Developing a model for determining optimal η in DEA-discriminant analysis for predicting suppliers’ group membership in supply chain

Elahe Boudaghi, Reza Farzipoor Saen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


This study aims at developing a model for determining optimal η in data envelopment analysis –discriminant analysis (DEA-DA) in order to predict the suppliers’ group membership in supply chain. Also, this paper improves the DEA-DA model developed by Sueyoshi (Eur. J. Oper. Res. 115(3), 564–582; [11]). In this model, η parameter is used in order to avoid a trivial solution and to create more discrimination. Since the main shortcoming of the DEA-DA is determining η value based on the researcher’s subjective decision, the present paper develops a model to determine optimal η. Consequently, this paper leads to the estimate of an optimal set of weights for producing a hyperplane for determining the discriminant linear function and, hence promotes the prediction precision of the group membership of a set of observations. The efficacy of proposed model is presented using a numerical example as well as a case study.

Original languageEnglish
Pages (from-to)134-155
Number of pages22
Issue number1
Publication statusPublished - Mar 2015


  • DEA-discriminant analysis
  • Discriminant linear function
  • Hierarchical cluster analysis
  • Predicting suppliers’ group membership

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research

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