Enhancing E-commerce Supply Chain and Shipping Efficiency with Machine Learning and Deep Learning Models

Bandarapu, Sathvika (2024) Enhancing E-commerce Supply Chain and Shipping Efficiency with Machine Learning and Deep Learning Models. Masters thesis, Dublin, National College of Ireland.

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Abstract

E-commerce businesses are increasingly prioritizing with the efficient supply chain and shipping processes to meet rapidly growing customer demands while also mitigating the operational costs. Despite advancements in machine learning (ML) and deep learning (DL) techniques, existing works predominantly focuses on predicting mainly late delivery risk rather than estimating scheduled and actual shipping durations which is very critical aspect for enhancing supply chain resilience. To address the gap, this study aims to predict both scheduled and actual shipping durations using the DataCo SMART SUPPLY CHAIN dataset. This research aims to make predictions as a multi-output regression problem while also employing extensive preprocessing, feature selection, and hyperparameter optimization to mitigate the prediction error. Results shown that tree-based ML models outperform DL models in capturing complex, nonlinear relationships within tabular data, with XGBoost achieving the highest accuracy. These findings highlight the potential of advanced predictive analytics to optimize logistics, minimize delays, and improve decision-making in e-commerce supply chain operations.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HF Commerce > Electronic Commerce Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HD Industries. Land use. Labor > Business Logistics > Supply Chain Management
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	01 Sep 2025 15:01
Last Modified:	01 Sep 2025 15:01
URI:	https://norma.ncirl.ie/id/eprint/8680

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