Research Note: Fuzzy Supplier Selection by Use of Weighted Indices

Document Type: Research Note

Authors

1 Young Researches and Elite Club, Karaj Branch, Islamic Azad University, Karaj, Iran

2 Department of Accounting, Islamic Azad University, Karaj Branch, Karaj, Iran

Abstract

Since supplier selection is an important part in management fields, this research focuses on weighted non-hierarchical fuzzy model to increase supply chain management performance. Supplier selection researches have significantly increased but most of methods have been focused on hierarchical determination of indices. This article by use of a multiple objective function tried to present a method that can consider non-hierarchical determination of indices in specific conditions. This research by use of deliverable indices of supply chain management tries to select the best suppliers. In this paper it is assumed that all suppliers have the ability to supply needed items but client can only make a product they provide. Quality of supply chain deliverable, supply chain reliability and supply chain visibility names indices have been selected to increase efficiency in the supply chain. This approach presents local optimal solutions by use of a heuristic logic in supply chain management. These indices are used as fuzzy to select the appropriate suppliers. By this fuzzy method, appropriate supplier can be set for each of the items. The presented approach have been introduced a weighted indices to determine best supplier in specific conditions. In this research weighted non-hierarchical fuzzy sets have been used to select appropriate suppliers. This method is useful for supplier selection problems.

Keywords


Tseng, M., Wu, K. and Nguyen, T. (2011). Information technology in supply chain management: a case study. Procedia - Social and Behavioral SCVences. 25, 257 – 272.
Woolliscroft, P., Caganova, D., Cambal, M., Holecek, J., Pucikova, L. (2013) Implications for optimisation of the automotive supply chain through knowledge management. Procedia CIRP. 7, 211 – 216.
Chen, Y., Mockus, L., Orcun, S., Reklaitis, G. (2012) Simulation-optimization approach to clinical trial supply chain management with demand scenario forecast. Computers and Chemical Engineering. 40, 2012, 82– 96
Song, D., Dong, J., Xu, J. (2014). Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties. European Journal of Operational Research. 232,522–536.
Yu, M., Goh, M. (2014). A multi-objective approach to supply chain visibility and risk. European Journal of Operational Research. 233,125–130.
Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research. 233,299–312.
Shaw, K., Shankar, R., Yadav, S., Thakur, L. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications. 39, 8182–8192.
Ishizaka, A., Nguyen, N. (2013). Calibrated fuzzy AHP for current bank account selection. Expert Systems with Applications. 40, 3775–3783.
Bas, E. (2013). The integrated framework for analysis of electricity supply chain using an integrated SWOT-fuzzy TOPSIS methodology combined with AHP: The case of Turkey. Electrical Power and Energy Systems. 44, 897–907
Pettersson, A., Segerstedt, A. (2013). Measuring supply chain cost. Int. J. Production Economics. 143,357–363.
Wang, G., Huang, S., Dismukes, J. (2005). Manufacturing supply chain design and evaluation. international journal of advanced manufacturing technology. 25, 93-100
Peidro, D., Mula, J., Poler, R., Lario, F. (2009). Quantitative models for supply chain planning under uncertainty: a review. international journal of advanced manufacturing technology. 43, 400-420.
Long, Q., Lin, J., Sun, Z. (2011). Modeling and distributed simulation of supply chain with a multi-agent platform. international journal of advanced manufacturing technology. 55, 1241-1252.
Singh, A., Mishra, P., Jain, R., Khurana, M. (2012). Design of global supply chain network with operational risks. International journal of advanced manufacturing technology. 60, 273-290.
Uthayakumar, R., Rameswari, M. (2013). Supply chain model with variable lead time under credit policy.  international journal of advanced manufacturing technology( 64), 389-397.
Bidhandi, M. H., Mohd Yusuff, R. (2011) Integrated supply chain planning under uncertainty using an improved stochastic approach. Applied Mathematical Modelling. 35, 2618–2630
Aliev, R., Fazlollahi, B., Guirimov, B., Aliev, R. (2007). Fuzzy-genetic approach to aggregate production–distribution planning in supply chain management. Information SCVences. 177, 4241–4255
Siyaprani, M. K., Gholami, K. (2014). The Influence of Knowledge Management Factors on Food Exports in Iran, International Journal of Management, Accounting and Economics, 1 (1), 37-51.