| dc.contributor.author | Verma, Abhishek | |
| dc.date.accessioned | 2025-09-24T06:59:40Z | |
| dc.date.available | 2025-09-24T06:59:40Z | |
| dc.date.issued | 2022-01 | |
| dc.identifier.uri | https://www.tandfonline.com/doi/full/10.1080/17517575.2022.2028195 | |
| dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/19529 | |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Taylor & Francis | en_US |
| dc.subject | Management | en_US |
| dc.subject | E-commerce | en_US |
| dc.subject | Data analytics | en_US |
| dc.subject | Machine learning (ML) | en_US |
| dc.subject | Supply chain | en_US |
| dc.subject | Facility location | en_US |
| dc.title | A data analytic-based logistics modelling framework for E-commerce enterprise | en_US |
| dc.type | Article | en_US |
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