TY - JOUR
T1 - Assessing sustainability of supply chains by double frontier network DEA
T2 - A big data approach
AU - Badiezadeh, Taliva
AU - Saen, Reza Farzipoor
AU - Samavati, Tahmoures
N1 - Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2018/10
Y1 - 2018/10
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=85020414759&partnerID=8YFLogxK
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U2 - 10.1016/j.cor.2017.06.003
DO - 10.1016/j.cor.2017.06.003
M3 - Article
AN - SCOPUS:85020414759
SN - 0305-0548
VL - 98
SP - 284
EP - 290
JO - Computers and Operations Research
JF - Computers and Operations Research
ER -