International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
Vol. 12, No. 1, 2015, pp. 15-34
Bioline Code: st15002
Full paper language: English
Document type: Research Article
Document available free of charge
International Journal of Environment Science and Technology, Vol. 12, No. 1, 2015, pp. 15-34
© Copyright 2015 - International Journal of Environment Science and Technology
Lean, green and resilient practices influence on supply chain performance: interpretive structural modeling approach|
Govindan, K.; Azevedo, S.G.; Carvalho, H. & Cruz-Machado, V.
Nowadays, companies are struggling to find
an appropriate supply chain strategy to achieve competitiveness.
Among the available strategies lean, green and
resilient are considered as a new management strategies
for the supply chain management to achieve competitiveness.
The major issues with theses strategies are the
integration and identification of critical issues related to
the strategies. This paper aims to identify the critical
lean, green and resilient practices on which top management
should focus in order to improve the performance
of automotive supply chains. The systematic
analysis of the lean, green and resilient practices is
expected to be of great value for their effective implementation
by the automotive companies. The interpretive
structural modeling approach is used as a useful methodology
to identify inter-relationships among lean, green
and resilient practices and supply chain performance and
to classify them according to their driving or dependence
power. According to this research, the practices with the
main driving power are just-in-time (lean practice),
flexible transportation (resilient practice) and
environmentally friendly packaging (green practice).
Customer satisfaction is the performance measure with
strong dependence and weak driving power; that is, it is
strongly influenced by the other researched variables but
does not affect them.
Lean; Green; Resilient; Supply chain performance; Interpretive structural modeling; Automotive supply chain
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