[1] Chienwattanasook, K., Tancho, N., Onputtha, S., Boonrattanakittibhumi, C., Sriyakul, T., & Waiyawuththanapoom, P. (2022). The role of industry 4.0 in supply chain sustainability: Evidence from the rubber industry. Uncertain supply chain management, 10(4), 1243–1252. https://doi.org/10.5267/j.uscm.2022.8.004
[2] Veile, J. W., Schmidt, M. C., Müller, J. M., & Voigt, K. I. (2024). The transformation of supply chain collaboration and design through Industry 4.0. International journal of logistics research and applications, 27(6), 986–1014. https://doi.org/10.1080/13675567.2022.2148638
[3] Hrouga, M. (2024). Towards a new conceptual digital collaborative supply chain model based on Industry 4.0 technologies: A conceptual framework. International journal of quality and reliability management, 41(2), 628–655. https://doi.org/10.1108/IJQRM-07-2022-0221
[4] Goswami, S. S., & Mondal, S. (2024). The role of 5G in enhancing IOT connectivity: A systematic review on applications, challenges, and future prospects. Big data and computing visions, 4(4), 314–325. https://doi.org/10.22105/bdcv.2024.486159.1215
[5] Dutta, G., Kumar, R., Sindhwani, R., & Singh, R. K. (2021). Digitalization priorities of quality control processes for SMEs: A conceptual study in perspective of Industry 4.0 adoption. Journal of intelligent manufacturing, 32(6), 1679–1698. https://doi.org/10.1007/s10845-021-01783-2
[6] Ajalli, M. (2024). Conceptual modeling of determining factors in the assessment of sustainability and resilience of the supply chain: a study of rubber industry suppliers in Iran. Journal of rubber research, 27(2), 259–274. https://doi.org/10.1007/s42464-024-00257-3
[7] Ajalli, M., Azimi, H., & Zamani, F. (2024). The impact of blockchain technology on the internet of things in the fourth industrial revolution: Applications, opportunities, and challenges. Modern research in performance evaluation, 3(1), 60–73. (In Persian). https://doi.org/10.22105/mrpe.2024.201235
[8] Ebrahimi, M. (2024). Determining and ranking the indicators of evaluating the performance of wood industry-employees. Journal of applied research on industrial engineering, 11(2), 166–178. https://doi.org/10.22105/jarie.2024.388802.1534
[9] Yadav, A., Sachdeva, A., Garg, R. K., Qureshi, K. M., Mewada, B. G., Al-Qahtani, M. M., & Qureshi, M. R. N. M. (2024). Challenges of blockchain adoption for manufacturing supply chain to achieve sustainability: A case of rubber industry. Heliyon, 10(20), e39448. https://doi.org/10.1016/j.heliyon.2024.e39448
[10] Pachouri, V., Singh, R., Gehlot, A., Pandey, S., Vaseem Akram, S., & Abbas, M. (2024). Empowering sustainability in the built environment: A technological Lens on industry 4.0 Enablers. Technology in society, 76, 102427. https://doi.org/10.1016/j.techsoc.2023.102427
[11] Omrany, H., Al-Obaidi, K. M., Husain, A., & Ghaffarianhoseini, A. (2023). Digital Twins in the construction industry: A comprehensive review of current implementations, enabling technologies, and future directions. Sustainability, 15(14), 10908. https://doi.org/10.3390/su151410908
[12] da Silva, T. H. H., & Sehnem, S. (2024). The utilization of Industry 4.0 technologies to enhance the circular economy through the engagement of stakeholders in Brazilian food technology companies. Business strategy and the environment, 34(1), 129–161. https://doi.org/10.1002/bse.3954
[13] Bosman, L., Hartman, N., & Sutherland, J. (2020). How manufacturing firm characteristics can influence decision making for investing in Industry 4.0 technologies. Journal of manufacturing technology management, 31(5), 1117–1141. https://doi.org/10.1108/JMTM-09-2018-0283
[14] Rezaei Manesh, B., Hamzavi, H., & Hosseini, S. M. (2025). Identifying and prioritizing drivers for optimizing sustainable performance of oil, gas, and petrochemical organizations with a futures research approach. Modern research in performance evaluation, 4(1), 11–26. (In Persian). https://doi.org/10.22105/mrpe.2025.506185.1146
[15] Heravi, A., Zamani Moghadam, A., Hashemi, S. A., Vakil Alroaia, Y., & Sajadi Jagharg, A. (2023). Evaluation of the influential factors in human resource development in state-owned enterprises using a mixed method. Journal of applied research on industrial engineering, 10(2), 238–255. https://doi.org/10.22105/jarie.2021.284052.1322
[16] Czukor, G., Dinçer, H., Eti, S., Yüksel, S., & Pamucar, D. (2025). Integrating artificial intelligence into fuzzy decision analytics: A novel approach to mitigating stereotype threat in sustainable business environments. Journal of fuzzy extension and applications, 6(2), 371–390. https://doi.org/10.22105/jfea.2025.480001.1641
[17] Siraj, M. T., Haque, M. R., Chowdhury, M. S., Islam, N., Biswas, B., & Chowdhury, M. K. H. (2024). Analyzing challenges in enterprise resource planning (ERP) implementation in a safety inspection company: An IVT2IF DEMATEL approach. Optimality, 1(2), 205–223. https://doi.org/10.22105/opt.v1i2.50
[18] Bayatzadeh, S. and Amiri, M. (2024). Identifying and evaluating supplier selection criteria in Iran’s steel industry according to Industry 4.0 technologies. Innovation management and operational strategies, 5(3), 306-330. (In Persian). https://doi.org/10.22105/imos.2024.472776.1379
[19] Thakur, R., Raj, M., & Malik, S. C. (2025). Correlation coefficient measures for probabilistic single valued neutrosophic hesitant fuzzy sets and its application in supply chain management. Journal of fuzzy extension and applications, 6(2), 391–409. https://doi.org/10.22105/jfea.2025.475283.1602
[20] Adolph Groenewald, C., Garg, A., Santosh Yerasuri, S., Sharma, H., & Sibonghanoy Groenewald, E. (2024). Smart supply chain management optimization and risk mitigation with artificial intelligence. Naturalista campano, 28(1), 261–270. https://museonaturalistico.it/index.php/journal/article/view/72
[21] Ekram Nosratian, N., & Taghavi Fard, M. T. (2023). A proposed model for the assessment of supply chain management using DEA and knowledge management. Computational algorithms and numerical dimensions, 2(3), 136–147. https://doi.org/10.22105/cand.2023.191008
[22] Montazeri, F. Z., Sorourkhah, A., Marinković, D., & Lukovac, V. (2024). Robust-fuzzy-probabilistic optimization for a resilient, sustainable supply chain with an inventory management approach by the seller. Big data and computing visions, 4(2), 146–163. https://doi.org/10.22105/bdcv.2024.481945.1208
[23] Shafique, M. N., Yeo, S. F., & Tan, C. L. (2024). Roles of top management support and compatibility in big data predictive analytics for supply chain collaboration and supply chain performance. Technological forecasting and social change, 199, 123074. https://doi.org/10.1016/j.techfore.2023.123074
[24] Silva, B. E., Vieira, J. G. V., & Yoshizaki, H. (2024). Motivating factors for blockchain technology adoption: A theoretical analysis from the perspective of supply chain collaboration. Journal of global operations and strategic sourcing, 18(1), 36–63. https://doi.org/10.1108/JGOSS-04-2023-0033
[25] Kunkel, S., Matthess, M., Xue, B., & Beier, G. (2022). Industry 4.0 in sustainable supply chain collaboration: Insights from an interview study with international buying firms and Chinese suppliers in the electronics industry. Resources, conservation and recycling, 182, 106274. https://doi.org/10.1016/j.resconrec.2022.106274
[26] Patrucco, A., Moretto, A., Trabucchi, D., & Golini, R. (2022). How do Industry 4.0 Technologies boost collaborations in buyer-supplier relationships? Research technology management, 65(1), 48–58. https://doi.org/10.1080/08956308.2021.1999131
[27] Attaran, S., Attaran, M., & Celik, B. G. (2024). Digital twins and industrial internet of things: Uncovering operational intelligence in industry 4.0. Decision analytics journal, 10, 100398. https://doi.org/10.1016/j.dajour.2024.100398
[28] Van Der Valk, H., Strobel, G., Winkelmann, S., Hunker, J., & Tomczyk, M. (2022). Supply chains in the era of digital twins - A review. Procedia computer science, 204, 156–163. https://doi.org/10.1016/j.procs.2022.08.019
[29] Sedky, A. (2023). Digital supply chain: A proposed solution to the global supply chain disruption impact on business sustainability. In Digital supply chain, disruptive environments, and the impact on retailers (pp. 160–177). IGI Global. https://doi.org/10.4018/978-1-6684-7298-9.ch009
[30] Itiat, N. E., Ekanem, I. I., & Ikpe, A. E. (2024). A comprehensive review on green energy technologies: An approach for environmental sustainability and eco-friendliness. Systemic analytics, 2(2), 315–334. https://doi.org/10.31181/sa22202427
[31] Bayatzadeh, S. and Talaie, H. (2024). Identifying and evaluating of sustainable and resilient supplier selection criteria according to industry 5.0 concepts (Case study: steel industry). Journal of decisions and operations research, 9(4), 1045-1063. (In Persian). https://doi.org/10.22105/dmor.2025.498734.1904
[32] Esmaeilian, G., Fardoie, R. S., Hourali, M., & Farbod, E. (2024). Investigating the impact of blockchain technology adoption on integration and economic sustainability of the automotive supply chain: A bayesian structural equation modeling approach. Transactions on quantitative finance and beyond, 1(1), 1–14. https://doi.org/10.22105/tqfb.v1i1.16
[33] Rezaei, A., Zare Mehrjerdi, Y., Owlia, M. S., & Khademizare, H. (2023). Identifying and prioritizing ecosystem indicatorsusing Fuzzy Delphi and Fuzzy DEMATEL approaches. Research in production and operations management, 14(2), 51-82. (In Persian). https://doi.org/10.22108/pom.2023.134879.1462
[34] Yurtay, Y. (2025). Carbon footprint management with Industry 4.0 technologies and erp systems in sustainable manufacturing. Applied sciences, 15(1). https://doi.org/10.3390/app15010480
[35] Labaran, M. J., & Masood, T. (2023). Industry 4.0 driven green supply chain management in renewable energy sector: A critical systematic literature review. Energies, 16(19), 6977. https://doi.org/10.3390/en16196977
[36] Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tseng, M. L., Nikbin, D., & Khanfar, A. A. A. (2025). Industry 4.0 digital transformation and opportunities for supply chain resilience: A comprehensive review and a strategic roadmap. Production planning & control, 36(1), 61–91. https://doi.org/10.1080/09537287.2023.2252376
[37] Mosleuzzaman, M., Arif, I., & Siddiki, A. (2024). Design and development of a smart factory using Industry 4.0 technologies. Academic journal on business administration, innovation & sustainability, 4(4). https://doi.org/10.69593/ajbais.v4i04.131
[38] Ismail, M. M., Ahmed, Z., Abdel-Gawad, A. F., & Mohamed, M. (2024). Toward supply chain 5.0: An integrated multi-criteria decision-making models for sustainable and resilience enterprise. Decision making: applications in management and engineering, 7(1), 160–186. https://doi.org/10.31181/dmame712024955
[39] Özek, A., & Yildiz, A. (2020). Digital supplier selection for a garment business using interval type-2 fuzzy TOPSIS. Tekstil ve konfeksiyon, 30(1), 61–72. https://doi.org/10.32710/tekstilvekonfeksiyon.569884
[40] Liu, L., Ang, S., Yang, F., & Zhang, X. (2025). Partner selection for supply chain collaboration: New data envelopment analysis models. Omega (United Kingdom), 132, 103245. https://doi.org/10.1016/j.omega.2024.103245
[41] Zhou, J., Xu, T., Chiao, Y., & Fang, Y. (2024). Interorganizational systems and supply chain agility in uncertain environments: The mediation role of supply chain collaboration. Information systems research, 35(1), 184–202. https://doi.org/10.1287/isre.2023.1210
[42] Queiroz, M. M., Telles, R., & Bonilla, S. H. (2020). Blockchain and supply chain management integration: A systematic review of the literature. Supply chain management, 25(2), 241–254. https://doi.org/10.1108/SCM-03-2018-0143
[43] Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57. https://doi.org/10.1016/j.omega.2014.11.009
[44] Mi, X., Tang, M., Liao, H., Shen, W., & Lev, B. (2019). The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega (United Kingdom), 87, 205–225. https://doi.org/10.1016/j.omega.2019.01.009
[45] Guo, S., & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-based systems, 121, 23–31. https://doi.org/10.1016/j.knosys.2017.01.010
[46] Ünver, M., & Aydoğan, B. (2025). Optimized climate change management: Integrating fuzzy CRITIC-TOPSIS approach with continuous function-valued fuzzy sets. Risk assessment and management decisions, 2(1), 1–27. https://doi.org/10.48314/ramd.vi.34
[47] Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega (United Kingdom), 64, 126–130. https://doi.org/10.1016/j.omega.2015.12.001
[48] Mohseni Andargoli, S. , Arasteh, A. and Divsalar, A. (2024). A novel hybrid approach for smart locker location in delivery systems: A solution for enhancing urban logistics performance (Case study: Babol county). Journal of decisions and operations research, 9(4), 1021-1044. (In Persian). https://doi.org/10.22105/dmor.2025.489345.1890
[49] Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making: methods and applications a state-of-the-art survey (pp. 58–191). Springer. https://doi.org/10.1007/978-3-642-48318-9_3
[50] Sulistiani, H., Pasaribu, A., Palupiningsih, P., Anwar, K., & Saputra, V. H. (2024). New TOPSIS: Modification of the TOPSIS method for objective determination of weighting. International journal of intelligent engineering and systems, 17(5), 991–1003. https://doi.org/10.22266/ijies2024.1031.74
