Assessing sustainability of supply chains by double frontier network DEA: A big data approach

Taliva Badiezadeh, Reza Farzipoor Saen*, Tahmoures Samavati

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

115 Citations (Scopus)


Nowadays, performance evaluation of sustainable supply chain management (SSCM) is a very important topic for researchers and practitioners. Data envelopment analysis (DEA) is an appropriate method for assessing performance of SSCM in presence of Big Data. Network DEA (NDEA) can calculate efficiency of multi-stage processes. In this paper, an NDEA model for calculating optimistic and pessimistic efficiency is developed. Our proposed model can incorporate undesirable outputs. Also, our model can rank supply chains in terms of efficiency scores. A case study demonstrates efficacy of our proposed model.

Original languageEnglish
Pages (from-to)284-290
Number of pages7
JournalComputers and Operations Research
Publication statusPublished - Oct 2018


  • Big Data
  • Data envelopment analysis (DEA)
  • Double frontier
  • Network data envelopment analysis (NDEA)
  • Sustainable supply chain management (SSCM)
  • Undesirable outputs

ASJC Scopus subject areas

  • General Computer Science
  • Modelling and Simulation
  • Management Science and Operations Research

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