Abstract
Supply Chain Management (SCM) has emerged as a pivotal element of contemporary business strategies, deftly incorporating advancements in Machine Learning (ML) and Deep Learning (DL) to bolster market performance. In the E-commerce sector, SCM, enhanced by ML, is driving critical transformative changes, which have become particularly vital in the post-pandemic landscape. This evolution in SCM is setting new benchmarks in process efficiency, encompassing comprehensive risk mitigation and substantial reduction in operational costs. It ensures swift delivery and elevated customer satisfaction, while also offering deep insights into the automation of goods delivery within E-commerce. This is a crucial aspect in sculpting a globally competitive SCM model. By utilizing advanced ML software, supply chain managers in E-commerce can refine their portfolios and identify the most fitting suppliers, thus propelling their businesses towards greater efficiency and effectiveness. This article is dedicated to exploring these significant developments. It begins by examining the principles of spatial and temporal data analysis. On this foundation, it elaborates on the implementation of SCM through a variety of modern techniques, with a special emphasis on ML and DL applications. These techniques are instrumental in formulating a framework grounded in spatial-temporal data analysis. Conclusively, the article delineates the design and practical details of SCM, integrating diverse characteristics and the latest technological innovations.
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Vijayalakshmi, S., Shanmugasundaram, S., Padmanabhan, P., Jerald Nirmal Kumar, S. (2024). Spatio-Temporal Supply Chains and E-Commerce. In: A, J., Abimannan, S., El-Alfy, ES.M., Chang, YS. (eds) Spatiotemporal Data Analytics and Modeling. Big Data Management. Springer, Singapore. https://doi.org/10.1007/978-981-99-9651-3_9
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