Abstract
In order to ensure the uninterrupted supply of items, the suppliers’ performance needs to be evaluated periodically. The evaluation process typically consists of identifying the attributes and criteria relevant to the decision, and measuring the performance of a supplier by considering the relevant criteria. But the evaluation process is complex. Linguistic assessment of suppliers may be carried out based on several criteria. Much of the data are difficult to obtain and ambiguous or vague to interpret. Nonetheless, a rational process of evaluation must exist to select the most appropriate suppliers. This paper develops a supplier evaluation approach based on the analytic network process (ANP) and fuzzy synthetic evaluation under a fuzzy environment. The importance weights of various criteria are considered as linguistic variables. These linguistic ratings can be expressed in triangular fuzzy numbers by using the fuzzy extent analysis. Fuzzy synthetic evaluation is used to select a supplier alternative and the Fuzzy ANP (FANP) method is applied to calculate the importance of the criteria weights. Then an integrated FANP and fuzzy synthetic evaluation methodology is proposed for evaluating and selecting the most suitable suppliers. A hypothetical example is presented and the results indicated that the combination of ANP and fuzzy synthetic evaluation provided useful tool to select the optimal supplier.
Similar content being viewed by others
References
Ayağ Z., Özdemir R. G. (2009) An intelligent approach to machine tool selection through fuzzy analytic network process. Journal of Intelligent Manufacturing 22(2): 163–177
Buckley J. J. (1985) Fuzzy hierarchical analysis. Fuzzy Sets and Systems 17: 233–247
Carrera D. A., Mayorga R. V. (2008) Supply chain management: A modular fuzzy inference system approach in supplier selection for new product development. Journal of Intelligent Manufacturing 19: 1–12
Cemalettin, K., & Baris, Y. (2010). A hybrid intelligent approach for supply chain management system. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0431-2.
Chan F. T. S., Kumar N. (2007) Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega-International Journal of Management Science 35(4): 417–431
Chang, D. Y. (1992). In: Extent Analysis and Synthetic Decision, Optimization Techniques and Applications (pp. 352). Singapore: World Scientific
Chang D. Y. (1996) Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research 95: 649–655
Chen C. T., Lin C. T., Huang S. F. (2006) A fuzzy approach for supplier evaluation and selection in supply chain management. International Journal of Production Economics 102: 289–301
Cheng C. H. (1997) Evaluating naval tactical missile systems by fuzzy AHP based on the grade value of membership function. European Journal of Operational Research 96: 343–350
Cheng C. H., Yang K. L., Hwang C. L. (1999) Evaluating attack helicopters by AHP based on linguistic variable weight. European Journal of Operational Research 116(2): 423–435
Chung S. H., Lee A. H. I., Pearn W. L. (2005) Analytic network process (ANP) approach for product mix planning in semiconductor fabricator. International Journal of Production Economics 96: 15–36
Deng H. (1999) Multicriteria analysis with fuzzy pairwise comparison. International Journal of Approximate Reasoning 21(3): 215–231
Haq A. N., Kannan G. (2006) Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment. International Journal of Production Research 44(10): 1963–1985
Hwang C. L., Yoon K. (1981) Multiple attribute decision making-methods and applications: A state of the art survey. Springer, Berlin
Jeong, C. S., & Lee, Y. H. (2002). A multi-criteria supplier selection (MCSS) model for supply chain management. VISION: The Journal of Business Perspective, pp. 51–60.
Kahraman C., Cebeci U., Ruan D. (2004) Multi-attribute comparison of catering service companies using fuzzy AHP: The case of Turkey. International Journal of Production Economics 87: 171–184
Kahraman C., Cebeci U., Ulukan Z. (2001) Multi-criteria supplier selection using fuzzy AHP. Logistics Information Management 16(6): 382–394
Kang, H.-Y., Lee A. H. I., & Yang C.-Y. (2010). A fuzzy ANP model for supplier selection as applied to IC packaging. Journal of Intelligent Manufacturing. doi:10.1007/s10845-010-0448-6.
Karsak E. E. (2002) Distance-based fuzzy MCDM approach for evaluating flexible manufacturing system alternatives. International Journal of Production Research 40(13): 3167–3181
Karsak E. E., Sozer S., Alpteki S. E. (2002) Product planning in quality function deployment using a combined analytic network process and goal programming approach. Computers & Industrial Engineering 44: 171–190
Leung L. C., Cao D. (2000) On consistency and ranking of alternatives in fuzzy AHP. European Journal of Operational Research 124: 102–113
Meade L. M., Presley A. (2002) R&D project selection using the analytic network process. IEEE Transactions on Engineering Management 49: 59–66
Mikhailov L. (2004) A fuzzy approach to deriving priorities from interval pairwise comparison judgments. European Journal of Operational Research 159: 687–704
Mohanty R. P., Agarwal R., Choudhury A. K., Tiwari M. K. (2005) A fuzzy ANP-based approach to R&D project selection: A case study. International Journal of Production Research 43: 5199–5216
Muralidharan C., Anantharaman N., Deshmukh S.G. (2002) A multi-criteria group decisionmaking model for supplier rating.. The Journal of Supply Chain Management 28: 22–33
Narasimhan, R., Talluri, S., & Mendez, D.(2001). Supplier evaluation and rationalization via data envelopment analysis: an empirical examination. The Journal of Supply Chain Management, 28–36.
Pi W. N., Low C. (2006) Supplier evaluation and selection via Taguchi loss functions and an AHP. International Journal of Advanced Manufacturing Technology 27(5–6): 625–630
Saaty T. L. (1980) The Analytical Hierarchy Process. McGraw-Hill, London
Saaty T. L. (1996) Decision making with dependence and feedback: The analytic network process. RWS Publications, Pittsburgh
Saaty T. L., Vargas L. G. (1998) Diagnosis with dependent symptoms: Bayes theorem and the analytic hierarchy process. Operations Research 46(4): 491–502
Shyur H. J., Shih H. S. (2006) A hybrid MCDM model for strategic vendor selection. Mathematical and Computer Modeling 44(7–8): 749–761
Soukup W.R. (1987) Supplier selection strategies. Journal of Purchasing and Materials Management 23(2): 7–12
Tam M. C. Y., Tummala V. M. R. (2001) An application of AHP in vendor selection of a telecommunications system. Omega 29: 171–182
Willis, T. H., Huston, C. R. & Pohlkamp, F.(1993). Evaluation measure of just-in-time supplier performance. Journal of Production and Inventory management, 2nd Quarter, 1–6.
Wong, J. T. (2010). DSS for 3PL provider selection in global supply chain: combining the multi-objective optimization model with experts’ opinions, Journal of Intelligent Manufacturing doi:10.1007/s10845-010-0398-z.
Xia W. J., Wu Z. M. (2007) Supplier selection with multiple criteria in volume discount environments. Omega-International Journal of Management Science 35(5): 494–504
Yu R., Tzeng G. H. (2006) A soft computing method for multi-criteria decision making with dependence and feedback. Applied Mathematics and Computation 180: 63–75
Yüksel I., Dagdeviren M. (2007) Using the analytic network process (ANP) in a SWOT analysis – A case study for a textile firm. Information Sciences 177(16): 3364–3382
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pang, B., Bai, S. An integrated fuzzy synthetic evaluation approach for supplier selection based on analytic network process. J Intell Manuf 24, 163–174 (2013). https://doi.org/10.1007/s10845-011-0551-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10845-011-0551-3