Supplier selection: A hybrid model using DEA, decision tree and neural network

  • Authors:

  • Desheng Wu

  • Affiliations:

  • Reykjavik University, Kringlunni 1, IS-103 Reykjavík, Iceland and RiskLab, University of Toronto, 1 Spadina Crescent, Toronto, Canada ON M5S 3G3

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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Abstract

As the most important responsibility of purchasing management, the problem of vendor evaluation and selection has always received a great deal of attention from practitioners and researchers. This management decision is a challenge due to the complexity and various criteria involved. This paper presents a hybrid model using data envelopment analysis (DEA), decision trees (DT) and neural networks (NNs) to assess supplier performance. The model consists of two modules: Module 1 applies DEA and classifies suppliers into efficient and inefficient clusters based on the resulting efficiency scores. Module 2 utilizes firm performance-related data to train DT, NNs model and apply the trained decision tree model to new suppliers. Our results yield a favorable classification and prediction accuracy rate.