Identification and Quantitative Analysis of Key Sustainability Indicators for Developing a Sustainable Closed-Loop Supply Chain Network for Nylon Yarn Product Using a Fuzzy Approach

Authors

Keywords:

Sustainable Closed, Loop Supply Chain, Fuzzy Delphi, Sustainability Indicators, Nylon Yarn, Economic Sustainability, Environmental Sustainability, Social Sustainability

Abstract

This study aimed to identify and validate the key economic, environmental, and social sustainability indicators relevant to the nylon yarn supply chain in the rubber and plastics industry using expert consensus and the fuzzy Delphi technique. This applied study employed a quantitative expert-based design using the fuzzy Delphi method. An initial pool of sustainability indicators was developed through an extensive review of the theoretical literature, previous empirical studies, and preliminary expert screening. Twenty-three experts with postgraduate education and substantial academic or managerial experience in industrial management, industrial engineering, supply chain management, production planning, procurement, distribution, and related areas were selected purposively. The preliminary indicators were classified into economic, environmental, and social dimensions and evaluated during successive Delphi rounds. Expert linguistic judgments were converted into triangular fuzzy numbers, aggregated using the geometric mean, and defuzzified to obtain crisp scores. Indicators with crisp values of at least 7.00 were retained. Stability of expert judgments between rounds was assessed using the absolute difference between defuzzified values, with values of 0.20 or less indicating sufficient consensus. Of the 26 initial sustainability indicators, 23 were retained after the first fuzzy Delphi round and three were excluded. In the second round, four additional indicators were removed, resulting in a final framework of 19 validated indicators comprising 10 economic, three environmental, and six social indicators. The highest second-round score was obtained for occupational accidents and work-related diseases (8.38), followed by variable water-consumption cost and number of rewarded employees (8.23 each). All retained indicators showed an absolute inter-round difference below 0.20, indicating stable expert consensus and eliminating the need for a third Delphi round. The study produced a concise, expert-validated, and context-specific sustainability indicator framework for the nylon yarn supply chain, demonstrating that sustainable performance in this industry requires simultaneous attention to economic efficiency, environmental resource use, occupational health and safety, employment, and human resource development.

References

Abir, A. S., Bhuiyan, I. A., Arani, M., & Billal, M. M. (2020). Multi-Objective Optimization for Sustainable Closed-Loop Supply Chain Network Under Demand Uncertainty: A Genetic Algorithm. arXiv preprint arXiv:2009.06047. https://doi.org/10.1109/ICDABI51230.2020.9325648

Ala, A., Goli, A., Mirjalili, S., & Simic, V. (2024). A fuzzy multi-objective optimization model for sustainable healthcare supply chain network design. Applied Soft Computing, 150, 111012. https://doi.org/10.1016/j.asoc.2023.111012

Azadi, M., Yousefi, S., Farzipoor Saen, R., Shabanpour, H., & Jabeen, F. (2023). Forecasting sustainability of healthcare supply chains using deep learning and network data envelopment analysis. Journal of Business Research, 154, 113357. https://doi.org/10.1016/j.jbusres.2022.113357

Bamakan, S. M. H., Malekinejad, P., Ziaeian, M., & Motavali, A. (2021). Bullwhip effect reduction map for COVID-19 vaccine supply chain. Sustainable Operations and Computers, 2, 139-148. https://doi.org/10.1016/j.susoc.2021.07.001

Bastas, A., & Liyanage, K. (2018). Sustainable supply chain quality management: A systematic review. Journal of Cleaner Production, 181, 726-744. https://doi.org/10.1016/j.jclepro.2018.01.110

Gholizadeh, H., Jahani, H., Abareshi, A., & Goh, M. (2021). Sustainable closed-loop supply chain for dairy industry with robust and heuristic optimization. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2021.107324

Gu, Y., Xue, M., Zhao, M., & Long, Y. (2023). Optimal Government Subsidy Decision and Its Impact on Sustainable Development of a Closed-Loop Supply Chain. Systems, 11(7), 378. https://doi.org/10.3390/systems11070378

Hasani, A., Mokhtari, H., & Fattahi, M. (2021). An optimization approach for sustainable and resilient supply chain design with regional considerations. Computers & Industrial Engineering, 158, 107414. https://doi.org/10.1016/j.cie.2021.107414

He, L., Wu, Z., Xiang, W., Goh, M., Xu, Z., Song, W., Ming, X., & Wu, X. (2021). A novel Kano-QFD-DEMATEL approach to optimise the risk resilience solution for sustainable supply chain. International Journal of Production Research, 59(6), 1714-1735. https://doi.org/10.1080/00207543.2020.1724343

Jafari Eskandari, M., & Emami Salout, H. (2022). A multi-objective optimization model for risk assessment in a sustainable closed-loop supply chain under parameter uncertainty: A conditional value-at-risk (CVaR) approach. Industrial Management Studies, 20(66), 251-298.

Khalili-Fard, A., Sabouhi, F., & Bozorgi-Amiri, A. (2024). Data-Driven Robust Optimization for a Sustainable Steel Supply Chain Network Design: Toward the Circular Economy. Computers & Industrial Engineering, 195, 110408. https://doi.org/10.1016/j.cie.2024.110408

Li, C., Zhang, F., Cao, C., Liu, Y., & Qu, T. (2019). Organizational coordination in sustainable humanitarian supply chain: An evolutionary game approach. Journal of Cleaner Production, 219, 291-303. https://doi.org/10.1016/j.jclepro.2019.01.233

Liu, J., Xi, Y., & Wang, J. (2023). Resilience strategies for sustainable supply chains under budget constraints in the post COVID-19 era. Frontiers of Engineering Management, 10(1), 143-157. https://doi.org/10.1007/s42524-022-0236-y

Madani, B., Saihi, A., & Abdelfatah, A. (2024). A Systematic Review of Sustainable Supply Chain Network Design: Optimization Approaches and Research Trends. Sustainability, 16(8), 3226. https://doi.org/10.3390/su16083226

Mahapatra, A. S., Sengupta, S., Dasgupta, A., Sarkar, B., & Goswami, R. T. (2025). What is the impact of demand patterns on integrated online-offline and buy-online-pickup in-store (BOPS) retail in a smart supply chain mana gement? Journal of Retailing and Consumer Services, 82, 104093. https://doi.org/10.1016/J.JRETCONSER.2024.104093

Ravansh, M. (2021). Multi-Objective Optimization for Designing a Closed-Loop Sustainable Supply Chain Network Under Flexibility Conditions. 1st National Conference on Optimization and Modern Problem-Solving Methods, Nahavand.

Rostamzadeh, R., Develi, E. I., Isavi, H., Saparauskas, J., Turskis, Z., & Ghorbani, S. (2025). Multi-product, multi-period sustainable perishable supply chain optimization with uncertainty navigation. Journal of Competitiveness, 17(2).

Sadeghi Goughari, B., Eskandari Damaneh, H., Dehghan, M. H., & Rezaei, H. R. (2024). Examining Risk Management Areas, Modeling, and Optimization of Sustainable Agricultural Supply Chain. 3rd National Conference on Quality Management and Reliability Engineering,

Shafiei, F., Kazemi, A., Jafarnjad Chaghoshi, A., Sazvar, Z., & Amouzadeh Mahdiraji, H. (2020). Presenting a Robust Optimization Model for Sustainable Supply Chains of Perishable Dairy Products. Production and Operations Management, 11(3). https://jpom.ui.ac.ir/article_25518.html?lang=en

Yousefi-Babadi, A., Bozorgi-Amiri, A., Tavakkoli-Moghaddam, R., & Govindan, K. (2023). Redesign of the sustainable wheat-flour-bread supply chain network under uncertainty: An improved robust optimization. Transportation Research Part E: Logistics and Transportation Review, 176, 103215. https://doi.org/10.1016/j.tre.2023.103215

Zhang, X., & Yousaf, H. M. A. U. (2020). Green supply chain coordination considering government intervention, green investment, and customer green preferences in the petroleum industry. Journal of Cleaner Production, 246, 118984. https://doi.org/10.1016/j.jclepro.2019.118984

Downloads

Publication Timeline

Published
Submitted
Revised
Accepted

How to Cite

Siavashi, Reza ., Shahbandarzadeh, H., Rajabzadeh Ghatari, A. ., & Heidari, E. . (2024). Identification and Quantitative Analysis of Key Sustainability Indicators for Developing a Sustainable Closed-Loop Supply Chain Network for Nylon Yarn Product Using a Fuzzy Approach. Journal of Resource Management and Decision Engineering, 3(3), 1-18. https://www.journalrmde.com/index.php/jrmde/article/view/459