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Title: | A data analytic-based logistics modelling framework for E-commerce enterprise |
Authors: | Verma, Abhishek |
Keywords: | Management E-commerce Data analytics Machine learning (ML) Supply chain Facility location |
Issue Date: | Jan-2022 |
Publisher: | Taylor & Francis |
Abstract: | Data-driven approaches have noteworthy significance in managing and improving logistics in E-commerce enterprises. This study focuses on the development of an integrated framework to analyse the Brazilian E-Commerce enterprise public dataset. From the analysis, it is found that sellers of Ibitinga city of SP state had the most count of late deliveries where 42 sellers are under-performing in terms of estimated delivery time. Locations of customers and sellers were spotted on a map to get a geographical representation. The proposed framework may help E-Commerce enterprise owners and retail merchants to make better decisions related to sales and E-Commerce enterprise logistics. |
URI: | https://www.tandfonline.com/doi/full/10.1080/17517575.2022.2028195 http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19529 |
Appears in Collections: | Department of Management |
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