شناسایی مؤلفه¬های انتخاب تأمین¬کنندگان در شبکه تأمین تاب¬آور پروژه¬های نفت و گاز ایران تحت محیط عدم اطمینان
محورهای موضوعی : مدیریت صنعتی
حمیدرضا کریمی
1
(دانشجوی دکتری مدیریت صنعتی گراش تولید و عملیات،واحد رودهن، دانشگاه آزاد اسلامی، ایران)
صابر خندان
2
(مدیریت صنعتی، مدیریت و حسابداری، دانشگاه آزاد اسلامی رودهن، رودهن، ایران)
ندا فرح بخش
3
(گروه مدیریت، دانشکده حسابداری و مدیریت، واحد رودهن، دانشگاه آزاد اسلامی،رودهن، ایران)
کلید واژه: پروژه های نفت و گاز, تاب آوری, تأمین کنندگان, تصمیم گیری چند شاخصه, شبکه تأمین, منطق فازی.,
چکیده مقاله :
هدف از انجام مقاله حاضر شناسایی مؤلفه¬های انتخاب تأمین¬کنندگان در شبکه تأمین تاب¬آور پروژه¬های نفت و گاز ایران با تکنیک دلفی فازی، وزن¬دهی و اولویت¬بندی هریک با تکنیک بهترین – بدترین فازی و ارزیابی و رتبه‌بندی گزینه¬ها (تأمین¬کنندگان) در خصوص میزان تاب¬آوری در شبکه تأمین پروژه¬های نفت و گاز ایران با تکنیک¬های مَپَک و کُوالیفلیکس و تجمیع نتایج با تکنیک بُردا می¬باشد. جنبه نوآوری و جدید بودن پژوهش حاضر بهره¬مندی از منطق فازی، در نظر گرفتن تاب¬آوری تأمین¬کنندگان و ترکیب آن با تکنیک¬های تصمیم¬گیری چند شاخصه در معرفی مدل بومی انتخاب تأمین¬کنندگان صنعت نفت و گاز ایران است. جامعه و نمونه آماری پژوهش حاضر را 23 نفر از مدیران ارشد حوزه لجستیک در صنایع نفت و گاز ایران تشکیل¬ می¬دهند. نتایج حاصل از غربال¬سازی مؤلفه¬ها با دلفی فازی نشان داد، الگوی بومی در شش معیار و سی و هفت زیرمعیار شناسایی شدند. نتایج حاصل از وزن¬دهی به ابعاد انتخاب تأمین¬کنندگان تاب¬آور با تکنیک بهترین – بدترین فازی نشان¬ داد، معیار انعطاف¬پذیری (تاب¬آوری) مهم‌ترین معیار و قیمت و هزینه (معیار اقتصادی)، رتبه دوم و چابکی، خدمات تأمین¬کننده، ویژگی و ظرفیت تأمین¬کننده و کیفیت و تکنولوژی به ترتیب رتبه¬های سوم تا ششم را کسب نمودند. همچنین زیرمعیارهای هر معیار نیز وزن¬دهی و رتبه¬بندی شدند. سپس پیمانکاران تأمین¬کننده ابزار دقیق در صنعت نفت و گاز ایران با مدل پیشنهادی ارزیابی و با تکنیک¬های مپک و کوالیفلیکس رتبه¬بندی گردیدند.
The purpose of this paper is to identify the components of selecting suppliers in the resilient supply network of Iran's oil and gas projects with the fuzzy Delphi technique, weighting and prioritizing each one with the fuzzy best-worst technique and evaluating and ranking the options. (suppliers) regarding the level of resilience in the supply network of Iran's oil and gas projects with Mapak and Covaliflex techniques and summarizing the results with Borda technique. The aspect of innovation and novelty of the current research is the use of fuzzy logic, consideration of supplier resilience and its combination with multi-indicator decision-making techniques in introducing the local model of supplier selection in Iran's oil and gas industry. The population and the statistical sample of the present study are 23 senior managers in the field of logistics in Iran's oil and gas industries. The results of component screening with fuzzy Delphi showed that indigenous patterns were identified in six criteria and thirty-seven sub-criteria. The results of weighting the dimensions of selecting resilient suppliers with the fuzzy best-worst technique showed that the criterion of flexibility (resilience) is the most important criterion, and price and cost (economic criterion), the second rank, and agility. Supplier services, supplier characteristics and capacity, and quality and technology were ranked third to sixth respectively. Also, the sub-criteria of each criterion were weighted and ranked. Then, the contractors supplying precision instruments in Iran's oil and gas industry were evaluated with the proposed model and ranked with MAPPAK and Qualiflix techniques.
1. Abbaspour Onari, M & Jahangoshai Rezaee, M. (2020). A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry. Operational Research.
2. Amiri, M., & jahani, S. (2010). Application of IDEA/AHP for Supplier evaluation and Selection. Industrial Management Journal, 2 (2), 5-22. (In Persian)
3. Alinejad, A., Makoui, A., & Esfandiyari, N. (2013). New techniques in multi-attribute decision making. Academic Jihad Publications.
4. Ballard, G & Elfving, J. (2020). Supplier Development: The Gateway to Supply Chain Management in the Construction Industry. Lean Construction Journal.
5. Chen, K.S., Chang, T.C & Guo, Y.Y. (2020). Selecting an optimal contractor for production outsourcing: a case study of gear grinding. Journal of the Chinese Institute of Engineers, 43 (5), 415-424.
6. Carvalho, H., Barroso, A., Machado, V., Azevedo, S. & Machado, C. (2012). Supply chain redesign for resilience using simulation. Computers & Industrial Engineering. 62, 329-341.
7. Gunasekaran, A., Subramanian, N., & Rahman, S. (2015). Supply chain resilience: role of complexities and strategies. International Journal of Production Research, 53 (22), 6809-6819.
8. Ghahremaneram, M., Abdi, A., Rozbahani, Z., & Shahabi, M.(2017). Investigating Models of Resilience in Business, Fifth International Conference on Research Approaches in Humanities and Management.
9. Ji, G., & Hong, W. (2024). Research on the Manufacturer’s Strategies under Different Supply Interruption Risk Based on Supply Chain Resilience. Sustainability, 16 (2), 874.
10. Jahani, M., Azar, A., & Maghbalbaarz, A. (2017). Interpretative-Structural Modeling of Factors Affecting Supply Chain Resilience. Journal of Human Resource Management Research, 7 (4), 1-27.
11. Jamasbi, N., Olfat, L., Amiri, M., & Pishvaee, M. S. (2023). Presenting a Model for Evaluation and Selection of Sustainable Third Party Logistics Service Providers in the supply chain Based on the Combined Approach of Fuzzy Analytical Hierarchy and Cocoso Technique (case study: dairy industry). Iranian journal of management sciences, 17 (68), 45-74. (In Persian)
12. Jafarnejad, A., Kazemi, A., & Arab, A. (2016). Identify and prioritization of the supplier's resilience assessment indicators based on the best-worst- method. Journal of Industrial Management Perspective, (23), 159-186.
13. Khan, M.M., Bashar, I., Minhaj, G.M., Wasi, A.I., & Hossain, N.U.I. (2023). Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach. Sustainable and Resilient Infrastructure, 8 (5), 453-469.
14. Khatami Firouzabadi, S. M. A. (2014). Evaluation and Selection of International Supplier, Underscoring Risk Factors. Research in Production and Operations Management, 5 (1), 53-70. (In Persian)
15. Karimmian, Z., Ghodsypour, S. H., & Gheidar-Kheljani, J. (2018). Supplier Selection Problem Considering Relationships between Suppliers and Supply Disruption Risk in complex products. Research in Production and Operations Management, 8 (2), 135-150. (In Persian)
16. Karimi, H. (2023). Designing a model for selecting suppliers in oil and gas projects with a fuzzy multi-criteria decision-making approach. International Conference on Management and Industry, 5 (5), 705-734. (In Persian)
17. Kobagani, M.H., & Shahbandarzadeh, H. (2022). Quantitative analysis of supplier selection criteria in resilient supply chain using multi-criteria decision making techniques. Business Journal, 90 (23), 115-140. (In Persian) 18. Li, Y., Diabat, A & Lu, C.C. (2020). Leagile supplier selection in Chinese textile industries: a DEMATEL approach. Annals of Operations Research, 287 (1), 303-322
19. Morovati Sharifabadi, A., Kazemi, F., & Hayati, M. (2013). A Fuzzy QFD and Fuzzy MCDM Approach for Supplier Selection (A study of Snow-Pars co. of Yazd). Journal of Industrial Management Perspective, 3 (2), 129-146. (In Persian)
20. Mirfakhrodini, H., Sharifabadi, A., & Mohammadi, M. (2017). Prioritize organizational strategies. Strategic Management Research, Twenty-third Year, No. 66, 109-81.
21. Nayeri, S., Khoei, M.A., Rouhani-Tazangi, M.R., GhanavatiNejad, M., Rahmani, M., & Tirkolaee, E.B. (2023). A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system. Engineering Applications of Artificial Intelligence, 124, 106511.
22. Rahimi, A., Rönnqvist, M., LeBel, L & Audy, J. F. (2020). Selecting wood supply contracts under uncertainty using stochastic programming. INFOR: InformationSystems and Operational Research, 1-21.
23. Ravanestan, K., Aghajani, H., Safaeeghadikalaei, A.h., & Yahyazadehfar, M.(2017). Determine the strategies and numbers of Iran Khodro Supply Chain. Journal of Strategic management research, (66), 24-45.
24. Shojaei, P & bolvardizadeh, A. (2020). Rough MCDM model for green supplier selection in Iran: a case of university construction project. Built Environment Project and Asset Management, 10 (3), 437-452.
25. Salehi, M., & Sayyah, M. (2017). Evaluation and Selection of Efficient Suppliers in terms of Uncertainty -The Grey Data Envelopment Analysis Approach. Iranian Journal of Trade Studies, 21 (81), 181-203. (In Persian)
26. Savoodi, S. (2019). Evaluation and Selection of Suppliers with Quantity Discounts (Case Study: Isfahan’s Mobarakeh Steel Company). Commercial Surveys, 16(93-92), 55-78.
27. Tong, L., Pu, Z., Chen, K & Yi, J. (2020). Sustainable maintenance supplier performance evaluation based on anextend fuzzy PROMETHEE II approach in petrochemical industry. Journal of Cleaner Production, 273, 122771.
28. Rezaei, J., (2015). Best –Worst multi –criteria decision- making method. Omega. 53, 49-57.
29. Ravanestan, K., Aghajani, H., Safaeeghadikalaei, A.h., & Yahyazadehfar, M. (2017). Determine the strategies and numbers of Iran Khodro Supply Chain. Journal of Strategic management research, (66), 24-45.
30. Urmia, I., Bonab, S.R., Haseli, G. (2023). Sustainable resilient supplier selection for IoT implementation based on the integrated BWM and TRUST under spherical fuzzy sets. Decision Making: Applications in Management and Engineering, 6 (1), 153-185.
31. Yazdi, A.K., Komijan, A.R., Wanke, P.F & Sardar, S. (2020). Oil project selection in Iran: A hybrid MADM approach in an uncertain environment. Applied Soft Computing, 88, 106066.
