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Prioritization of the approaches for overcoming smart sustainable manufacturing barriers using stochastic fuzzy EDAS method

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Abstract

In manufacturing organizations, digitization and sustainability significantly impact how digital and green features are integrated into physical products and services. This study looks at how these two ideas come together in traditional manufacturing. Making things smart and eco-friendly is important for growing factories and has begun to evolve. However, due to the most significant barriers, it is challenging for manufacturing industries to embrace smart, sustainable solutions. In the past, researchers looked at what's stopping factories from being sustainable and using new technology. A comprehensive literature review identified 33 barriers and 31 solutions under eight smart, sustainable manufacturing (SSM) categories. The fuzzy AHP approach prioritized these barriers, with technology barriers ranking highest. Stochastic fuzzy EDAS assessed and ranked SSM solutions, highlighting the most effective collaborative efforts in environmental awareness (S10) and training on smart and sustainable processes (S2). This study also addresses economic, social, and environmental stakeholder issues. A sensitivity analysis is also performed to examine the validity of the current study’s results. The results of this research may assist decision-makers and professionals in defining the main barriers and implementing solutions for the effective adoption of smart sustainability practices throughout the industrial sector. This paper is just the beginning, and there's more to learn.

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References

  1. Nallusamy, S., Saravanan, V.: Optimization of process flow in an assembly line of manufacturing unit through lean tools execution. Int. J. Eng. Res. Africa 38, 133–143 (2018). https://doi.org/10.4028/www.scientific.net/JERA.38.133

    Article  Google Scholar 

  2. Hemalatha, C., Sankaranarayanasamy, K., Durairaaj, N.: Lean and agile manufacturing for work-in-process (WIP) control. Mater. Today Proc. (2021). https://doi.org/10.1016/j.matpr.2020.12.473

    Article  Google Scholar 

  3. Fahmideh, M., Beydoun, G.: Big data analytics architecture design—An application in manufacturing systems. Comput. Ind. Eng. 128, 948–963 (2019). https://doi.org/10.1016/j.cie.2018.08.004

    Article  Google Scholar 

  4. Ramadan, M., Al-maimani, H., Noche, B.: RFID-enabled smart real-time manufacturing cost tracking system. Int. J. Adv. Manuf. Technol. (2016). https://doi.org/10.1007/s00170-016-9131-1

    Article  Google Scholar 

  5. Cheng, Y., Tao, F., Zhao, D., Zhang, L.: Robotics and Computer-Integrated Manufacturing Modeling of manufacturing service supply – demand matching hypernetwork in service-oriented manufacturing systems. Robot. Comput. Integr. Manuf. 45, 59–72 (2017). https://doi.org/10.1016/j.rcim.2016.05.007

    Article  Google Scholar 

  6. Baldassarre, F., Ricciardi, F.: The additive manufacturing in the industry 40 Era: The case of an Italian FabLab. J. Emerg. Trends Market Manag. I(1), 105–115 (2017)

    Google Scholar 

  7. Mao, S., Wang, B., Tang, Y., Qian, F.: Opportunities and challenges of artificial intelligence for green manufacturing in the process industry. Engineering 5(6), 995–1002 (2019). https://doi.org/10.1016/j.eng.2019.08.013

    Article  Google Scholar 

  8. Li, Y., Gao, M., Yang, L., Zhang, C., Zhang, B., Zhao, X.: Design of and research on industrial measuring devices based on Internet of Things technology. Ad Hoc Netw. 102, 102072 (2020). https://doi.org/10.1016/j.adhoc.2020.102072

    Article  Google Scholar 

  9. Kusiak, A.: Smart manufacturing. Int. J. Prod. Res. 56(1–2), 508–517 (2018). https://doi.org/10.1080/00207543.2017.1351644

    Article  Google Scholar 

  10. Malek, J., Desai, T.N.: A systematic literature review to map literature focus of sustainable manufacturing. J. Clean. Prod. 256, 120345 (2020). https://doi.org/10.1016/j.jclepro.2020.120345

    Article  Google Scholar 

  11. Zhang, H., Veltri, A., Calvo-Amodio, J., Haapala, K.R.: Making the business case for sustainable manufacturing in small and medium-sized manufacturing enterprises: a systems decision making approach. J Clean Prod (2020). https://doi.org/10.1016/j.jclepro.2020.125038

    Article  Google Scholar 

  12. Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., Parmentola, A.: Smart manufacturing systems and applied industrial technologies for a sustainable industry: a systematic literature review. Appl. Sci. (2020). https://doi.org/10.3390/APP10082897

    Article  Google Scholar 

  13. Maghazei, O., Netland, T.: Drones in manufacturing: exploring opportunities for research and practice. J. Manuf. Technol. Manag. 31(6), 1237–1259 (2020). https://doi.org/10.1108/JMTM-03-2019-0099

    Article  Google Scholar 

  14. Wawrla, L., Maghazei, O., Netland, P. D. T.: Applications of drones in warehouse operations” ETH Zurich, no. August, p. 13

  15. Wegner, A., Graham, J., Ribble, E.: A New Approach to Cyberphysical Security in Industry 4.0. Springer, Berlin, pp. 59–72 (2017)

  16. Prinsloo, J., Sinha, S., von Solms, B.: A review of industry 4.0 manufacturing process security risks. Appl. Sci. (2019). https://doi.org/10.3390/app9235105

    Article  Google Scholar 

  17. Beier, G., Ullrich, A., Niehoff, S., Reibig, M., Habich, M.: Industry 4.0: how it is defined from a sociotechnical perspective and how much sustainability it includes—a literature review. J. Clean Prod. (2020). https://doi.org/10.1016/j.jclepro.2020.120856

    Article  Google Scholar 

  18. Ervural, B.C., Ervural, B.: Overview of cyber security in the industry 4.0 Era, pp. 267–284. Springer, Cham (2018)

    Google Scholar 

  19. Moghaddam, M., Cadavid, M.N., Kenley, C.R., Deshmukh, A.V.: Reference architectures for smart manufacturing: a critical review. J. Manuf. Syst. 49(June), 215–225 (2018). https://doi.org/10.1016/j.jmsy.2018.10.006

    Article  Google Scholar 

  20. Agarwal, S., et al.: Prioritizing the barriers of green smart manufacturing using AHP in implementing Industry 4.0: a case from Indian automotive industry. TQM J (2022). https://doi.org/10.1108/TQM-07-2022-0229

    Article  Google Scholar 

  21. Kumar, P., Brar, P.S., Singh, D., Bhamu, J.: Fuzzy AHP approach for barriers to implement LSS in the context of Industry 4.0. Int. J. Product. Perform. Manag. (2020). https://doi.org/10.1108/IJPPM-12-2021-0715

    Article  Google Scholar 

  22. Dutta, G., Kumar, R., Sindhwani, R., Singh, R.K.: Overcoming the barriers of effective implementation of manufacturing execution system in pursuit of smart manufacturing in SMEs. Procedia Comput. Sci. 200(January), 820–832 (2022). https://doi.org/10.1016/j.procs.2022.01.279

    Article  Google Scholar 

  23. Malek, J., Desai, T.N.: A framework for prioritizing the solutions to overcome sustainable manufacturing barriers. Clean. Logist. Supply Chain 1(July), 100004 (2021). https://doi.org/10.1016/j.clscn.2021.100004

    Article  Google Scholar 

  24. Yip, W.S., To, S.: Identification of stakeholder related barriers in sustainable manufacturing using Social Network Analysis. Sustain. Prod. Consum. 27, 1903–1917 (2021). https://doi.org/10.1016/j.spc.2021.04.018

    Article  Google Scholar 

  25. Kumar, P., Bhamu, J., Sangwan, K.S.: Analysis of barriers to industry 4.0 adoption in manufacturing organizations: an ISM approach. Procedia CIRP 98(March), 85–90 (2021). https://doi.org/10.1016/j.procir.2021.01.010

    Article  Google Scholar 

  26. Wong, L.W., Tan, G.W.H., Lee, V.H., Ooi, K.B., Sohal, A.: Psychological and system-related barriers to adopting blockchain for operations management: an artificial neural network approach. IEEE Trans. Eng. Manag. (2021). https://doi.org/10.1109/TEM.2021.3053359

    Article  Google Scholar 

  27. Virmani, N., Bera, S., Kumar, R.: Identification and testing of barriers to sustainable manufacturing in the automobile industry: a focus on Indian MSMEs. Benchmarking 28(3), 857–880 (2021). https://doi.org/10.1108/BIJ-08-2020-0413

    Article  Google Scholar 

  28. Stentoft, J., AdsbøllWickstrøm, K., Philipsen, K., Haug, A.: Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers. Prod Plan Control 32(10), 811–828 (2021). https://doi.org/10.1080/09537287.2020.1768318

    Article  Google Scholar 

  29. Majumdar, A., Garg, H., Jain, R.: Managing the barriers of Industry 4.0 adoption and implementation in textile and clothing industry: Interpretive structural model and triple helix framework. Comput Ind 125, 103372 (2021). https://doi.org/10.1016/j.compind.2020.103372

    Article  Google Scholar 

  30. Chauhan, C., Singh, A., Luthra, S.: Barriers to industry 4.0 adoption and its performance implications: an empirical investigation of emerging economy. J. Clean. Prod. 285, 124809 (2021). https://doi.org/10.1016/j.jclepro.2020.124809

    Article  Google Scholar 

  31. Singh, R., Bhanot, N.: An integrated DEMATEL-MMDE-ISM based approach for analysing the barriers of IoT implementation in the manufacturing industry. Int. J. Prod. Res. 58(8), 2454–2476 (2020). https://doi.org/10.1080/00207543.2019.1675915

    Article  Google Scholar 

  32. Raj, A., Dwivedi, G., Sharma, A., de Sousa-Jabbour, A.B.L., Rajak, S.: Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. Int. J. Prod. Econ. (2020). https://doi.org/10.1016/j.ijpe.2019.107546

    Article  Google Scholar 

  33. Singh, M., Singh, K., Sethi, A.: An empirical investigation and prioritizing critical barriers of green manufacturing implementation practices through VIKOR approach. World J. Sci. Technol. Sustain. Dev. 17(2), 235–254 (2020). https://doi.org/10.1108/wjstsd-08-2019-0060

    Article  Google Scholar 

  34. Ariffin, R., et al.: Drivers and barriers analysis for green manufacturing practices in Malaysian SMEs: a preliminary findings. Procedia CIRP 26, 658–663 (2020). https://doi.org/10.1016/j.procir.2015.02.085

    Article  Google Scholar 

  35. Narayanan, A.E., Sridharan, R., Ram Kumar, P.N.: Analyzing the interactions among barriers of sustainable supply chain management practices: a case study. J. Manuf. Technol. Manag. 30(6), 937–971 (2019). https://doi.org/10.1108/JMTM-06-2017-0114

    Article  Google Scholar 

  36. Sindhwani, R., Mittal, V.K., Singh, P.L., Aggarwal, A., Gautam, N.: Modelling and analysis of barriers affecting the implementation of lean green agile manufacturing system (LGAMS). Benchmarking 26(2), 498–529 (2019). https://doi.org/10.1108/BIJ-09-2017-0245

    Article  Google Scholar 

  37. Rauch, E., Dallasega, P., Unterhofer, M.: Requirements and barriers for introducing smart manufacturing in small and medium-sized enterprises. IEEE Eng. Manag. Rev. 47(3), 87–94 (2019). https://doi.org/10.1109/EMR.2019.2931564

    Article  Google Scholar 

  38. Bhandari, D., Singh, R.K., Garg, S.K.: Prioritisation and evaluation of barriers intensity for implementation of cleaner technologies: framework for sustainable production. Resour. Conserv. Recycl. 146(February), 156–167 (2019). https://doi.org/10.1016/j.resconrec.2019.02.038

    Article  Google Scholar 

  39. Klein, M.M., Biehl, S.S., Friedli, T.: Barriers to smart services for manufacturing companies – an exploratory study in the capital goods industry. J. Bus. Ind. Mark. 33(6), 846–856 (2018). https://doi.org/10.1108/JBIM-10-2015-0204

    Article  Google Scholar 

  40. Raut, R., Narkhede, B.E., Gardas, B.B., Luong, H.T.: An ISM approach for the barrier analysis in implementing sustainable practices: the Indian oil and gas sector. Benchmarking 25(4), 1245–1271 (2018). https://doi.org/10.1108/BIJ-05-2016-0073

    Article  Google Scholar 

  41. Gupta, H., Barua, M.K.: A framework to overcome barriers to green innovation in SMEs using BWM and Fuzzy TOPSIS. Sci. Total. Environ. 633, 122–139 (2018). https://doi.org/10.1016/j.scitotenv.2018.03.173

    Article  Google Scholar 

  42. Kumar, A., Dixit, G.: An analysis of barriers affecting the implementation of e-waste management practices in India: a novel ISM-DEMATEL approach. Sustain. Prod. Consum. 14, 36–52 (2018). https://doi.org/10.1016/j.spc.2018.01.002

    Article  Google Scholar 

  43. Bhanot, N., Rao, P.V., Deshmukh, S.G.: An integrated approach for analysing the enablers and barriers of sustainable manufacturing. J. Clean. Prod. 142, 4412–4439 (2017). https://doi.org/10.1016/j.jclepro.2016.11.123

    Article  Google Scholar 

  44. Mittal, V.K., Sangwan, K.S.: Prioritizing barriers to green manufacturing: environmental, social and economic perspectives. Procedia CIRP 17, 559–564 (2014). https://doi.org/10.1016/j.procir.2014.01.075

    Article  Google Scholar 

  45. Ghorabaee, M.K., Zavadskas, E.K., Olfat, L., Turskis, Z.: Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Inform 26(3), 435–451 (2015). https://doi.org/10.15388/Informatica.2015.57

    Article  Google Scholar 

  46. Emovon, I., Norman, R.A., Murphy, A.J.: Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems. J. Intell. Manuf. 29(3), 519–531 (2018). https://doi.org/10.1007/s10845-015-1133-6

    Article  Google Scholar 

  47. Mathew, M., Chakrabortty, R.K., Ryan, M.J.: A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Eng. Appl. Artif. Intell. 96(June), 103988 (2020). https://doi.org/10.1016/j.engappai.2020.103988

    Article  Google Scholar 

  48. Mathew, M., Chakrabortty, R.K., Ryan, M.J.: Selection of an optimal maintenance strategy under uncertain conditions: an interval type-2 fuzzy AHP-TOPSIS method. IEEE Trans. Eng. Manag. (2020). https://doi.org/10.1109/tem.2020.2977141

    Article  Google Scholar 

  49. Kahraman, C., KeshavarzGhorabaee, M., Zavadskas, E.K., CevikOnar, S., Yazdani, M., Oztaysi, B.: Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. J. Environ. Eng. Landsc. Manag. 25(1), 1–12 (2017). https://doi.org/10.3846/16486897.2017.1281139

    Article  Google Scholar 

  50. Ju, Y., et al.: A new framework for health-care waste disposal alternative selection under multi-granular linguistic distribution assessment environment. Comput. Ind. Eng. 145(May), 106489 (2020). https://doi.org/10.1016/j.cie.2020.106489

    Article  Google Scholar 

  51. Li, X., Ju, Y., Ju, D., Zhang, W., Dong, P., Wang, A.: Multi-attribute group decision making method based on EDAS under picture fuzzy environment. IEEE Access 7, 141179–141192 (2019). https://doi.org/10.1109/ACCESS.2019.2943348

    Article  Google Scholar 

  52. Hou, H., Zhao, C.: A novel D-SCRI–EDAS method and its application to the evaluation of an online live course platform. Systems (2022). https://doi.org/10.3390/systems10050157

    Article  Google Scholar 

  53. Zhu, Y.J., Guo, W., Liu, H.C.: Knowledge representation and reasoning with an extended dynamic uncertain causality graph under the pythagorean uncertain linguistic environment. Appl. Sci. (2022). https://doi.org/10.3390/app12094670

    Article  Google Scholar 

  54. Chini, M., Arefi, S.L., Zolfani, S.H., Ustinovicius, L.: Choosing a proper method for strengthening WPC beams with grooving method using swara-edas. Arch. Civ. Eng. 64(4), 161–174 (2018). https://doi.org/10.2478/ace-2018-0050

    Article  Google Scholar 

  55. Lin, C.T., Chiang, C.Y.: Development of strategies for Taiwan’s corrugated box precision printing machine industry—an implementation for SWOT and EDAS methods. Sustainability (2022). https://doi.org/10.3390/su14095144

    Article  Google Scholar 

  56. Abubakr, M., Abbas, A.T., Tomaz, I., Soliman, M.S., Luqman, M., Hegab, H.: Sustainable and smart manufacturing: an integrated approach. Sustain. 12(6), 1–19 (2020). https://doi.org/10.3390/su12062280

    Article  Google Scholar 

  57. Moldavska, A., Welo, T.: The concept of sustainable manufacturing and its definitions: a content-analysis based literature review. J. Clean. Prod. 166, 744–755 (2017). https://doi.org/10.1016/j.jclepro.2017.08.006

    Article  Google Scholar 

  58. Singh, P.K., Sarkar, P.: A framework based on fuzzy AHP-TOPSIS for prioritizing solutions to overcome the barriers in the implementation of ecodesign practices in SMEs. Int. J. Sustain. Dev. World Ecol. 26(6), 506–521 (2019). https://doi.org/10.1080/13504509.2019.1605547

    Article  Google Scholar 

  59. Dubois, A., Gadde, L.E.: ‘Systematic combining’-a decade later. J. Bus. Res. 67(6), 1277–1284 (2014). https://doi.org/10.1016/j.jbusres.2013.03.036

    Article  Google Scholar 

  60. Rao, P. K., Bhargav, V. R.: A study on green packaging- a case study approach with reference to dell Inc. No. July, pp. 83–84, (2016)

  61. Çinar, Z.M., Nuhu, A.A., Zeeshan, Q., Korhan, O., Asmael, M., Safaei, B.: Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability (2020). https://doi.org/10.3390/su12198211

    Article  Google Scholar 

  62. Andreadis, E., Garza-Reyes, J.A., Kumar, V.: Towards a conceptual framework for value stream mapping (VSM) implementation: an investigation of managerial factors. Int. J. Prod. Res. 55(23), 7073–7095 (2017). https://doi.org/10.1080/00207543.2017.1347302

    Article  Google Scholar 

  63. Arey, D., Le, C.H., Gao, J.: Lean industry 4.0: a digital value stream approach to process improvement. Procedia Manuf. 54, 19–24 (2020). https://doi.org/10.1016/j.promfg.2021.07.004

    Article  Google Scholar 

  64. Vrchota, J., Pech, M., Rolínek, L., Bednář, J.: Sustainability outcomes of green processes in relation to industry 4.0 in manufacturing: systematic review. Sustainability (2020). https://doi.org/10.3390/su12155968

    Article  Google Scholar 

  65. Mazurek, J., Mielcová, E.: The evaluation of economic recession magnitude: introduction and application. Prague Econ. Pap. 2, 182–205 (2013). https://doi.org/10.18267/j.pep.447

    Article  Google Scholar 

  66. Song, M., Wang, S., Zhang, H.: Could environmental regulation and R & D tax incentives affect green product innovation ? J. Clean. Prod. 258, 120849 (2020). https://doi.org/10.1016/j.jclepro.2020.120849

    Article  Google Scholar 

  67. Nižetić, S., Šolić, P., López-de-Ipiña González-de-Artaza, D., Patrono, L.: Internet of Things (IoT): opportunities, issues and challenges towards a smart and sustainable future. J. Clean. Prod. (2020). https://doi.org/10.1016/j.jclepro.2020.122877

    Article  Google Scholar 

  68. Manavalan, E., Jayakrishna, K.: A review of Internet of Things (IoT) embedded sustainable supply chain for industry 40 requirements. Comput. Ind. Eng. 127, 925–953 (2019). https://doi.org/10.1016/j.cie.2018.11.030

    Article  Google Scholar 

  69. Balaji, V., Venkumar, P., Sabitha, M. S., Amuthaguka, D.: DVSMS: dynamic value stream mapping solution by applying IIoT. Sadhana - Acad. Proc. Eng. Sci., (2020) https://doi.org/10.1007/s12046-019-1251-5

  70. Jena, M.C., Mishra, S.K., Moharana, H.S.: Application of Industry 4.0 to enhance sustainable manufacturing. Environ. Prog. Sustain. Energy 39(1), 1–11 (2020). https://doi.org/10.1002/ep.13360

    Article  Google Scholar 

  71. Machado, C.G., Winroth, M.P., Ribeiro da Silva, E.H.D.: Sustainable manufacturing in Industry 4.0: an emerging research agenda. Int. J. Prod. Res. 58(5), 1462–1484 (2020). https://doi.org/10.1080/00207543.2019.1652777

    Article  Google Scholar 

  72. Teodorico, C., Carmina, S., Anastasia, S., Roberto, G., Francesco, T., Maria, R.: Noise and cardiovascular effects in workers of the sanitary fixtures industry. Int. J. Hyg. Environ. Health (2014). https://doi.org/10.1016/j.ijheh.2014.09.007

    Article  Google Scholar 

  73. Awan, U., Arnold, M.G., Gölgeci, I.: Enhancing green product and process innovation: towards an integrative framework of knowledge acquisition and environmental investment. Bus. Strateg. Environ. 30(2), 1283–1295 (2021). https://doi.org/10.1002/bse.2684

    Article  Google Scholar 

  74. Song, M., Wang, S., Sun, J.: Environmental regulations, staff quality, green technology, R&D efficiency, and profit in manufacturing. Technol. Forecast. Soc. Change 133(March), 1–14 (2018). https://doi.org/10.1016/j.techfore.2018.04.020

    Article  Google Scholar 

  75. Ahmad, S., Yew, K., Lang, M., Peng, W.: Resources, conservation & recycling sustainable product design and development : a review of tools, applications and research prospects. Resour. Conserv. Recycl. 132(January), 49–61 (2018). https://doi.org/10.1016/j.resconrec.2018.01.020

    Article  Google Scholar 

  76. Ahmad, S., Ahmad, S.: Green human resource management: policies and practices. Cogent Bus. Manag. (2015). https://doi.org/10.1080/23311975.2015.1030817

    Article  Google Scholar 

  77. Yousefnezhad, N., Malhi, A., Främling, K.: Security in product lifecycle of IoT devices: a survey. J. Netw. Comput. Appl. 171(June), 102779 (2020). https://doi.org/10.1016/j.jnca.2020.102779

    Article  Google Scholar 

  78. Saura, J.R., Ribeiro-Soriano, D., Palacios-Marqués, D.: Evaluating security and privacy issues of social networks based information systems in Industry 4.0. Enterp. Inf. Syst. 00(00), 1–17 (2021). https://doi.org/10.1080/17517575.2021.1913765

    Article  Google Scholar 

  79. Jamai, I., Ben Azzouz, L., Saidane, L. A.: Security issues in Industry 4.0,” 2020 Int. Wirel. Commun. Mob. Comput. IWCMC 2020, vol. 0, pp. 481–488, 2020, https://doi.org/10.1109/IWCMC48107.2020.9148447.

  80. Olabanji, O. M., Mpofu, K.: Appraisal of conceptual designs: coalescing fuzzy analytic hierarchy process (F-AHP) and Fuzzy Grey Relational Analysis (F-GRA). Elsevier B.V., 2020. https://doi.org/10.1016/j.rineng.2020.100194.

  81. Gegovska, T., Koker, R., Cakar, T.: Green supplier selection using fuzzy multiple-criteria decision-making methods and artificial neural networks. Comput. Intell. Neurosci. (2020). https://doi.org/10.1155/2020/8811834

    Article  Google Scholar 

  82. Buckley, J.J.: Fuzzy hierarchical analysis. Fuzzy Sets Syst. 17(3), 233–247 (1985). https://doi.org/10.1016/0165-0114(85)90090-9

    Article  MathSciNet  Google Scholar 

  83. Ilieva, G., Yankova, T., Klisarova-Belcheva, S.: Decision analysis with classic and fuzzy EDAS modifications. Comput. Appl. Math. 37(5), 5650–5680 (2018). https://doi.org/10.1007/s40314-018-0652-0

    Article  MathSciNet  Google Scholar 

  84. Lai, I.K.W., Shi, G.: The impact of privacy concerns on the intention for continued use of an integrated mobile instant messaging and social network platform. Int. J. Mob. Commun. 13(6), 641–669 (2015). https://doi.org/10.1504/IJMC.2015.072086

    Article  Google Scholar 

  85. Flatt, H., Schriegel, S., Jasperneite, J., Trsek, H., Adamczyk, H.: Analysis of the Cyber-Security of industry 4.0 technologies based on RAMI 4.0 and identification of requirements. IEEE Int. Conf. Emerg. Technol. Fact. Autom. ETFA, vol. 2016-Novem, (2016), https://doi.org/10.1109/ETFA.2016.7733634.

  86. Punia Sindhu, S., Nehra, V., Luthra, S.: Recognition and prioritization of challenges in growth of solar energy using analytical hierarchy process: Indian outlook. Energy 100, 332–348 (2016). https://doi.org/10.1016/j.energy.2016.01.091

    Article  Google Scholar 

  87. Pathak, S.K., Sharma, V., Chougule, S.S., Goel, V.: Prioritization of barriers to the development of renewable energy technologies in India using integrated Modified Delphi and AHP method. Sustain. Energy Technol. Assessments 50(November), 101818 (2020). https://doi.org/10.1016/j.seta.2021.101818

    Article  Google Scholar 

  88. Sandu, N., Gide E.: A model for successful adoption of cloud-based services in Indian SMEs. In: Proc. - 2019 Int. Conf. Futur. Internet Things Cloud, FiCloud 2019, pp. 169–174, 2019, https://doi.org/10.1109/FiCloud.2019.00031.

  89. Silva, S., Nuzum, A.K., Schaltegger, S.: Stakeholder expectations on sustainability performance measurement and assessment. a systematic literature review. J. Clean. Prod. 217, 204–215 (2019). https://doi.org/10.1016/j.jclepro.2019.01.203

    Article  Google Scholar 

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Conceptualization: Amber Batwara · Vikram Sharma · Mohit Makkar. Methodology: Amber Batwara. Writing: Amber Batwara. Supervision: Vikram Sharma · Mohit Makkar.

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Correspondence to Amber Batwara.

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Batwara, A., Sharma, V. & Makkar, M. Prioritization of the approaches for overcoming smart sustainable manufacturing barriers using stochastic fuzzy EDAS method. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-024-01891-2

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  • DOI: https://doi.org/10.1007/s12008-024-01891-2

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