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Evaluation and selection of supplier in supply chain with fuzzy analytical network process approach

Document Type : Research Paper

Authors

1 Deparment of Industrial Engineering, Najafabad Branch, Islamic Azad University, Esfahan, Iran.

2 Department of Production Engineering, South Ural State University, Lenin Prosp. 76, 454080 Chelyabinsk, Russia.

3 Department of Industrial Engineering, Malek Ashtar University, Esfahan, Iran.

4 Fraunhofer-Institut für Holzforschung Wilhelm-Klauditz Institut WKI, Bienroder Weg 54 E, 38108 Brunswick, Germany.

5 Operations and Information Management Group, Aston Business School, Aston University, Birmingham B4 7ET, United Kingdom.

Abstract

One of the most important issues concerning the designing a supply chain is selecting the supplier. Selecting proper suppliers is one of the most crucial activities of an organization towards the gradual improvement and a promotion in performance. This intricacy is because suppliers fulfil a part of customer’s expectancy and selecting among them is multi-criteria decision, which needs a systematic and organized approach without which this decision may lead to failure. The purpose of this research is proposing a new method for assessment and rating the suppliers. We have identified several evaluation criteria and attributes; the selection among them was by the Simple Multi-Attribute Rating Technique (SMART) method, then we have specified the connection and the influence of the criteria on each other by DEMATEL method. After that, suppliers were graded by using the Fuzzy Analytical Network Process (FANP) approach and the most efficient one was selected. The innovation of this research is combining the SMART method, DEMATEL method, and Analytical Network Process in Fuzzy state which lead to more exact and efficient results which is proposed for the first time by the researchers of this study.

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References
  1. selection under interval-valued intuitionistic uncertain linguistic environment. Information sciences, 486, 254-270.
  2. Schramm, V. B., Cabral, L. P. B., & Schramm, F. (2020). Approaches for supporting sustainable supplier selection-A literature review. Journal of cleaner production, 123089. https://doi.org/10.1016/j.jclepro.2020.123089
  3. Narasimhan , R ., Talluri, S., & Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. Journal of supply chain management, 37(3),28-37.
  4. Jeong, C. S., & Lee, Y. H. (2002). A multi-criteria supplier selection (MCSS) model for supply chain management. VISION: the journal of business perspective, 51–60.
    https://www.researchgate.net/profile/Young-Hae-Lee/publication/264235100_Multi-Criteria_Supplier_SelectionMCSS_Model_for_Supply_Chain_Management/links/550123d90cf2aee14b58ecd7/Multi-Criteria-Supplier-SelectionMCSS-Model-for-Supply-Chain-Management.pdf
  5. Xia, W., & Wu, Z. (2007). Supplier selection with multiple criteria in volume discount environments. Omega, 35(5), 494-504.
  6. Kumar, M., Vrat, P., & Shankar, R. (2006). A fuzzy programming approach for vendor selection problem in a supply chain. International journal of production economics, 101(2), 273-285.
  7. Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multi-attribute utility measurement. Organizational behavior and human decision processes, 60(3), 306–325.
  8. Lootsma, F. A. (1996). A model for the relative importance of the criteria in the multiplicative AHP and SMART. European journal of operational research, 94(3), 467-476.
  9. Haddara, M. (2014). ERP selection: the SMART way. Procedia technology, 16, 394-403.
  10. Cheng, C. C., Chen, C. T., Hsu, F. S., & Hu, H. Y. (2012). Enhancing service quality improvement strategies of fine-dining restaurants: New insights from integrating a two-phase decision-making model of IPGA and DEMATEL analysis. International journal of hospitality management, 31(4), 1155-1166.
  11. Sumrit, D., & Anuntavoranich, P. (2013). Using DEMATEL method to analyze the causal relations on technological innovation capability evaluation factors in Thai technology-based firms. International transaction journal of engineering, management, & applied sciences & technologies, 4(2), 81-103.
  12. Wu, H. H., & Tsai, Y. N. (2011). A DEMATEL method to evaluate the causal relations among the criteria in auto spare parts industry. Applied mathematics and computation, 218(5), 2334-2342.
  13. Yamazaki, M., Ishibe, K., Yamashita, S., Miyamoto, I., Kurihara, M., & Shindo, H. (1997). An analysis of obstructive factors to welfare service using DEMATEL method. Reports of the faculty of engineering, 48, 25-30.
  14. Razmi, J., Rafiei, H., & Hashemi, M. (2009). Designing a decision support system to evaluate and select suppliers using fuzzy analytic network process. Computers & industrial engineering, 57(4), 1282-1290.
  15. Saaty, T. L. (1980). The analytic hierarchy process, planning, priority setting, resource allocation. McGraw-Hill.
  16. Saaty, T. L. (1996). Decision making with dependence and feedback: the analytic network process. RWS Publications.
  17. Saaty, T. L. (1999). Fundamentals of the analytical network process. Proceeding of ISHP (pp. 48–63). Kobe.
  18. Saaty, T. L., & Vergas, L. G. (2006). Decision making with analytic network process. New York: Springer Sciences.
  19. Taslicali, A. K., & Ercan, S. (2006). The analytic hierarchy and the analytic network process in multicriteria decision making: a comparative study. Journal of aeronautics and space technologies, 2(4), 55–65.
  20. Chang, D. Y. (1996). Application of the extent analysis method on fuzzy AHP. European journal of operational research, 95, 649–655.
  21. Das, S. K., Edalatpanah, S. A., & Mandal, T. (2018). A proposed model for solving fuzzy linear fractional programming problem: numerical point of view. Journal of computational science, 25, 367-375.
  22. Edalatpanah, S. A., & Smarandache, F. (2019). Data envelopment analysis for simplified neutrosophic sets. Neutrosophic sets and systems, 29, 215-226.
  23. Edalatpanah, S. A. (2020). Data envelopment analysis based on triangular neutrosophic numbers. CAAI transactions on intelligence technology, 5(2), 94-98.
  24. Edalatpanah, S. A. (2018). Neutrosophic perspective on DEA. Journal of applied research on industrial engineering, 5(4), 339-345.
  25. Edalatpanah, S. A. (2020). Neutrosophic structured element. Expert systems, 37(5), e12542. https://doi.org/10.1111/exsy.12542
  26. Behera, D., Peters, K., Edalatpanah, S. A., & Qiu, D. (2020). New methods for solving imprecisely defined linear programming problem under trapezoidal fuzzy uncertainty. Journal of information and optimization sciences, 1-27. https://doi.org/10.1080/02522667.2020.1758369
  27. Shafi Salimi, P., & Edalatpanah, S. A. (2020). Supplier selection using fuzzy AHP method and D-Numbers. Journal of fuzzy extension and applications, 1(1), 1-14.
  28. Karaşan, A., & Kahraman, C. (2019). A novel intuitionistic fuzzy DEMATEL–ANP–TOPSIS integrated methodology for freight village location selection. Journal of intelligent & fuzzy systems, 36(2), 1335-1352.
  29. Liu, H. C., Quan, M. Y., Li, Z., & Wang, Z. L. (2019). A new integrated MCDM model for sustainable supplier selection under interval-valued intuitionistic uncertain linguistic environment. Information sciences, 486, 254-270.
  30. Schramm, V. B., Cabral, L. P. B., & Schramm, F. (2020). Approaches for supporting sustainable supplier selection-A literature review. Journal of cleaner production, 123089. https://doi.org/10.1016/j.jclepro.2020.123089
  31. Narasimhan , R ., Talluri, S., & Mendez, D. (2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. Journal of supply chain management, 37(3), 28-37.
  32. Jeong, C. S., & Lee, Y. H. (2002). A multi-criteria supplier selection (MCSS) model for supply chain management. VISION: the journal of business perspective, 51–60. https://www.researchgate.net/profile/Young-Hae-Lee/publication/264235100_Multi-Criteria_Supplier_SelectionMCSS_Model_for_Supply_Chain_Management/links/550123d90cf2aee14b58ecd7/Multi-Criteria-Supplier-SelectionMCSS-Model-for-Supply-Chain-Management.pdf
  33. Xia, W., & Wu, Z. (2007). Supplier selection with multiple criteria in volume discount environments. Omega, 35(5), 494-504.
  34. Kumar, M., Vrat, P., & Shankar, R. (2006). A fuzzy programming approach for vendor selection problem in a supply chain. International journal of production economics, 101(2), 273-285.
  35. Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multi-attribute utility measurement. Organizational behavior and human decision processes, 60(3), 306–325.
  36. Lootsma, F. A. (1996). A model for the relative importance of the criteria in the multiplicative AHP and SMART. European journal of operational research, 94(3), 467-476.
  37. Haddara, M. (2014). ERP selection: the SMART way. Procedia technology, 16, 394-403.
  38. Cheng, C. C., Chen, C. T., Hsu, F. S., & Hu, H. Y. (2012). Enhancing service quality improvement strategies of fine-dining restaurants: New insights from integrating a two-phase decision-making model of IPGA and DEMATEL analysis. International journal of hospitality management, 31(4), 1155-1166.
  39. Sumrit, D., & Anuntavoranich, P. (2013). Using DEMATEL method to analyze the causal relations on technological innovation capability evaluation factors in Thai technology-based firms. International transaction journal of engineering, management, & applied sciences & technologies, 4(2), 81-103.
  40. Wu, H. H., & Tsai, Y. N. (2011). A DEMATEL method to evaluate the causal relations among the criteria in auto spare parts industry. Applied mathematics and computation, 218(5), 2334-2342.
  41. Yamazaki, M., Ishibe, K., Yamashita, S., Miyamoto, I., Kurihara, M., & Shindo, H. (1997). An analysis of obstructive factors to welfare service using DEMATEL method. Reports of the faculty of engineering, 48, 25-30.
  42. Razmi, J., Rafiei, H., & Hashemi, M. (2009). Designing a decision support system to evaluate and select suppliers using fuzzy analytic network process. Computers & industrial engineering, 57(4), 1282-1290.
  43. Saaty, T. L. (1980). The analytic hierarchy process, planning, priority setting, resource allocation. McGraw-Hill.
  44. Saaty, T. L. (1996). Decision making with dependence and feedback: the analytic network process. RWS Publications.
  45. Saaty, T. L. (1999). Fundamentals of the analytical network process. Proceeding of ISHP (pp. 48–63). Kobe.
  46. Saaty, T. L., & Vergas, L. G. (2006). Decision making with analytic network process. New York: Springer Sciences.
  47. Taslicali, A. K., & Ercan, S. (2006). The analytic hierarchy and the analytic network process in multicriteria decision making: a comparative study. Journal of aeronautics and space technologies, 2(4), 55–65.
  48. Chang, D. Y. (1996). Application of the extent analysis method on fuzzy AHP. European journal of operational research, 95, 649–655.
Journal of Fuzzy Extension and Applications
Volume 2, Issue 1
March 2021
Pages 69-88
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