Forecasting Sales and Inventory in Supply Chain using Machine Learning Methods

Putra, Rian Dwi (2023) Forecasting Sales and Inventory in Supply Chain using Machine Learning Methods. Masters thesis, Dublin, National College of Ireland.

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Abstract

The aim of the project "Forecasting Sales and Inventory in Supply Chain using Machine Learning Methods" is to revolutionize supply chain management by utilizing cutting-edge machine learning algorithms. Through the utilization of linear regression, lasso regression, ridge regression, decision tree regression, random forest regression, Light Gradient Boosting Regression (LightGBM), and Extreme Gradient Boosting Regression (XGBoost), this study attempts to accomplish precise sales forecasting and optimize inventory management. The models will enable DataCo to make data-driven decisions, improve demand forecasting accuracy, and optimize inventory allocation by analysing historical sales data and customer trends. It is expected that the DataCo Smart Supply Chain will reach new heights of efficiency, cost-effectiveness, and customer satisfaction because of the successful implementation of these machine learning models, establishing DataCo as a market leader in the ever-changing and competitive supply chain landscape.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED Kelly, John UNSPECIFIED
Uncontrolled Keywords:	supply chain management; regression analysis; sales forecasting; inventory management
Subjects:	H Social Sciences > HF Commerce 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:	Tamara Malone
Date Deposited:	28 Dec 2024 14:50
Last Modified:	28 Dec 2024 14:50
URI:	https://norma.ncirl.ie/id/eprint/7251

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