A decision support model for identification and prioritization of key performance indicators in the logistics industry

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dc.authoridKucukaltan, Berk/0000-0002-2766-3248
dc.authoridAktas, Emel/0000-0003-3509-6703
dc.authorwosidKucukaltan, Berk/R-4151-2019
dc.authorwosidIrani, Zahir/HHM-3209-2022
dc.authorwosidAktas, Emel/A-8654-2008
dc.contributor.authorKucukaltan, Berk
dc.contributor.authorIrani, Zahir
dc.contributor.authorAktas, Emel
dc.date.accessioned2024-06-12T11:07:51Z
dc.date.available2024-06-12T11:07:51Z
dc.date.issued2016
dc.departmentTrakya Üniversitesien_US
dc.description.abstractPerformance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs. to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies. (C) 2016 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.chb.2016.08.045
dc.identifier.endpage358en_US
dc.identifier.issn0747-5632
dc.identifier.issn1873-7692
dc.identifier.scopus2-s2.0-84984985615en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage346en_US
dc.identifier.urihttps://doi.org/10.1016/j.chb.2016.08.045
dc.identifier.urihttps://hdl.handle.net/20.500.14551/22189
dc.identifier.volume65en_US
dc.identifier.wosWOS:000386986000037en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Human Behavioren_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectLogistics Performance Indicatorsen_US
dc.subjectBalanced Scorecarden_US
dc.subjectANPen_US
dc.subjectMulti-Criteria Decision Makingen_US
dc.subjectStakeholdersen_US
dc.subjectSocial Mediaen_US
dc.subjectSustainability Balanced Scorecarden_US
dc.subjectAnalytic Hierarchy Processen_US
dc.subjectSupply Chain Managementen_US
dc.subject3rd-Party Logisticsen_US
dc.subjectReverse Logisticsen_US
dc.subjectMeasurement Systemsen_US
dc.subjectService Providersen_US
dc.subjectNetworken_US
dc.subjectFrameworken_US
dc.subjectSelectionen_US
dc.titleA decision support model for identification and prioritization of key performance indicators in the logistics industryen_US
dc.typeArticleen_US

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