Smart Distribution in E-Commerce: Harnessing Machine Learning and Deep Learning Approaches for Improved Logistics
DOI:
https://doi.org/10.22399/ijcesen.1157Keywords:
E-Commerce, Distribution Systems, Logistics, Machine learning, Deep learningAbstract
The e-commerce receives extreme competition in recent years, significantly with the requirement of facing the demands of consumers in speed, effective and accessibility. The distribution systems composes the crucial role in the assurance of faster and exact delivery of the products from the warehouses to the consumers. Due to the growth in the globalized e-commerce, there is an increasing demand for classic and manageable distributor systems. The conventional distribution systems includes the stocking and shipping of products directly to the consumers and fails in faster deliveries and tracking of orders. Hence, the distributors systems requires to integrate the parameters such as maintenance of records, exact orders and the maintenance of logistics for the assurance of on time delivery without extra costs. The above systems manages the issues such as weather modifications with the disturbance in the supply chains and multi-channel logistics issues. The ML and DL algorithms allows the e-commerce business for transferring from the traditional to the potential and data driven techniques. The ML algorithms examines the earlier and real time data for forecasting the demands whereas the DL algorithms assess the formless data such as feedbacks of consumers and the fashions of social media for additional innovations. Hence, the utilization of those algorithms enhances the ability of operations, reduction in cost with the increased fulfilment of consumers resulting in the enlarged competition of the e-commerce sector. Moreover, the ML and DL algorithms are fine-tuning the e-commerce future with the enhancement in distribution systems and generating the capability of modifying the iterative market transitions for facing the needs of consumers.
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