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

Taliva Badiezadeh; Reza Farzipoor Saen; Tahmoures Samavati

doi:10.1016/j.cor.2017.06.003

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

Operations Management & Business Statistics

Research output: Contribution to journal › Article › peer-review

96 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	284-290
Number of pages	7
Journal	Computers and Operations Research
Volume	98
DOIs	https://doi.org/10.1016/j.cor.2017.06.003
Publication status	Published - Oct 2018

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Modelling and Simulation
Management Science and Operations Research

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10.1016/j.cor.2017.06.003

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abstract = "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.",

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AU - Badiezadeh, Taliva

AU - Saen, Reza Farzipoor

AU - Samavati, Tahmoures

PY - 2018/10

Y1 - 2018/10

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AB - 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.

KW - Big Data

KW - Data envelopment analysis (DEA)

KW - Double frontier

KW - Network data envelopment analysis (NDEA)

KW - Sustainable supply chain management (SSCM)

KW - Undesirable outputs

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JO - Computers and Operations Research

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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