Designing an Ecological Sustainability Assessment Model for the Large Oil Industry Supply Chain with a Fuzzy Cognitive Map

Subject Areas : Management

Shabnam Inanloo ¹ , Mahmood Modiri ^{2 *} , Kiamars Fathi hafshjani ³ , Mohammad ALli Afsharkazemi ⁴

1 - Department of Industrial Management, ST.C., Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, ST.C., Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, ST.C., Islamic Azad University, Tehran, Iran
4 - Department of Industrial Management, CT.C., Islamic Azad University, Tehran, Iran

Received: 2025-02-26 Accepted : 2025-07-01 Published : 2025-07-30

Keywords: Sustainable Supply Chain, Large Paradigm, Oil Industry, Fuzzy Cognitive Map. ,

Abstract :

Oil industry supply chain management faces environmental challenges to achieve sustainable development goals. For this purpose, the present study was conducted with the aim of using the large paradigm to help manage a sustainable supply chain in the oil industry. To achieve this goal, the present study was conducted in two qualitative and quantitative phases. In the qualitative part, the model indicators were identified and extracted using a data-driven strategy. In the quantitative part, a causal model was designed using a fuzzy cognitive map approach. The statistical population of the expert research included senior managers in the oil industry, who were selected through non-probability and purposeful sampling, numbering 14 people. The findings showed that all the model indicators are related to each other, such that the three indicators of increasing profitability, reverse logistics design, and reducing delay time have the greatest relationship with other indicators, respectively, which indicates the importance of these factors. It is expected that by designing appropriate reverse logistics and planning to reduce delay time, it is possible to increase profitability and improve the performance of sustainable supply chain management.

References:

Ajamzadeh, L., & Anvari, A. (2013). A review of the design of a combined model of large and sustainable supply chains Second National Conference on the Study of Strategies for Promoting Management, Accounting and Industrial Engineering in Organizations, Gachsaran, Islamic Azad University, Gachsaran Branch. [In Persian]
Alqudah, H. (2020). Impact of ERP System Usage on Supply Chain Integration: A Structural Equation Modeling, Jordanian Pharmaceutical Manufacturing Case Study. Journal of Economics and Business, 3(2).
Aminifar, Z., & Arabi, M. (2015). Sustainable supply chain management and the necessity of its study Conference on Modern Research in Industrial Management and Engineering. [In Persian]
Bottani, E., Bigliardi, B., & Rinaldi, M. (2022). Development and proposal of a LARG performance measurement system for a food supply chain. IFAC, 55(10), 2437-2444.
Carvalho, H., Azevedo, S. G., & Cruz-Machado, V. (2016). LARG index: a benchmarking tool for improving the leanness, agility, resilience and greenness of the automotive supply chain. An International Journal, 23(6), 1472-1499.
Ching, N., Ghobakhloo, M., Iranmanesh, M., Maroufkhani, P., & Asadi, S. (2022). Industry 4.0 applications for sustainable manufacturing: A systematic literature review and a roadmap to sustainable development. Journal of Cleaner Production, 334, 130133.
Dahlmann, F., & Roehrich, J. (2019). Sustainable supply chain management and partner engagement to manage climate change information. 28(8), 1632-1647.
Das, D. (2018). The impact of Sustainable Supply Chain Management practices on firm performance: Lessons from Indian organizations. Journal of Cleaner Production, 203, 179-196.
De Sousa, J., Alves, M., & Leocadio, L. (2019). Environmental management of Larg supply chain: A diagnostic instrument proposed for assessing suppliers. Brazilian Business Review, 16(6), 211-223.
Fahimnia, B., Sarkis, J., & Talluri, S. (2019). Editorial: Design and Management of Sustainable and Resilient Supply Chains. IEEE Transactions on Engineering Management, 66(1), 2-7. https://doi.org/10.1109/TEM.2018.2870924
Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., & Bello, R. (2019). A review on methods and software for fuzzy cognitive maps. Artificial intelligence review, 52, 1707-1737.
Florescu, M. S., Ceptureanu, E. G., Cruceru, A. F., & Ceptureanu, S. I. (2019). Sustainable supply chain management strategy influence on supply chain management functions in the oil and gas distribution industry. Energies, 12(9), 1632.
Garcia-Buendia, N., Moyano Fuentes, J., Maqueira-Marin, J., & Cobo, M. (2021). 22 years of lean supply chain management: a science mapping-based bibliometric analysis. International Journal of Production Research, 59(6), 1901-1921.
Ghorbanpour, A., & Azimi, Z. N. (2022). Application of green supply chain management in the oil industries: modeling and performance analysis. Materials Today: Proceedings(49), 542-553.
Goodarzian, F., Ghasemi, P., Santibanez Gonzalez, E., & Babaee Tirkolaee, E. (2023). A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms. Journal of Environmental Management(328), 116892.
Hasheminasab, H., & GholipouHammou, Y. (2018). Life cycle approach in sustainability assessment for petroleum refinery projects with fuzzy-AHP. Sage Journals, 29(7).
Hong, J., Zhang, Y., & Ding, M. (2018). Sustainable supply chain management practices, supply chain dynamic capabilities, and enterprise performance. Journal of Cleaner Production(20), 3508-3519.
Huseyin, I., Salih Zeki, I., Halit, K., Aliekber, A., & Mehmet, N. E. (2013). The Impact of ERP Systems and Supply Chain Management Practices on Firm Performance: Case of Turkish Companies. Procedia - Social and Behavioral Sciences(99), 1124 – 1133.
Kosko, B. (1986). Fuzzy Cognitive Maps. International Journal on Man– Machine Studies(24), 65-75. https://doi.org/10.1016/S0020-7373(86)80040-2
Kwon, H. B., & Lee, J. (2019). Exploring the differential impact of environmental sustainability, operational efficiency, and corporate reputation on market valuation in hightech-oriented firms. International Journal of Production Economics(211), 1-14.
Nozari, H., & Ghahramaneh Nahr, J. (2017). Providing a framework for implementing an agile supply chain based on big data. Innovation Management and Operational Strategies, 2(2), 128-136. [In Persian]
Olfat, L., Tafreshi Motlagh, A., Bamdad Sofi, J., & Amiri, M. (2017). The relationship model between lean/green supply chain and corporate sustainability. Iranian journal of management sciences, 11(44). [In Persian]
Popovic, T., Kraslawski, A., Barbosa-Póvoa, A., & Carvalho, A. (2017). Quantitative indicators of social sustainability assessment and product responsibility aspects of supply chains. Journal of International Studies, 10(4), 9-36.
Rahimi, K., Aqaqalizadeh-e-Siyar, A., & Izadiar, M. (2013). Presenting sustainable performance strategies in the automotive supply chain using fuzzy network analysis. Karafen, online publication. [In Persian]
Raut, R., Mangla, S. K., Narwane, V. S., Dora, M., & Liu, M. (2021). Big data analytics as a mediator in lean agile resilient and green (LARG) practices effect on sustainable supply chain. Transportation Research Part E(145), 102170.
Reyes, J., Mula, J., & Diaz-madronero, M. (2023). Development of a conceptual model for lean supply chain planning in industry 4.0 multidimensional analysis for operations management. Production Planning and Control, 34(12), 1209-1224.
Sahu, A. k., Raut, R. D., Gedam, V., Cheikhrouhou, N., & Sahu, A. K. (2022). Lean, agile, resilience, green practices adoption challenges in sustainable agri-food supply chain. Business Strategy and the Environment, 32(6), 3272-3291.
Sahu, A. K., Sharma, M., Raut, R. D., Sahu, A. K., Sahu, N. K., Antony, J., & Tortorella, G. L. (2023). Decision-making framework for supplier selection using an integrated MCDM approach in a lean-agile-resilient-green environment: evidence from Indian automotive sector. The TQM Journal, 35(4), 964-1006. https://doi.org/10.1108/TQM-12-2021-0372
Salleh, N., Rasidi, N., & Jeevan, J. (2020). lean, agile, resilience and green paradigm in supply chain operations: a trial a seaport system. Australian Journal of Maritime and Ocean Affairs, 12(4), 200-216.
Sedighpour, A., Zandiyeh, M., Alam Tabriz, A., & Dari, B. (2018). Design and Explanation of a Resilient Supply Chain Model in the Iranian Pharmaceutical Industry. Industrial management studies, 16(51), 55-106. [In Persian]
Shafiee, M., Zare Mehrjerdi, Y., & Keshavarz, M. (2021). Integration lean, agile, resilient and sustainable practices in supply chain network: matemathical moelling and the AUGMECON2 approach. International Journal of Systems Science: Operations and Logistics. https://doi.org/10.1080/23302674.2021.1921878
Sharma, M., Antony, R., & Tsagarakis, K. (2023). Green, resilient, agile, lean and sustainable fresh food supply chain enablers: evidence from India. Annals of Operations Research, 14(5), 774-791.
Sharma, V., Raut, R., Mangla, S. K., Narkhede, B. E., Luthra, S., & Gokhale, R. (2021). A systematic literature review to integrate lean agile resilient green and sustainable paradigms in the supply chain. Business Strategy and the Environment(30), 1191-1212.
Sonar, H., Mukherjee, A., Gunasekaran, A., & Singh, R. (2022). Sustainable supply chain management of automotive sector in context to the circular economy. Business Strategy and the Environment, 31(7), 3635-3648.
Suifan, T., Alazab, M., & Alhyari, S. (2019). Trade-off among lean, agile, resilient and green paradigms: an empirical study on pharmaceutical industry in Jordan using a TOPSIS-entropy method. 69-101.
Thomé, K. M., Cappellesso, G., Ramos, E. L. A., & de Lima Duarte, S. C. (2020). Food supply chains and short food supply chains: coexistence conceptual framework. Journal of Cleaner Production(317), 123207.
Tseng, C., & Kiang, Y. J. (2024). Optimizing Supply Chain Sustainability through AI-Driven Policies and Integrator Facility. International Journal of Supply and Operations Management, 2383-1359. https://doi.org/10.22034/ijsom.2024.110137.2911
Wu, K., Liao, C., Tseng, M., & Jiayao, H. (2017). Toward sustainability: using big data to explore the decisive attributes of supply chain risks and uncertainties. Journal of Cleaner Production, 142, 663-676.