyber-physical security challenges in manufacturing systems, Manufacturing Letters, 2(2), pp. 74–77.

Korneev, N.V., 2020, Intelligent complex security management system FEC for the industry 5.0, IOP Conference Series: Materials Science and Engineering, 950(1), 012016.

Porambage, P., Gür, G., Moya Osorio, D.P., Livanage, M., M. Ylianttila, M., 2021, 6G security challenges and potential solutions, 2021 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Porto, Portugal, pp. 622-627.

Bilge, L., Dumitraş, T., 2012, Before we knew it: an empirical study of zero-day attacks in the real world, Proceedings of the 2012 ACM conference on Computer and communications security, pp. 833-844.

Wrona, K., 2015, Securing the internet of things a military perspective, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 502-507.

Porambage, P., Gür, G., Osorio, D.P.M., Liyanage, M., Gurtov, A., Ylianttila, M., 2021, The roadmap to 6g security and privacy, IEEE Open Journal of the Communications Society, 2, pp. 1094-1122.

Esposito, C., Castiglione, A., Martini, B., Choo, K.-K.R., 2016, Cloud manufacturing: security, privacy, and forensic concerns, IEEE Cloud Computing, 3(4), pp. 16–22.

Santosh, C., Wall, C., 2022, AI and ethical issues, AI, Ethical Issues and Explainability - Applied Biometrics, Springer, Singapore,

Vesnic-Alujevic, L., Nascimento, S., Pólvora, A., 2020, Societal and ethical impacts of artificial intelligence: critical notes on European policy frameworks, Telecommunications Policy, 44(6), 101961.

Maija, B., Lars, D.N., Athanasios, P., 2021, Industry 5.0: towards a sustainable, human-centric and resilient European industry, European Commission, Directorate-General for Research and Innovation, Publications office, Luxembourg.

Schwalbe, M., 2016, Additive manufacturing scalability, implementation, readiness, and transition, Predictive Theoretical and Computational Approaches for Additive Manufacturing: Proceedings of a Workshop, The National Academic Press, Washington, DC, 10, pp. 81–102.

Sharma, I., Kiran, D., Garg, I., 2020, Industry 5.0 and smart cities: a futuristic approach, European Journal of Molecular & Clinical Medicine, 7(8), pp. 2750–2756.

Lima, F., De Carvalho, C.N., Acardi, M.B., Dos Santos, E.G., De Miranda, G.B., Maia, R.F., Massote, A., A., 2019, Digital manufacturing tools in the simulation of collaborative robots: towards industry 4.0, Brazilian Journal of Operations & Production Management, 16(2), pp. 261–280.

Stahl, B.C., 2021, Artificial intelligence for a better future: an ecosystem perspective on the ethics of AI and emerging digital technologies, Springer Nature, 124 p.

Mukherjee, A.A., Raj, A., Aggarwal, S., 2023, Identification of barriers and their mitigation strategies for industry 5.0 implementation in emerging economies, International Journal of Production Economics, 257, 108770.

Sheridan, T.B., 2016, Human–Robot Interaction, Human Factors, The Journal of the Human Factors and Ergonomics Society, 58(4), pp. 525–532.

Sanghi, S., Subbiah, M.V., Reddy, R.C.M., Ganguly, S., Gupta, G.S., Unni, J., Sarkar, S., Sarin, A., Chand, V.S., Vasavada, M., 2012, Preparing a globally competitive skilled workforce for Indian economy: emerging trends and challenges, Vikalpa: The Journal for Decision Makers, 37(3), pp. 87–128.

Kraaijenbrink, J., Spender, J.-C., Groen, A.J., 2010, The resource-based view: a review and assessment of its critiques, Journal of Management, 36(1), pp. 349–372.

Liyanage, M., Braeken, A., Kumar, P., Ylianttila, M., 2020, IoT security: advances in authentication, John Wiley and Sons.

Zadeh, L.A., 1965, Fuzzy sets, Information and Control, 8, pp. 338-358.

Smarandache, F., 2005, Neutrosophic set- a generalization of the intuitionistic fuzzy set, International Journal of Pure and Applied Mathematics, 24(3), pp. 287-297.

Ye, J., 2014, A multi-criteria decision-making method using aggregation operators for simplified neutrosophic sets, Journal of Intelligent and Fuzzy Systems, 26, pp. 2459-2466.

Broumi, S., Smarandache, F., 2014, New operations on interval neutrosophic sets, Neutrosophic Theory and Applications, 1, pp. 256-266.

Peng, J.J., Wang, J.Q., Wu, X.H., Wang, J., Chen, X.H., 2015, Multi-valued neutrosophic sets and power aggregation operators with their applications in multi-criteria group decision-making problems, International Journal of Computational Intelligent Systems, 8(2), pp. 345- 363.

Gao, Z., Zhu, L., Li, Z., Fan, P., 2015, Threat evaluation of early warning detection based on incomplete attribute information TODIM method, 3rd International Conference on Machinery, Materials, and Information Technology Applications. Atlantic press, United States. pp. 40-47.

Gomes, L.F.A.M., Rangel, L.A.D., Maranhao, F.J.C., 2009, Multicriteria analysis of natural gas destination in Brazil: an application of the TODIM method, Mathematical and Computer Modeling, 50(1-2), pp. 92-100.

Gomes L.F.A.M., Machado, M.A.S., Santos, D.J., Caldeira A.M., 2015, Ranking of suppliers for steel industry: a comparison of the original TODIM and the Choquet-extended TODIM methods, Procedia Computer Science, 55, pp. 706-714.

Adali, E.A., Isik, A.T., Kundakci, N., 2016, TODIM method for the selection of elective course, European Scientific Journal, 12(10), pp. 314-324.

Qin, Q., Liang, F-F., Li, L, Chen, Y-W., Yu, G-F., 2017, A TODIM-based multi- criteria group decision making with triangular intuitionistic fuzzy numbers, Applied Soft Computing, 55, pp. 93-107.

Krohling, R.A., de Souza, T.T.M., 2012, Combining prospect theory and fuzzy numbers to multi-criteria decision making, Expert Systems with Applications, 39, pp. 11487-11493.

Krohling, R.A., de Souza T.T.M., 2012, F-TODIM: An application of the fuzzy TODIM method, to rental evaluation of residential properties, Congreso Latino-Iberoamericano de Investigation perativa, Symposio Brasileiro de Pesquisa Operational, September 24-28, Rio de Janeiro, Brazil, pp. 431-443.

Gomes, L.F.A.M., Machado, M.A.S., Costa, F.F., Rangel, L.A.D., 2013, Criteria interactions in multiple criteria decision aiding: a Choquet formulation for the TODIM method, Procedia Computer Science, 17, pp. 324-331.

Gomes, L.F.A.M., Machado, M.A.S., Costa, F.F., Rangel, L.A.D., 2013, Behavioral multi-criteria decision analysis: the TODIM method with criteria interactions, Annals of Operations Research, 211, pp. 531-548.

Passos, A.C., Teixeira, M.G., Garcia, K.C., Cardoso, A.M., Gomes, L.F.A.M., 2014, Using the TODIM-FSE method as a decision-making support methodology for oil spill response, Computers & Operations Research, 42, pp. 40-48.

Wei, C., Zhiliang, R., Rodriguez, R.M., 2014, A hesitant fuzzy linguistic TODIM method based on a score function, International Journal of Computational Intelligence Systems, 8(4), pp. 701-712.

Lourenzutti, R., Krohling, R.A., 2013, A study of TODIM in a intuitionistic fuzzy and random environment, Expert Systems with Applications, 40, pp. 6459-6468.

Lourenzutti, R., Krohling, R.A., 2014, The Hellinger distance in multi-criteria decision making: an illustration to the TOPSIS and TODIM methods, Expert Systems with Applications, 41, pp. 4414-4421.

Lourenzutti, R., Krohling, R.A., 2015, TODIM based method to process heterogeneous information, Procedia Computer Science, 55, pp. 318-327.

Li, M., Wu, C., Zhang, L, You, L.N., 2015, An intuitionistic fuzzy-TODIM method to solve distributor evaluation and selection problem, International Journal of Simulation Modelling, 14(3), pp. 511-524.

Tosun, Ö., Akyüz, G., 2015, A fuzzy TODIM approach for the supplier selection problem, International Journal of Computational Intelligence Systems, 8(2), pp. 317-329.

Ren, P., Xu, Z., Gou, X., 2016, Pythagorean fuzzy TODIM approach to multi-criteria decision making, Applied Soft Computing, 42, pp. 246-259.

Zhang, M., Peide, L., Lanlan, S., 2016, An extended multiple attribute group decision-making TODIM method based on the neutrosophic numbers, Journal of Intelligent and Fuzzy Systems, 30(3), pp. 1773-1781.

Lin, C., Lee, C., Lin, J., 2016, Using the fuzzy TODIM method as a decision making support methodology for house purchasing, Journal of Testing and Evaluation, 44(5), pp. 1925-1936.

Sang, X., Liu, X., An interval type-2 fuzzy sets- based TODIM method and its application to green supplier selection, Journal of the Operational Research Society, 67(5), pp. 722- 734.

Pramanik, S., Dalapati, S., Alam, S., Roy, T.P., 2017, NC-TODIM-based MAGDM under a neutrosophic cubic set environment, Journal of Information, 8(149), 149.

Sun, R., Hu, J-J., Chen, X., 2017, Novel single-valued Neutrosophic decision-making approaches based on prospect theory and their applications in physician selection, Soft Computing, 20, pp. 1-15.

He, X., Wu, Y., 2017, City sustainable development evaluation based on hesitant multiplicative fuzzy information, Mathematical Problems in Engineering. 2017, 8306508 p.

Deng, X., Gao, H., 2019, TODIM method for multiple attribute decision making with 2-tuple linguistic Pythagorean fuzzy information, Journal of Intelligent Fuzzy Systems, 37(2), pp. 1769-1780.

Davoudabadi, R., Mousavi, S.M., Mohagheghi, V., 2020, A new last aggregation method of multi-attributes group decision making based on concepts of TODIM, WASPAS and TOPSIS under interval-valued intuitionistic fuzzy uncertainty, Knowledge and Information System, 62, pp. 1371-1391.

Zindani, D., Maity, S.R., Bhowmik, S., 2020, Interval-valued intuitionistic fuzzy TODIM method based on Schweizer–Sklar power aggregation operators and their applications to group decision making, Soft Computing, 24, pp. 14091–14133.

Ulrich, F.S., Henri, G., 2018, Fuzzy triangular aggregation operators, International Journal of Mathematics and Mathematical Sciences, 2018, 9209524 p.

Liao, N., Wei, G., Chen, X., 2022, TODIM method based on cumulative prospect theory for multiple attributes group decision making under probabilistic hesitant fuzzy setting, International Journal of Fuzzy System, 24, pp. 322–339.

Leoneti, A.B., Gomes, L.F.A.M., 2021, A novel version of the TODIM method based on the exponential model of prospect theory: the ExpTODIM method, European Journal of Operational Research, 295(3), pp. 1042-1055.

Sun, H., Yang, Z., Cai, Q., Wei, G., Mo, Z., 2023, An extended Exp-TODIM method for multiple attribute decision making based on the Z-Wasserstein distance, Expert Systems with Applications, 214, 119114.

Prashar, A., Aggarwal, S., 2019, Modeling enablers of supply chain quality risk management: a grey-DEMATEL approach, The TQM Journal, 32(5), pp. 1059–1076.

Wang, F., Zhang, J., Zhang, P., 2021, Influencing Factors of Smart Community Service Quality: Evidence from China, Tehnički Vjesnik 28(4), pp. 1187-1196.

Tzeng, G.H., Chiang, C.H., Li, C.W., 2007, Evaluating intertwined effects in e-learning programs: a novel hybrid MCDM model based on factor analysis and DEMATEL, Expert System with Applications, 32(4), pp. 1028–1044.

Raj, A., Dwivedi, G., Sharma, A., Lopes de Sousa Jabbour, A.B., Rajak, S., 2020, Barriers to the adoption of Industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective, International Journal of Production Economics, 224, 107546.

Sheng, L.S., Xiao, Y.Y., Hu, C.L., Ping, Z., DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications, Mathematical Problems in Engineering, 2018, 3696457.

Romel, M., Kabir, G., Ng, K.T.W., 2023, Analysis of barriers to photovoltaic waste management to achieve net-zero goal of Canada, Environ Science and Pollution Research,

Wenping, X., Yu, L., David, P., 2023, An evaluation of factors influencing the vulnerability of emergency logistics supply chains, International Journal of Logistics Research and Applications,

Riaz, M., Farid, H.M.A., Ashraf, S., Kamaci, H., 2023, Single-valued neutrosophic fairly aggregation operators with multi-criteria decision-making, Comp. Appl. Math. 42, 104.

Granados, C., 2023, Quadripartitioned single-valued neutrosophic properties and their application to factors affecting energy prices, Process Integration and Optimization for Sustainability, 7, pp. 575–582.

Liu, Y., Yang, X., 2023, EDAS method for single-valued neutrosophic number multiattribute group decision-making and applications to physical education teaching quality evaluation in colleges and universities, Mathematical Problems in Engineering, 2023, 5576217.

Nagarajan, D., Kavikumar, J., 2022, Single-valued and interval-valued neutrosophic hidden Markov model, Mathematical Problems in Engineering, 2022, 5323530.

Wang, W., Huang, B., Wang, T., Optimal scale selection based on multi-scale single-valued neutrosophic decision-theoretic rough set with cost-sensitivity, International Journal of Approximate Reasoning, 155(C), pp. 132–144.

Nagarajan, D., Kanchana, A., Kavikumar, J., Kausar, N., Edalatpanah, S.A., Shah, M.A., 2023, A novel approach based on neutrosophic Bonferroni mean operator of trapezoidal and triangular neutrosophic interval environments in multi-attribute group decision making, Scientific Reports, 13, 10455.

Wang, H., Zhang, Y., Sunderraman, R., 2005, Truth-value based interval neutrosophic sets, Proceedings of the 2005 IEEE International Conference on Granular Computing, Beijing, China, 1, pp. 274–277.

Smarandache, F., 2020, The score, accuracy, and certainty functions determine a total order on the set of neutrosophic triplets (T, I, F), Neutrosophic Sets and Systems, 38, pp. 1-14.

Wang, H., Smarandache, F., Zhang, F.Q., Sunderraman, R., 2010, Single valued neutrosophic sets, Multispace and Multistructure, 4, pp. 410-413.

Zhang, H., Wang, J.-Q., Chen, X-H., 2014, Interval neutrosophic sets and their application in multicriteria decision making problems, The Scientific World Journal, 2014, 645953.

Mukherjee, A.A., Raj, A., Aggarwal, S., 2023, Identification of barriers and their mitigation strategies for industry 5.0 implementation in emerging economies, International Journal of Production Economics, 257, 108770.



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