نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه مدیریت صنعتی، دانشگاه آزاد اسلامی، واحد قزوین.

2 استادیار، گروه مدیریت صنعتی، دانشگاه آزاد اسلامی، واحد قزوین.

چکیده

با بررسی‌های پژوهش‌های پیشین مشخص شد که بیشتر مطالعات صورت‌­گرفته درباره بررسی توانمندی‌های زنجیره تأمین پایدار به‌صورت آزمون‌های آماری و مدل‌سازی‌های ریاضی محدود به چند متغیر است. هدف این پژوهش، ارائه یک مدل ارزیابی توانمندی‌های چندگانه و همچنین تعیین نقش این توانمندی‌ها در سیستم ارزیابی زنجیره‌تأمین پایدار است. پس از تعیین قواعد مدل‌سازی، مدل ریاضی تهیه می‌شود. قلمرو مکانی انجام این پژوهش شامل 16 شرکت فعال در صنعت کاشی و سرامیک در ایران است. نتایج ارزیابی نشان می‌دهد که بیشتر شرکت‌های موردمطالعه از نظر توانمندی در سطح 2 قرار می‌گیرند؛ ازاین‌­رو پیشنهاد می‌­شود شرکت‌های تولیدکننده صنعت کاشی و سرامیک برای ارزیابی سطح توانمندی و تعیین شکاف توانمندی‌های خود، نسبت به بهره‌برداری از این متغیرها اقدام نمایند. در این پژوهش از ابزار چک‌لیست برای پالایش معیارها با کاربرد دلفی فازی استفاده شده؛ سپس با مطالعه مدل‌های ارزیابی، مدل مناسب تهیه شد. سپس با استفاده از فرآیند تحلیل سلسله‌مراتبی اقدام به مقایسه و نهایی­‌کردن معیارها شده است؛ همچنین با استفاده از سیستم خبره فازی (FIS and ANFIS) نسبت به نهایی‌­سازی معیارها اقدام شده است. بر پایه مدل تهیه‌­شده نتیجه مدل راستی آزمایی گردید. شبیه‌سازی مدل با استفاده از نرم‌افزارهای MATLAB و Simulink انجام شد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Designing a Model for Evaluation of Sustainable Supply Chain Multi Capabilities Based on Artificial Intelligence

نویسندگان [English]

  • Valiollah Aslani Liaei 1
  • Sadegh Abedi 2
  • Alireza Irajpour 2
  • Reza Ehtesham Rathi 2

1 PhD. Student, Department of Industrial management, Islamic Azad University, Qazvin Branch.

2 Assistant Professor, Department of Industrial management, Islamic Azad University, Qazvin Branch.

چکیده [English]

According to previous research, most studies on the evaluation of sustainable supply chain capabilities are limited to statistical variables and mathematical modeling. The purpose of this study is to propose multiple capacity evaluation models in the sustainable supply chain. The spatial scope of this research includes a survey of 16 companies active in the ceramic & tile industry in Iran. According to fuzzy expert system modeling, four capabilities, including competitiveness, operational, technology, and resilience as input variables and three levels for sustainable supply chain capabilities as output variables were determined. Simulink was used to simulate and integrate the designed fuzzy systems. The evaluation results show that most of the companies studied fall into the level 2 capability. It is recommended that the ceramic tile manufacturing companies evaluate the level of capability and determine the sustainable capabilities of their supply chain capabilities to exploit these variables. Resiliency capabilities have an impact on upgrading the evaluation of multiple capabilities in the sustainable supply chain. Therefore, companies are advised to incorporate flexibility and adaptability into developing domestic and foreign markets and market orientation and not to define future projects as environmental changes.

کلیدواژه‌ها [English]

  • Supply Chain: Sustainable Supply Chain
  • Fuzzy Expert System
  • Fuzzy Delphi
  • Simulation
  • Artificial Intelligence
  1. Acquaye A., Ibn-Mohammed, T., Genovese, A., Godfred A Afrifa, Fred A Yamoah & Oppon, E. (2018). A Quantitative Model for Environmentally Sustainable Supply Chain Performance Measurement. European Journal of Operational Research, (269), 188-205.
  2. Barbosa A., da Silva, C. & Carvalho, A. (2018). Opportunities and challenges in sustainable supply chain: An operations research perspective. European Journal of Operational Research, 268(2), 399-431.
  3. Das D. (2017). Development and validation of a scale for measuring Sustainable Supply Chain Management practices and performance. Journal of Cleaner Production, (164), 1344-1362.
  4. Fakoor Sagihe, A. M., Olfat, L., Feizi K. & Amiri, M. (2014). A model of Supply chain resilience for competitiveness in Iranian automotive companies.Operation and production management, 5(1), 143-164.
  5. Irajpur A., Majd S. H., & Esmaeilirad, D. (2017). Evaluation of sustainable supply chain performance by phazzy approach (Case study: Qazvin Glass Industy). Journal of Management Studies and Accounting, 3(1), 271-280. (In Persian).
  6. Jagan Mohan Reddy. K., Neelakanteswara Rao, A., Krishnanand, L. (2019). A review on supply chain performance measurement systems. Procedia Manufacturing, 30, 40–47.
  7. Karamouz, S. S. (2019). Performance measurement of supply chain quality management by combination balance scorecard and system dynamics. Journal of Industrial management perspective, 9(35), 165-193. (In Persian)
  8. Kouchaki Tajani T., Mohtashami, A., Amiri M., & Ehtesham Rathi, R. (2021). Presenting a Robust Optimization Model to Design a Comprehensive Blood Supply Chain under Supply and Demand Uncertainties. Journal of Industrial management perspective, 11(1), 81-116. (In Persian)
  9. Kozarević S. & Puškab, A. (2018). Use of fuzzy logic for measuring practices and performances of supply chain. operations research perspectives, (5), 150-160.
  10. Mani V., Gunasekaran A., & Delgado, C. (2018). Enhancing supply chain performance through supplier social sustainability: An emerging economy perspective. International Journal of Production Economics, (195), 259-272.
  11. Mirghafoori S.H., Morovati Sharifabadi A., & Karimi Takaloet, S. (2017). using cognitive mapping method in designing of sustainable supply chain model in type-2 fuzzy environment. Journal of healthcare management (journal of health system), 8(3), 51 -64.
  12. Rajeev A., Rupeh, K., Pati, Sidhartha S. Padhi & Govindan, K. (2017). Evolution of sustainability in supply chain management: A literature review. Journal of Cleaner Production, (162), 299-314.
  13. Rajesh R., (2019). Exploring the sustainability performances of firms using environmental, social, and governance scores. Journal of Cleaner Production, (247),
  14. Sadeghi, A., Azar, A., Valmohammdi, Ch., & Alirezaiee, A. (2018). Designing an assessment model of service supply chain by using neural network in order to improving quality and productivity of service (Case study: Home appliance industries of Iran). Journal of engineering and quality management, (3), 182-202.
  15. Shoua Y., Li, Y., Park, Y., & Kang, M. (2018). Supply chain integration and operational performance: The contingency effects of production systems. Journal of Purchasing and Supply Management, (24), 352-360.
  16. Taghzadeh yazdi M. R., & Salmani Zarchi, E. (2020). Presenting a comprehensive multi- objevtive model of multi level - multi product green closed- loop supply chain with a weighted sum method approach: Pareto front generation (Case study: shahpar momtaz shoes co.). Journal of Industrial management perspective, 9(4), 107-137. (In Persian)
  17. Uygun Ö., & Dede, A. (2016). Performance evaluation of green supply chain management using integrated fuzzy multi-criteria decision making techniques. Computers & Industrial Engineering, (102), 502-511.
  18. Wong T.C., Kris M.Y. Law, Hon K. Yau & Ngan, S.C. (2011). Analyzing supply chain operation models with the PC-algorithm and the neural network, Expert Systems with Applications, (38), 7526-7534.
  19. Yu W., Roberto Chavez, Mark A. Jacobs, & MengyingFengd, (2018). Data-driven supply chain capabilities and performance: A resource-based view, Transportation Research Part E: Logistics and Transportation Review, (114), 371-385.
  20. Zarrinpoor, N., & Omidvari, Z. (2021). A Robust Optimization Model for the Strategic and Operational Design of the Oil Supply Chain. Journal of Industrial management perspective, 10(4),155-191, (In Persian).
  21. Zhang M., KeiTse, , Dohertya, B., Li S. & Akhtar, P. (2018). Sustainable supply chain management: Confirmation of ahigher-order model, Resources, Conservation and Recycling, (128), 206-221.