Optimising Supply Chain Performance with Machine Learning for Predicting Late Deliveries

Rathi, Avni (2024) Optimising Supply Chain Performance with Machine Learning for Predicting Late Deliveries. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research explores the application of machine learning approaches to enhance e-commerce supply chain management, focusing on two critical aspects: managing late deliveries and improving sales prediction accuracy. Since late deliveries can have a major effect on sales performance, the study combines both factors to give a thorough understanding of their interrelated effects. This work examines the effectiveness of machine learning models in predicting the risk of late deliveries, employing Random Forest, Decision Tree, and Logistic Regression. The study utilized a comprehensive dataset containing sales and delivery information to evaluate model performance. The findings reveal that the Random Forest Classifier at 97.58% accuracy outperformed other models in predicting late deliveries after applying hyperparameter tuning to optimize the performance of the model, demonstrating the highest accuracy and robustness. Feature importance method was performed to find key predictors which impact model results. Further for sales prediction, this work investigates how machine learning algorithms can improve sales behavior prediction. The models evaluated include Linear Regression, Decision Tree Regression, and Lasso Regression. Among these, Decision Tree Regression achieved the best performance, with an exceptional R-squared value at 0.996 indicating superior accuracy in forecasting sales behavior. The research highlights the significance of effective data transformation, feature engineering, and categorical encoding in model performance.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Alam, Naushad UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science 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:	25 Aug 2025 10:36
Last Modified:	25 Aug 2025 10:36
URI:	https://norma.ncirl.ie/id/eprint/8618

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