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  • ارزیابی و رتبه‌بندی تأمین‌کنندگان در زنجیره تأمین با استفاده از سیستم‌های تصمیم-گیری چندمعیاره فازی ذوزنقه‌ای

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آدرس مقاله


کد مقاله : IMJ-1801-1009 (R4) بازدید : 67 صفحه: 49 - 64

نوع مقاله: پژوهشی

ارزیابی و رتبه‌بندی تأمین‌کنندگان در زنجیره تأمین با استفاده از سیستم‌های تصمیم-گیری چندمعیاره فازی ذوزنقه‌ای

محورهای موضوعی : مدیریت صنعتی

hassanali aghajani 1 (Associate Professor and Faculty Member in University of Mazandaran.)
hossein Samadi-Miarkolaei 2 (Master of Public Administration, Member of Young Researcher Club, Islamic Azad University, Qaemshahr)
hamzeh samadi 3 (Ph.D. of Public Administration, Department of Public Administration, Science and Research Branch, Islamic Azad University, Tehran, Iran)
mehdi sohanian 4 (university of mazandaran)

تاریخ دریافت : 1396/10/30 تاریخ پذیرش : 1397/10/17 تاریخ انتشار : 1397/11/01

کلید واژه: تاپسیس فازی, ارزیابی, زنجیره تامین, رتبه‌بندی, ایران خودرو,

چکیده مقاله :

امروزه سازمان‌ها به‌خوبی دریافته‌اند که نیازبه زنجیره‌تامین کارا دارند، تا بتوانند در بازار جهانی و اقتصاد شبکه‌ای و به‌هم پیوسته امروزی توان رقابت با رقبای خود را داشته باشند. ازطرفی از مهم‌ترین عوامل پایداری و بقا دراین محیط رقابتی، کاهش هزینه‌های تولید محصول است. براین اساس، انتخاب مناسب تامین‌کنندگان می‌تواند تاثیر بسیار زیادی بر کاهش هزینه‌های تولید و قابلیت رقابت‌پذیری سازمان داشته باشد. هدف تحقیق حاضر، تعیین و بومی‌سازی معیارها، رتبه‌بندی و انتخاب تامین‌کنندگان در صنعت خودروسازی ایران می‌باشد. این تحقیق براساس هدف کاربردی و از لحاظ ماهیت، توصیفی است. هم‌چنین جهت جمع‌آوری اطلاعات برای آزمون چهارچوب پیشنهادی، یک مطالعه موردی در صنعت خودروسازی به‌کار گرفته شده است. دراین پژوهش از روش تحلیل عاملی تائیدی برای انتخاب و بومی‌سازی متغیرها، و سیستم استنتاج‌فازی برای تعیین اوزان معیارهای تصمیم‌گیری و از روش تاپسیس‌فازی برای ارزیابی و رتبه‌بندی تامین‌کنندگان استفاده شده است. به‌کارگیری هم‌زمان این روش‌ها نوآوری این پژوهش در میان پیشینه مرتبط با این حوزه می‌باشد. پس از مرور پیشینه تحقیق، معیارها با استفاده از آزمون تحلیل عاملی در شش مورد کیفیت، تحویل، مهارتهای فنی، خدمات، سرمایه‌گذاری و طراحی محصول دسته‌بندی شده، و سپس شرکت‌های تأمین‌کننده باطری توان، برنا، صبا و نیروگستران مورد ارزیابی و رتبه-بندی قرار گرفتند. یافته‌ها نشان داد که شرکت برنا باطری(52/0) بهترین تأمین‌کننده، و شرکت‌های توان، صبا و نیرو با کسب امتیازات (45/0)، (41/0)،و(31/0) به ترتیب در درجات بعدی قرار دارند. در نهایت به‌مدیران توصیه شده که چگونه می‌توانند از یافته‌های تحقیق حاضر بمنظور انتخاب بهترین تأمین‌کننده و بهبود توان رقابتی سازمان خود استفاده نمایند.

چکیده انگلیسی:

Nowadays, Organizations are well aware of their need to an efficient supply chain, so that they can compete with their rivals in today's global market and in the network and interconnected economy. Reducing the costs of production is the most important factor for survival in this competitive environment. Thus, selecting the appropriate supplier could have a great deal of influence on reducing the production costs and increasing the organization competitiveness ability. The aim of this paper is the determination and customization of the criteria, ranking and selecting the suppliers of SCM in Automobile Manufacturing Companies of Iran. This study, in terms of purpose, is an applied study and in terms of nature, is a descriptive study. In order to collection of data to test the proposed framework, it is conducted a case study in the automobile industry. In this study, the confirmatory factor analysis is used for selection and customization of the variables, and the fuzzy inference system is applied for determination of the weights of decision-making criteria, and the fuzzy TOPSIS method employed for evaluation and ranking the suppliers. The contribution of this study in the relevant literature to this field is usage and combination of three aforementioned methods. By means of literature review, the criteria categorized in 6 categories: Quality, Delivery, Technical skills, Services, Investment, and Product Design, and then, the selected supplier companies including Tavan, Borna, Saba, and Niroo-Gostaran have been analyzed and ranked.

منابع و مأخذ:


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_||_

  1. Astom, K., & Golhar, D. Y. (1993). JIT purchasing: Attribute classification and literature review, Prod. Planning Control. 4(3), 273-282.
    2.
  2. Aghajani, H., & Ahmadpour, M. (2011). Application of fuzzy topsis for ranking suppliers of supply chain in automobile manufacturing companies in Iran. Fuzzy Information and Engineering, 3(4), 433-444.
    3.
  3. Banfield, H. (1999). Harnessing Value In The Supply Chain Strategic Sourcing In Action, John Wiley & Sons Inc.
    4.
  4. Bache, J., R. Carr, J. Parnaby, and A. M. Tobias. (1987). Supplier development systems." International Journal of Technology Management. 2(2), 219-228.
    5.
  5. Büyüközkan, G., & Çifçi, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000-3011.
    6.
  6. Bharadwaj, N. (2004). Investigating the decision criteria used in electronic components procurement, Industrial Marketing Management. 33. 317– 323.
    7.
  7. Chen C. T., & Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management, Int. j. Production Economics. 102. 289-301.
    8.
  8. Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European journal of operational research, 142(1), 174-186.
    9.
  9. Constantine, S., & Katsikeas, N. G., & Paparoidamis, E. K. (2004). Supply source selection criteria: The impact of supplier performance on distributor performance. Industrial Marketing Management. 33. 755–764.
    10.
  10. Dickson, G.W. (1996). An Analysis of vender selection system and decision. journal of purchasing. 2(1). 5-17
    11.
  11. Dempsey, W. A. (1978). Vender selection and buying. Process Industrial Marketing Management. 7. 257-267.
    12.
  12. Dulmin, R., & Valeria, . (2003). Supplier selection using a multi-criteria decision aid method. Journal of Purchasing & Supply Management. 9. 177–187.
    13.
  13. Eng, T. Y. (2006). Mobile supply chain management: Challenges for implementation. Technovation, 26(5), 682-686.
    14.
  14. Giannoccaro, I., & Albino, V., & Carbonara, N. (2002). Supply chain cooperation in industrial districts: A simulation analysis. European Journal of Operational Research. 9(2). 1-19.
    15.
  15. Ghodsypour S. H., & OBrien, C. (1998). A Decision support system for supplier selection using an integrated analytic hierarchy process and linear programming, International Journal of Production Economics. 196-212.
    16.
  16. Hoha, S., & Krishnan, R. (2007). A hybrid approach to supplier selection for the maintenance of a competitive supply chain. Expert Systems with Applications. 25. 207-216.
    17.
  17. Humphreys, P., & Ronan, M., & Felix, C. (2003). Using case-based reasoning to evaluate supplier environmental management performance, Expert Systems with Applications. 25. 141–153.
    18.
  18. Hult, G.M.T., & Ferrell, O. C. (1997). A global learning organization structure and market information processing, Journal of Business Research. 40. 155–166.
    19.
  19. Junior, F. R. L., & Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209.
    20.
  20. Kheljani, J. G., Ghodsypour, S. H., & O’Brien, C. (2009). Optimizing whole supply chain benefit versus buyer's benefit through supplier selection. International Journal of Production Economics, 121(2), 482-493.
    21.
  21. Lin, H-T., & Wen-Ling, C. (2007). Order selection and pricing methods using flexible quantity and fuzzy approach for buyer evaluation, Production. Manufacturing and Logistics. 52. 215-226.
    22.
  22. Li, S., Rao, S. S., Ragu-Nathan, T. S., & Ragu-Nathan, B. (2005). Development and validation of a measurement instrument for studying supply chain management practices. Journal of operations management, 23(6), 618-641.
    23.
  23. Monczka, R. M., & Handfield, R. B., & Giunipero, L. C., & Patterson, J. L. (2008). Purchasing and Supply Chain Management, South Western. CENGEGE Learning.
    24.
  24. McCormack, K., Bronzo Ladeira, M., & Paulo Valadares de Oliveira, M. (2008). Supply chain maturity and performance in Brazil. Supply Chain Management: An International Journal, 13(4), 272-282.
    25.
  25. Manthou, V., Vlachopoulou, M., & Folinas, D. (2004). Virtual e-Chain (VeC) model for supply chain collaboration. International Journal of Production Economics, 87(3), 241-250.
    26.
  26. Mikaeil, R., Ozcelik, Y., Yousefi, R., Ataei, M., & Hosseini, S. M. (2013). Ranking the sawability of ornamental stone using Fuzzy Delphi and multi-criteria decision-making techniques. International Journal of Rock Mechanics and Mining Sciences, 58, 118-126.
    27.
  27. Nunnally, J. C. (1978). Psychometric theory. Second ed., McGraw-Hill, New York.
    28.
  28. Oliver, R. K., & Webber, M. D. (1982). Supply-chain management: logistics catches up with strategy. Outlook, 5(1), 42-47.
    29.
  29. Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359.
    30.
  30. Sadi-Nezhad, S., & Damghani, K. K. (2010). Application of a fuzzy TOPSIS method base on modified preference ratio and fuzzy distance measurement in assessment of traffic police centers performance. Applied soft computing, 10(4), 1028-1039.
    31.
  31. Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2004). Managing The Supply Chain: Definitive Guide. Tata McGraw-Hill Education.
    32.
  32. Shin, H., Collier, D. A., & Wilson, D. D. (2000). Supply management orientation and supplier/buyer performance. Journal of operations management, 18(3), 317-333.
    33.
  33. Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
    34.
  34. Van de ven, A., & Ferry, D. (1979). Measuring and assessing organizations, John Wiley, New York.
    35.
  35. Weber, C. A. (1999). A multiobjective approach to vendor selection, Eur. J. Oper. Res. 68. 173-184.
    36.
  36. Wu, W. W., & Lee, Y. T. (2007). Developing global managers’ competencies using the fuzzy DEMATEL method. Expert systems with applications, 32(2), 499-507.
    37.

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