Optimization in supply chain management, the current state and future directions: A systematic review and bibliometric analysis
Abstract: The purpose of this
paper is finding the current state of research and identifies high-potential
area for future investigation in optimization in supply chain management.
Design/methodology/approach: In this paper we present Bibliometric and
Network analysis to examine current state research on optimization in supply
chain management to identify established and emergent research field for future
investigation. The systematic research review which we used in our study have
not grasp or assess by other researchers on this topic. Firstly, based on our
methodology Bibliometric analysis began by identifying 1610 publications raised
from scientific journals, included literatures from 1994 to March of 2016.
Secondly, we applied PageRank algorithm in our data for citation analysis to
indicate the significance of a publication. Thirdly, the topological decision
variables analysis is done based on Louvain method for network data clustering,
for this proposes we used the rigorous tools.
Finding: Based on our Network analysis result, the optimization in supply
chain management research can be divided into four clusters /modules that
introduced fundamental skill, knowledge, theory, application and method.
Research limitations/implications: We presented some limitation in our
research in some fields which could allow new researchers and practitioners
conduct the future research to grow up in different dimensions.
Practical implications: Practitioners or policy maker usually are not
familiar with these type researches so this is why mush of these survey remain
in theatrical and conceptual .Future investigation needs to play in practical
application domain instead stop merely
in opinion.
Originality/value: Based on our research, the researchers have more
attention to work in conceptual analysis due to other fields but we believe
that in facility location problem there many remarkable rooms still exist for
future research to development. We also contributed more details in the papers.
Author: Mahmood Movahedipour,
Mengke Yang, Jianqiu Zeng, Xiankang Wu, Shafaq Salam
Journal Code: jptindustrigg160043
