A data analytic-based logistics modelling framework for E-commerce enterprise

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Date

2022-01

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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.

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Management, E-commerce, Data analytics, Machine learning (ML), Supply chain, Facility location

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