TY - JOUR
T1 - Measuring congestion in sustainable supply chain based on data envelopment analysis
AU - Shadab, Maryam
AU - Saati, Saber
AU - Farzipoor Saen, Reza
AU - Khoveyni, Mohammad
AU - Mostafaee, Amin
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/10
Y1 - 2021/10
N2 - Sustainable Supply Chain Management (SSCM) involves the integrating of environmental, social and economic concerns into supply chain management activities with emphasis on the managers' efforts in the context of reducing the negative social and environmental impacts. Evaluating sustainable supply chain performance and efficiency is a significant topic for many researchers and scholars. Presence of input and intermediate product congestion is one of the key issues that results in lower efficiency and performance in a sustainable supply chain. Therefore, determination of congestion is of prime importance and removing it improves performance of the sustainable supply chain. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Some studies have been conducted to detect the intermediate product congestion via solving Network DEA (NDEA) models without considering the role of intermediate products. In this study, a sustainable supply chain with two-stage structure was considered. Then, the congestion status according to the role of intermediate products was found out for the first time. Towards this aim, different scenarios which congestion can occur in intermediate products were identified. Then, in each scenario, the dominant cone definition was developed in network structure and NDEA models were proposed. Finally 20 Iranian sustainable supply chains of Resin manufacturing companies have been used to demonstrate applicability of the proposed models.
AB - Sustainable Supply Chain Management (SSCM) involves the integrating of environmental, social and economic concerns into supply chain management activities with emphasis on the managers' efforts in the context of reducing the negative social and environmental impacts. Evaluating sustainable supply chain performance and efficiency is a significant topic for many researchers and scholars. Presence of input and intermediate product congestion is one of the key issues that results in lower efficiency and performance in a sustainable supply chain. Therefore, determination of congestion is of prime importance and removing it improves performance of the sustainable supply chain. One of the most appropriate methods for detecting congestion is Data Envelopment Analysis (DEA). Some studies have been conducted to detect the intermediate product congestion via solving Network DEA (NDEA) models without considering the role of intermediate products. In this study, a sustainable supply chain with two-stage structure was considered. Then, the congestion status according to the role of intermediate products was found out for the first time. Towards this aim, different scenarios which congestion can occur in intermediate products were identified. Then, in each scenario, the dominant cone definition was developed in network structure and NDEA models were proposed. Finally 20 Iranian sustainable supply chains of Resin manufacturing companies have been used to demonstrate applicability of the proposed models.
KW - Congestion
KW - Intermediate products role
KW - Network Data Envelopment Analysis (NDEA)
KW - Sustainable Supply Chain Management (SSCM)
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U2 - 10.1007/s00521-021-05889-9
DO - 10.1007/s00521-021-05889-9
M3 - Article
AN - SCOPUS:85104394235
SN - 0941-0643
VL - 33
SP - 12477
EP - 12491
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 19
ER -