Evaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT model

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dc.authoridTopal, Ayse/0000-0003-1882-4545
dc.authoridEcer, Fatih/0000-0002-6174-3241
dc.authoridUlutaş, Alptekin/0000-0002-8130-1301
dc.authorwosidTopal, Ayse/AAH-2633-2020
dc.authorwosidEcer, Fatih/AAE-8455-2020
dc.authorwosidUlutaş, Alptekin/HHZ-2996-2022
dc.contributor.authorUlutas, Alptekin
dc.contributor.authorTopal, Ayse
dc.contributor.authorGorcun, Omer Faruk
dc.contributor.authorEcer, Fatih
dc.date.accessioned2024-08-04T20:54:52Z
dc.date.available2024-08-04T20:54:52Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractAutomotive businesses often delegate logistical tasks to third-party logistics (3PLs) service providers to acquire a competitive edge in the dynamic market. Nevertheless, selecting the most suitable third-party logistics (3PL) partner is a multifaceted undertaking that needs careful evaluation of several criteria and alternatives. This research aims to introduce an integrated grey Multiple Criteria Decision Making (MCDM) framework for automotive businesses to deal with the multidimensional 3PL selection decision problem. This framework incorporates an enhanced Preference Selection Index (PSI), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), and Mixed Aggregation by Comprehensive Normalization Technique (MACONT). The LOPCOW-G and grey PSI (PSI-G) methods extract the criterion weights, whereas the MACONT-G method ranks the alternatives. The suggested framework's practicality is shown by conducting a case study about evaluating and selecting a third-party logistics (3PLs) provider. The findings indicate that the parameters of significant importance are skilled workforce (0.0977), financial strength (0.0901), and IT-IS competence (0.0839). Furthermore, TPL4 has been recognized as the most optimum option with a value of 0.4797. The MACONT-G model is as well compared against other grey MCDM techniques to assess the validity of the proposed model. The Pearson correlation coefficient between MACONT-G and the other models based on grey sets is 0.958, suggesting a significant and positive link. Furthermore, it is worth noting that a sensitivity analysis has been conducted to validate the accuracy and reliability of the created framework. In conclusion, this study has identified managerial and policy implications that might assist policymakers and executives in effectively evaluating 3PL providers.en_US
dc.identifier.doi10.1016/j.eswa.2023.122680
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85178060985en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2023.122680
dc.identifier.urihttps://hdl.handle.net/11616/101690
dc.identifier.volume241en_US
dc.identifier.wosWOS:001125934800001en_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.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutomotive industryen_US
dc.subject3PLs service provider selectionen_US
dc.subjectGrey LOPCOWen_US
dc.subjectGrey MACONTen_US
dc.subjectGrey PSIen_US
dc.subjectSupply chain managementen_US
dc.titleEvaluation of third-party logistics service providers for car manufacturing firms using a novel integrated grey LOPCOW-PSI-MACONT modelen_US
dc.typeArticleen_US

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