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A Hybrid Method for Vendor Selection using Neural Network

International Journal of Computer Applications
© 2010 by IJCA Journal
Number 12 - Article 8
Year of Publication: 2010
Jitendra Kumar
Nirjhar Roy

Jitendra Kumar and Nirjhar Roy. Article:A Hybrid Method for Vendor Selection using Neural Network. International Journal of Computer Applications 11(12):35–40, December 2010. Published By Foundation of Computer Science. BibTeX

	author = {Jitendra Kumar and Nirjhar Roy},
	title = {Article:A Hybrid Method for Vendor Selection using Neural Network},
	journal = {International Journal of Computer Applications},
	year = {2010},
	volume = {11},
	number = {12},
	pages = {35--40},
	month = {December},
	note = {Published By Foundation of Computer Science}


Problem of vendor evaluation and selection has always been viewed as the most important responsibility of purchasing department and for such reason, received a great deal of attention from practitioners and researchers. This solution has always been a complex process as various criteria, known and half known are involved in making a decision. This work attempts to develop a rule based model, to evaluate the performance of vendors, supplying components and raw materials to a multinational organization engaged in designing, manufacturing and delivering a range of products covering various stages of electric power transmission and distribution system. To select the vendors, there is a need to rank all the potential vendors according to a performance measure because in this industry almost all items are outsourced from vendors and input material cost constitute almost 80% cost of the product. For such reason any organization is required to select suitable vendors who can supply input materials and components to the organization as per the need timely with right specification and requisite quantity.

This paper presents a hybrid model using analytic hierarchy process (AHP) and neural networks (NNs) theory to assess vendor performance. The model consists of two modules: Module 1 applies AHP using pair wise comparison of criteria for all vendors, In the process the importance of the criteria is also obtained using an iterative algorithm. Module 2 utilizes the results of AHP into NNs model for vendor selection. The results yield the best vendor and appropriate score to compare the performance of each vendor. Selection of alternative vendors also can be carried out by using the historical data. Validation of the entire developed algorithm has been carried out separately.


  • Akarte, M. M., Surendra, N.V., Ravi, B., and Rangaraj N. 2001. Web based casting supplier evaluation using analytical hierarchy process. Journal of the Operational Research Society, Vol. 52, no. 5, pp. 511-522.
  • Boer, L. De, Van der Wegen, L., and Telgen, J. 1998. Outranking methods in support of supplier selection. European Journal of Purchasing and Supply Management, vol. 4, no. 2/3, pp.109-118.
  • Choya, K. L., Leea, W. B., and Lo, V. 2003. Design of an intelligent supplier relationship management system: a hybrid case based neural network approach. Expert Systems with Applications, vol. 24, pp. 225-237.
  • Degraeve, Z. and Roodhooft, F. 1998. Determining sourcing strategies: a decision model based on activity and cost driver information. Journal of the Operational Research Society, vol. 49, no. 8, pp. 81-789.
  • Degraeve, Z. and Roodhooft, F. 1999. Improving the efficiency of the purchasing process using total cost of ownership information: the case of heating electrodes at Cockerill Sambre S. A. European Journal of Operational Research, vol.112, no. 1, pp. 42-53.
  • Degraeve, Z. and Roodhooft, F. 2000. A mathematical programming approach for procurement using activity based costing. Journal of Business Finance and Accounting, vol. 27, no. (1-2), pp. 69-98.
  • Dickson, G. W. 1966. A analysis of vendor selection systems and decisions. J. Purch, vol. 2, pp. 5–17.
  • Faris, C.W., Robinson, P.J., and Wind, Y. 1967. Industrial Buying and Creative Marketing. Allyn & Bacon, Boston.
  • Ho, W., Xu, X., and Dey, P. K. 2010. Multi - criteria decision making approaches for supplier evaluation and selection: A literature review. European Journal of Operational Research, vol. 202, pp.16–24.
  • Huang, S. H. and Keskar, H. 2007. Comprehensive and configurable metrics for supplier selection. International Journal of Production Economics, vol. 105, no. 2, pp. 510–523.
  • Humphreys, P., Huang, G., Cadden, T., and McIvor, R. 2007. Integrating design metrics within the early supplier selection process. Journal of Purchasing & Supply Management, vol. 13, pp. 42–52.
  • Kraljic, P. 1983. Purchasing must become supply management. Harvard Business Review, vol. 61, no. 5, pp. 109-117.
  • Larson, G. B. 1995. An ANN pruning algorithm based approach to vendor selection. Journal of Systems Management (USA), vol. 46, no. 5, pp. 56-60.
  • Li, Guo-Dong, Yamaguchi, D., and Nagai, M. 2007. A grey-based decision-making approach to the supplier selection Problem. Mathematical and Computer Modeling, vol. 46, pp. 573–581.
  • Mohammad Ebrahim, R., Razmi, J., and Haleh, H. 2009. Scatter search algorithm for supplier selection and order lot sizing under multiple price discount environment. Advances in Engineering Software, vol. 40, pp. 766-776.
  • Muralidharan, C., Anantharaman, N., and Deshmukh, S. G. 2002. A multi-criteria group- decision making model for supplier rating. Journal of Supply Chain Management, vol. 38, no. 4, pp. 22–33.
  • Ozdemir, D., and Temur, G. T. 2009. DEA ANN approach in supplier evaluation system. World Academy of Science, Engineering and Technology, 54.
  • Quin, L. 2009. An ANN pruning algorithm based approach to vendor selection. Kybernetes, vol. 38, 3-4, pp. 314-320.
  • Saaty, T. L. 1980. The analytic hierarchy process. New York: McGraw-Hill.
  • Timmerman, E. 1986. An approach to vendor performance evaluation. Journal of Purchasing and Supply Management, vol. 1, pp. 27-32.
  • Venkata Rao, R. 2007. Supplier selection in a supply chain using analytic hierarchy process and genetic algorithm methods. International Journal of Services and Operations Management, vol. 3, no. 3, pp. 355-369.
  • Verma, R. and Pullman, M. E. 1998. An analysis of supplier selection process. Omega, Int. J. Mgmt. Sci., vol. 26, no. 6, pp. 739–750.
  • Wang, G., Huang, S. H., and Dismukes, J. P. 2004. Product-driven supply chain selection using integrated multi-criteria decision-making methodology. International Journal of production Economics, vol. 91, no. 1, pp. 1–15,.
  • Wang, H. S., and Che, Z. H. 2007. An integrated model for supplier selection decisions in configuration changes. Expert Systems with Applications, vol. 32, pp. 1132–1140.
  • Weber, C. A., Current, J. R., and Desai, A. 1998. Non-cooperative negotiation strategies for vendor selection. European Journal of Operational Research, vol. 108, pp. 208-223.
  • Weber, C. A., Current, J. R., and Desai, A. 2000. An optimization approach to determining the number of vendors to employ. Supply Chain Management: An International Journal, vol. 5, no. 2, pp. 90–98.
  • Wu, D. 2009. Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert Systems with Applications, vol. 36, pp. 9105-9112.
  • Zenz, G. 1981. Purchasing and the Management of Materials. Wiley, New York.