Please use this identifier to cite or link to this item: https://doi.org/10.1016/S1570-7946(07)80141-6
DC FieldValue
dc.titleA performance assessment framework for supply chain networks
dc.contributor.authorRaj Thangavelu, S.
dc.contributor.authorSamavedham, L.
dc.date.accessioned2014-06-16T09:33:51Z
dc.date.available2014-06-16T09:33:51Z
dc.date.issued2007
dc.identifier.citationRaj Thangavelu, S.,Samavedham, L. (2007). A performance assessment framework for supply chain networks. Computer Aided Chemical Engineering 24 : 709-714. ScholarBank@NUS Repository. <a href="https://doi.org/10.1016/S1570-7946(07)80141-6" target="_blank">https://doi.org/10.1016/S1570-7946(07)80141-6</a>
dc.identifier.isbn9780444531575
dc.identifier.issn15707946
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54699
dc.description.abstractIn multi-echelon decentralized supply chains, distribution logistics (inclusive of both material and information flows) play a leading part in helping a supply chain gain advantage over competitors. The uncertain consumer demand and non-optimal operation of distribution nodes are some of the major problems that a supply chain must contend with. A distribution node in the network generally belongs to different companies thereby encouraging the decentralized management of nodes. Decentralized management may worsen the overall performance of the supply chain system and in turn affect the supply chain cost and customer satisfaction. Our work is focused on developing an assessment framework to examine and enhance the performance of an existing supply chain. Data from an existing network is used to determine the bottlenecks or poorly performing nodes. With the knowledge of supply chain architecture, time-series data analysis techniques are employed in this effort. Simulation based optimization are extensively employed to enrich the performance of the inferior nodes close to achievable benchmark standards by minimizing the supply chain cost. The concepts presented will be complemented by realistic simulation examples. © 2007 Elsevier B.V. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/S1570-7946(07)80141-6
dc.sourceScopus
dc.subjectdiagnosis
dc.subjectmanagement
dc.subjectoptimization.
dc.subjectperformance metrics
dc.subjectsupply chain
dc.typeArticle
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.1016/S1570-7946(07)80141-6
dc.description.sourcetitleComputer Aided Chemical Engineering
dc.description.volume24
dc.description.page709-714
dc.identifier.isiutNOT_IN_WOS
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