تحلیل و رتبه‌بندی عوامل کلیدی موفقیت در ادغام هوش مصنوعی و اقتصاد چرخشی برای تحقق زنجیره تأمین هوشمند و پایدار با رویکرد دیمتل

نوع مقاله : پژوهشی

نویسندگان
1 دانشیار گروه مدیریت، دانشکده علوم انسانی، دانشگاه میبد، میبد، ایران.
2 دانشجوی کارشناسی ارشد مدیریت عملکرد، گروه مدیریت، دانشگاه میبد، میبد، ایران
چکیده
این مقاله درصدد شناسایی و تحلیل عوامل کلیدی موفقیت (CSFs) در ادغام هوش مصنوعی (AI) و اقتصاد چرخشی (CE) در بستر زنجیره تأمین هوشمند (SSC) و پایدار است. این پژوهش از نظر هدف، کاربردی و از نظر روش گردآوری داده‌ها، توصیفی-پیمایشی است. داده‌ها از طریق مطالعات کتابخانه‌ای و نظرسنجی از خبرگان صنعت و دانشگاه گردآوری و با تکنیک دیمتل تحلیل شدند. یافته‌های پژوهش نشان دادند که عوامل حیاتی موفقیت در ادغام هوش مصنوعی و اقتصاد چرخشی در زنجیره تأمین هوشمند و پایدار در پنج بُعد مدیریتی، فناوری، همکاری شبکه‌ای، فرآیندی و پایداری شناسایی شدند. نتایج تحلیل دیمتل بیانگر آن است که ابعاد مدیریتی و فناوری به‌عنوان عوامل علّی و اثرگذار اصلی شناخته می‌شوند. در مقابل، ابعاد فرآیندی و پایداری بیشترین میزان تأثیرپذیری را از سایر حوزه‌ها دارند. همچنین بعد همکاری شبکه‌ای با بالاترین مقدار ارتباط کلی، نقش واسطه‌ای کلیدی میان ابعاد مدیریتی و پایداری ایفا می‌کند. نتایج پژوهش منجر به ارائه یک چارچوب مفهومی تلفیقی شد که نشان می‌دهد ادغام هوش مصنوعی و اقتصاد چرخشی از طریق تقویت ابعاد مدیریتی، فناورانه و همکاری شبکه‌ای می‌تواند به بهینه‌سازی فرآیندها، ارتقای پایداری و ایجاد زنجیره تأمین هوشمند و تاب‌آور منجر شود. این چارچوب، ارتباط علّی و ساختاری میان عوامل کلیدی موفقیت را تبیین کرده و مسیر تحقق توسعه پایدار و مزیت رقابتی پایدار را برای سازمان‌ها فراهم می‌سازد.

کلیدواژه‌ها

موضوعات


عنوان مقاله English

Analyzing and Ranking of Critical Success Factors in the Integration of Artificial Intelligence and Circular Economy for Achieving a Smart and Sustainable Supply Chain With the DEMATEL Approach

نویسندگان English

Mohammad Zarei Mahmoudabadi 1
Amirmahdi Torkzaban 2
1 Department of Management, Faculty of Humanities, Meybod University, Meybod, Iran.
2 MSc. Student of Performance Management, Department of Management, Meybod University, Meybod, Iran
چکیده English

The purpose of this paper is to identify and analyze the Critical Success Factors (CSFs) in the integration of Artificial Intelligence (AI) and Circular Economy (CE) within the context of Smart and Sustainable Supply Chains (SSC). This research is applied in nature, with a descriptive-survey approach for data collection. The data were gathered through bibliographic studies and surveys from industry experts and academic professionals, and analyzed using the DEMATEL technique. The Critical Success Factors in the integration of AI and Circular Economy in Smart and Sustainable Supply Chains were identified across five dimensions: managerial, technological, network collaboration, process, and sustainability. The DEMATEL analysis revealed that managerial and technological dimensions are recognized as the primary causal and impactful factors, while process and sustainability dimensions are most affected by other areas. Furthermore, the network collaboration dimension plays a key mediating role between managerial and sustainability dimensions, with the highest overall correlation. The results led to the development of an integrated conceptual framework, demonstrating that the integration of AI and Circular Economy, through enhancing the managerial, technological, and network collaboration dimensions, can optimize processes, promote sustainability, and lead to the creation of a smart and resilient supply chain. This framework elucidates the causal and structural relationships between the critical success factors and provides a pathway for achieving sustainable development and competitive advantage for organizations.

کلیدواژه‌ها English

Artificial Intelligence
Circular Economy
Key Success Factors
Smart and Sustainable Supply Chain
Industry 4.0
Acerbi, F., Forterre, D. A., & Taisch, M. (2021). Role of Artificial Intelligence in Circular Manufacturing: A Systematic Literature Review. IFAC-PapersOnLine, 54(1), 367–372. doi: https://doi.org/10.1016/j.ifacol.2021.08.040
Agi, M. A. N. (2022). Understanding the Enablers of Blockchain Technology Adoption in Sustainable Supply Chains: A DEMATEL-Based Analysis. IFAC-PapersOnLine, 55(10), 1962–1967. doi: https://doi.org/10.1016/j.ifacol.2022.09.686
Al Maazmi, A., Piya, S., & Araci, Z. C. (2024). Exploring the Critical Success Factors Influencing the Outcome of Digital Transformation Initiatives in Government Organizations. Systems, 12(12), 524. https://doi.org/10.3390/systems12120524
Aladaileh, M. J., Lahuerta-Otero, E., & Aladayleh, K. J. (2024). Mapping sustainable supply chain innovation: A comprehensive bibliometric analysis. Heliyon, 10(7), e29157. doi:10.1016/j.heliyon.2024.e29157 https://www.cell.com/action/showPdf?pii=S2405-8440%2824%2905188-0
Asher, S., Nafees, M., & Syeda, T. (2024). Exploring the change management framework: An in-depth investigation. MethodsX, 13, 102978. doi:https://doi.org/10.1016/j.mex.2024.102978
Bashynska, I., Malynovska, Y., Kolinko, N., Bielialov, T., Järvis, M., Kovalska, K., & Saiensus, M. (2024). Performance Assessment of Sustainable Leadership of Enterprise’s Circular Economy-Driven Innovative Activities. Sustainability, 16(2), 558. Retrieved from https://www.mdpi.com/2071-1050/16/2/558
Bashynska, I., & Prokopenko, O. (2024). Leveraging Artificial Intelligence for Circular Economy: Transforming Resource Management, Supply Chains, and Manufacturing Practices. Scientific Journal of Bielsko-Biala School of Finance and Law, 28(2), 85–91. file:///C:/Users/SabaNet/Downloads/wsfip.sj2.2024.13.pdf  doi:10.19192/wsfip.sj2.2024.13
Bimpizas-Pinis, M., Calzolari, T., & Genovese, A. (2022). Exploring the transition towards circular supply chains through the arcs of integration. International Journal of Production Economics, 250, 108666. doi: https://doi.org/10.1016/j.ijpe.2022.108666
Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299–312. doi:https://doi.org/10.1016/j.ejor.2013.09.032
Brintrup, A., Kosasih, E., Schaffer, P., Zheng, G., Demirel, G., & MacCarthy, B. L. (2024). Digital supply chain surveillance using artificial intelligence: definitions, opportunities and risks. International Journal of Production Research, 62(13), 4674–4695. https://doi.org/10.1080/00207543.2023.2270719
Caiado, R. G., Scavarda, L. F., Azevedo, B. D., de Mattos Nascimento, D. L., & Quelhas, O. L. (2022). Challenges and Benefits of Sustainable Industry 4.0 for Operations and Supply Chain Management—A Framework Headed toward the 2030 Agenda. Sustainability, 14(2). https://doi.org/10.3390/su14020830
Chalmeta, R., & Santos-deLeón, N. (2020). Sustainable Supply Chain in the Era of Industry 4.0 and Big Data: A Systematic Analysis of Literature and Research. Sustainability, 12, 4108. https://doi.org/10.3390/su12104108
Chen, L., Shen, Q., Yu, X., & Chen, X. (2024). Knowledge spillovers along the sustainable supply chain of China's listed companies: The role of long-term orientation. Journal of Innovation and Knowledge, 9(2). https://doi.org/10.1016/j.jik.2024.100478
Chiu, Y.-J., Chen, H.-C., Tzeng, G.-H., Shyu, J., & Shyu, G. H. (2006). Marketing strategy based on customer behaviour for the LCD-TV. Int. J. Management and Decision Making J.Z. Int. J. Management and Decision Making, 7, 143–165. doi:10.1504/IJMDM.2006.009140 https://www.researchgate.net/profile/Gwo-Hshiung-Tzeng 2/publication/228633301_Marketing_strategy_based_on_customer_behaviour_for_the_LCD-TV/links/0f3175316c66fb957d000000/Marketing-strategy-based-on-customer-behaviour-for-the-LCD-TV.pdf
Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. doi: https://doi.org/10.1016/j.compind.2024.104132
Demir, S., Gunduz, M. A., Kayikci, Y., & Paksoy, T. (2023). Readiness and Maturity of Smart and Sustainable Supply Chains: A Model Proposal. Engineering Management Journal, 35(2), 181–206. https://shura.shu.ac.uk/29993/1/Kayikci-ReadinessMaturitySmart%28AM%29.pdf https://shura.shu.ac.uk/29993/
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small- and medium-sized enterprises. International Journal of Production Research, 62(15), 5417–5456. https://pure.hud.ac.uk/ws/files/56281974/Accepted_manuscript.pdf doi:10.1080/00207543.2023.2179859
Dwivedi, A., Sassanelli, C., Agrawal, D., Moktadir, M. A., & D'Adamo, I. (2023). Drivers to mitigate climate change in context of manufacturing industry: An emerging economy study. Business Strategy and the Environment, 32(7), 4467–4484. doi:https://doi.org/10.1002/bse.3376
Falah, N., Falah, N., Solis-Guzman, J., & Meléndez, M. M. (2025). Contribution of circular economy levels to sustainable development goals: Literature review based on natural language processing techniques. Sustainable Futures, 10, 101011. doi:https://doi.org/10.1016/j.sftr.2025.101011
Fatimah, Y. A., Govindan, K., Sasongko, N. A., & Hasibuan, Z. A. (2024). The critical success factors for sustainable resource management in circular economy: Assessment of urban mining maturity level. Journal of Cleaner Production, 469, 143084. doi:https://doi.org/10.1016/j.jclepro.2024.143084
Francisco, M., & Linnér, B.-O. (2023). AI and the governance of sustainable development. An idea analysis of the European Union, the United Nations, and the World Economic Forum. Environmental Science & Policy, 150, 103590. doi:https://doi.org/10.1016/j.envsci.2023,103590
Gabus, A., & Fontela, E. (1973). Perceptions of the world problematique: communication procedure, communicating with those bearing collective responsibility. https://www.scirp.org/reference/referencespapers?referenceid=1429249
Gaur, T. S., Yadav, V., Prakash, S., & Mittal, S. (2025). Investigating the key challenges in adopting Supply Chain 4.0 in the context of Indian electronics industry: a DEMATEL approach. Journal of Global Operations and Strategic Sourcing. https://doi.org/10.1108/JGOSS-02-2025-0010 doi:10.1108/jgoss-02-2025-0010
Geissdoerfer, M., Morioka, S. N., de Carvalho, M. M., & Evans, S. (2018). Business models and supply chains for the circular economy. Journal of Cleaner Production, 190, 712–721. doi: https://doi.org/10.1016/j.jclepro.2018.04.159
Georgescu, L. P., Fortea, C., Antohi, V. M., Balsalobre-Lorente, D., Zlati, M. L., & Barbuta–Misu, N. (2025). Economic, technological and environmental drivers of the circular economy in the European Union: a panel data analysis. Environmental Sciences Europe, 37(1), 76. https://link.springer.com/content/pdf/10.1186/s12302-025-01119-4.pdf doi:10.1186/s12302-025-01119-4
Ghanbari, M. (2023). Presenting the smart- sustainable supply chain model based on artificial intelligence. International Journal of Innovation in Management, Economics and Social Sciences, 3, 61–70. https://doi.org/10.59615/ijimes.3.4.61
Goyal, S., & Gupta, S. (2024). A comprehensive review of current techniques, issues, and technological advancements in sustainable E-waste management. e-Prime - Advances in Electrical Engineering, Electronics and Energy, 9, 100702. doi:https://doi.org/10.1016/j.prime.2024.100702
Hernandez, J. C. R., Villa-Enciso, E., Cardona-Acevedo, S., Valencia, J., & Velasquez Salas, S. (2025). Smart Innovation for a Circular Economy: A Systematic Review of Emerging Trends and the Future of AI in the Sustainable Economy. Sustainability, 17(13), 5793. https://www.mdpi.com/2071-15793/13/17/050
Hori, S., & Shimizu, Y. (1999). Designing methods of human interface for supervisory control systems. Control Engineering Practice, 7(11), 1413–1419. doi:https://doi.org/10.1016/S0967-0661(99)00112-4
Huang, L., Zhen, L., Wang, J., & Zhang, X. (2022). Blockchain implementation for circular supply chain management: Evaluating critical success factors. Industrial Marketing Management, 102, 451–464. doi: https://doi.org/10.1016/j.indmarman.2022.02.009
Idogawa, J., Bizarrias, F., & Camara, R. (2023). Critical success factors for change management in business process management. Business Process Management Journal, 29. doi:10.1108/BPMJ-11-2022-0625
Karimi Takalo, S., Sharifi, H., & Bakhshi Khorde Blagh, E. (2024). Analyzing the drivers of the Smart sustainable circular supply chain using the combined methods of Dematel and adversarial interpretative structural modeling. Industrial Management Studies, 22(73), 241–285. doi: 10.22054/jims.2024.77945.2899 (in Persian)
Kazmi, S., Sarfraz, H., Bukhari, S., & Javed, I. (2025). Reverse Logistics in the Circular Economy Context. The Asian Bulletin of Big Data Management, 5, 104–125. doi:10.62019/qegey841
Kemmner, F.-A., Legenvre, H., & Hameri, A.-P. (2025). Sharing data to implement the circular economy: the case of digital product passports. Industrial Management & Data Systems, 1–20. doi:10.1108/imds-04-2024-0403
Lahane, S., Kant, R., & Shankar, R. (2020). Circular supply chain management: A state-of-art review and future opportunities. Journal of Cleaner Production, 258, 120859. doi: https://doi.org/10.1016/j.jclepro.2020,120859
Leyh, C., Köppel, K., Neuschl, S., & Pentrack, M. (2021). Critical Success Factors for Digitalization Projects.
Liou, J. J. H., Tzeng, G.-H., & Chang, H.-C. (2007). Airline safety measurement using a hybrid model. Journal of Air Transport Management, 13(4), 243–249. doi:https://doi.org/10.1016/j.jairtraman.2007.04.008
Liu, J., Quddoos, M. U., Akhtar, M. H., Amin, M. S., Tariq, M., & Lamar, A. (2022). Digital technologies and circular economy in supply chain management: in the era of COVID-19 pandemic. Operations Management Research, 15, 326–341. doi:https://doi.org/10.1007/s12063-021-00227-7
Malekzadeh, G., Kazemi, M., Lagzian, M., & Mortazavi, S. (2016). Modeling organizational intelligence using DEMATEL method in Iranian public universities. Journal of Modelling in Management, 11(1), 134-153. doi;10.1108/jm2-12-2013-0062
Mohsen, S. E., Hamdan, A., & Shoaib, H. M. (2024). Digital transformation and integration of artificial intelligence in financial institutions. Journal of Financial Reporting and Accounting. doi:10.1108/JFRA-09-2023-0544
Morshedi, M., Hargaden, V., Papakostas, N., & Ghadimi, P. (2025). Integrating Circular Economy and Industry 4.0 within Circular Supply Chains: A Literature Review and Network Analysis Insights. Procedia CIRP, 135, 768–773. doi: https://doi.org/10.1016/j.procir.2024.12.073
Movahed, A. B., Movahed, A. B., & Nozari, H. (2024). Opportunities and Challenges of Smart Supply Chain in Industry 5.0. In H. Nozari (Ed.), Information Logistics for Organizational Empowerment and Effective Supply Chain Management (pp. 1). Hershey, PA, USA: IGI Global. doi: https://doi.org/ 10.4018/979-8-3693-0159-3.ch006
Mridha, B., Pareek, S., Goswami, A., & Sarkar, B. (2023). Joint effects of production quality improvement of biofuel and carbon emissions towards a smart sustainable supply chain management. Journal of Cleaner Production, 386, 135629. doi:https://doi.org/10.1016/j.jclepro.2022.135629
Oladapo, B. I., Olawumi, M. A., & Omigbodun, F. T. (2024). AI-Driven Circular Economy of Enhancing Sustainability and Efficiency in Industrial Operations. Sustainability, 16(23), 10358. Retrieved from https://www.mdpi.com/2071-1050/16/23/10358
Panda, D., Haque, S., Frishammar, J., & Parida, V. (2025). Modularity for circular economy: Four circularity pathways for industrial firms. Journal of Cleaner Production, 519, 146001. doi: https://doi.org/10.1016/j.jclepro.2025.146001
Pandey, D., Nassa, V. K., Pandey, B. K., Thankachan, B., Dadheech, P., Mahajan, D. A., & George, A. S. (2024). Artificial intelligence and machine learning and its application in the field of computational visual analysis. In Emerging Engineering Technologies and Industrial Applications (pp. 36–57). DOI:10.4018/979-8-3693-1335-0.ch003
Pathan, M. S., Richardson, E., Galvan, E., & Mooney, P. (2023). The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland. Sustainability. 9451, 12(15), Retrieved from https://www.mdpi.com/2071-1050/15/12/9451
Patidar, S., Sukhwani, V. K., & Shukla, A. C. (2023). Modeling of Critical Food Supply Chain Drivers Using DEMATEL Method and Blockchain Technology. Journal of The Institution of Engineers (India): Series C, 104(3), 541–552. doi:10.1007/s40032-023-00941-0
Platon, V., Pavelescu, F.-M., Antonescu, D., Constantinescu, A., Frone, S., Surugiu, M., . . . Popa, F. (2024). New evidence about artificial intelligence and eco-investment as boosters of the circular economy. Environmental Technology & Innovation, 35, 103685. doi:https://doi.org/10.1016/j.eti.2024.103685
Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. doi: https://doi.org/10.1016/j.ijpe.2021.108250
Qiao, D., Jiao, J., Khalid, N., & Ali, M. H. (2025). Supply chain concentration and corporate green innovation: Evidence from China. Innovation and Green Development, 4(2), 100202. doi: https://doi.org/10.1016/j.igd.2024.100202
Raut, S., Hossain, N. U. I., Kouhizadeh, M., & Fazio, S. A. (2025). Application of artificial intelligence in circular economy: A critical analysis of the current research. Sustainable Futures, 9, 100784. doi: https://doi.org/10.1016/j.sftr.2025.100784
Redko, K. (2024). Circular economy and AI empowerment in social entrepreneurship: a path to sustainability. International Science Journal of Management, Economics & Finance. 27-35, 3, doi: 10.46299/j.isjmef.20240303.04
Rehman, S., Mahmood, R., Abidi, N., & Yusoff, W. (2025). Integrating Smart Supply Chain with Green Practices to Enhance Sustainable Supply Chain Performance. Operations and Supply Chain Management: An International Journal, 1–18. doi:10.31387/oscm0600460
Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), 2117–2135. doi: https://doi.org/10.1080/00207543.2018.1533261
Saruchera, F., Salimi-Zaviyeh, S.-G., & Raeesi Vanani, I. (2024). Smart Supply Chain Management. In (pp. 152–178). doi:https://doi.org/10.4324/9781032719870-11
Shokouhyar, S., Pahlevani, N., & Mir Mohammad Sadeghi, F. (2019). Scenario analysis of smart, sustainable supply chain on the basis of a fuzzy cognitive map. Management Research Review, 43(4), 463–496. doi: 10.1108/mrr-01-2019-0002
Sklavos, G., Theodossiou, G., Papanikolaou, Z., Karelakis, C., & Ragazou, K. (2024). Environmental, Social, and Governance-Based Artificial Intelligence Governance: Digitalizing Firms’ Leadership and Human Resources Management. Sustainability, 16(16), 7154. Retrieved from https://www.mdpi.com/2071-1050/16/16/7154
Taghva, M. R. , Naghizadeh, M. and Naghizadeh, R. (2012). Identifying and Prioritizing Important necessities for formulation of Open Source Software Roadmap in Iran. Business Intelligence Management Studies, 1(1), 1-28. https://ims.atu.ac.ir/article_1126.html?lang=en  
Toșa, C., Paneru, C. P., Joudavi, A., & Tarigan, A. K. M. (2024). Digital transformation, incentives, and pro-environmental behaviour: Assessing the uptake of sustainability in companies' transition towards circular economy. Sustainable Production and Consumption, 47, 63. 643-2. doi: https://doi.org/10.1016/j.spc.2024.04.032
Trevisan, A. H., Boscarato, A., Acerbi, F., Terzi, S., & Sassanelli, C. (2025). Enhancing Circular Economy education and training for the manufacturing sector: A holistic skills framework. Journal of Environmental Management, 380, 124982. doi:https://doi.org/10.1016/j.jenvman.2025.124982
Van Opstal, W., Smeets, A., & Pals, E. (2024). Aligning incentives for implementing reversible bonding as a circular economy innovation. Business Strategy and the Environment, 33(8), 8017–8036. doi: https://doi.org/10.1002/bse.3904
Vudugula, S. (2025). Sustainable smart supply chains: a review of green technologies and their impact on logisticS (Vol. 4). doi:https://doi.org/10.63125/1fehce37
Wang, K.-H. (2025). From fear to adaptation: The dynamic impact of AI on worker behavior and technological well-being. Social Sciences & Humanities Open, 12, 101951. doi:https://doi.org/10.1016/j.ssaho.2025.101951
Wei, S., Liu, W., Lin, Y., Wang, J., & Liu, T. (2023). Smart supply chain innovation model selection: exploitative or exploratory innovation? International Journal of Logistics Research and Applications, 26(4), 478–497. doi: 10.1080/13675567.2021.1965104
Wu, W.-W., & Lee, Y.-T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert Systems with Applications, 32(2), 499–507. doi:https://doi.org/10.1016/j.eswa.2005.12.005
Xu, T., Wang, H., Feng, L., & Zhu, Y. (2024). Risk Factors Assessment of Smart Supply Chain in Intelligent Manufacturing Services Using DEMATEL Method With Linguistic q-ROF Information. Journal of Operations Intelligence, 2(1), 129–152. doi:10.31181/jopi21202417
Yontar, E. (2023). Critical success factor analysis of blockchain technology in agri-food supply chain management: A circular economy perspective. Journal of Environmental Management, 330, 117173. doi: https://doi.org/10.1016/j.jenvman.2022.117173
Zejjari, I., & Benhayoun, I. (2024). The use of artificial intelligence to advance sustainable supply chain: retrospective and future avenues explored through bibliometric analysis. Discover Sustainability, 5. doi: 10.1007/s43621-024-00364-6
Zhou, Y. (2025). AI-driven digital circular economy with material and energy sustainability for industry 4.0. Energy and AI, 20, 100508. doi:https://doi.org/10.1016/j.egyai.2025,100508