Neural network application for supplier selection

Davood Golmohammadi, Robert C. Creese and Haleh Valian

International Journal of Product Development, 2009, vol. 8, issue 3, 252-275

Abstract: An efficient decision-making model was developed to select suppliers using multi-layer feed forward neural networks. A set of input functions for supplier selection criteria was defined to create input data for training the model. Both types of criteria, qualitative and quantitative, were considered in the model. Fuzzy techniques were applied to convert qualitative data to quantitative data. Pairwise comparisons matrices were applied for output values and weight assignment. The neural network model structure was designed and tested based on backpropagation. The results of the neural network model indicated that the proper structure of the model had a crucial effect on its performance. The selection of appropriate initial weights, learning rate and momentum were critical in improving the model performance. To prove the capability of the proposed model, suppliers of three products were ranked based on the proposed model and the results were compared with the managers' ranking. The proposed neural network model can use historical data of suppliers to evaluate their performance in the vendor supplier selection decision. The vendor can update the suppliers' database information over time for future decisions.

Keywords: decision making; neural networks; supplier selection; backpropagation; fuzzy sets; fuzzy logic. (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpdev:v:8:y:2009:i:3:p:252-275

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