Interpretive structural modelling to assess third party logistics providers. Issue 1 (2016)

• Record Type: Journal Article
• Authors: Gupta, Aman; Walton, Robert O.
• Is Part Of: World review of intermodal transportation research. Volume 6:Issue 1(2016)
• Page Start: 59
• Page End: 73
• Publication Date: 2016
• Subjects: global supply chains -- logistics outsourcing -- third-party logistics -- 3PL providers -- 3PL selection -- supplier selection -- interpretive structural modelling -- ISM -- supply chain management -- SCM -- costs -- culture -- cultural alignment -- reputation -- quality -- location -- collaboration -- services -- experience -- financial stability -- flexibility -- information technology -- metrics; Containerization -- Research -- Periodicals; Transit, International -- Research -- Periodicals; Freight and freightage -- Management -- Periodicals; 387.5442068
• Languages: English
• ISSNs: 1749-4729

Abstract

Third-party logistics (3PL) providers offer logistics outsourcing to firms so that companies can focus on their core competencies. Because the supply chain has such a large impact on a firm's cost and customer service, it is important to ensure that the selected 3PL understands and is capable of providing the services needed by a firm. This paper explores the use of interpretive structural modelling (ISM) in evaluating the selection of a 3PL. Based on expert opinions, the participants rated the relationships between 12 main criteria for 3PL selection and then ISM was applied. The results indicated that cost has the most dependence and least driving power, whereas financial stability has the most driving power and least dependence. Cultural alignment showed weak driving power and weak dependence. Dependent attributes which had weak driving power and strong dependence were cost, reputation, and quality. Locations, collaboration, range of services, and experience had attributes which have strong driving power and strong dependence. And finally, financial stability, flexibility, information technology, and metrics were found to have strong driving power and weak dependence.