Thesis Abstract

This project focuses on developing a machine learning model for predictive inventory management to optimize inventory levels and minimize stockouts and overstock situations. The project will leverage historical sales data, demand forecasts, and external factors to train a predictive model that can accurately anticipate future inventory needs. By implementing the model within inventory management systems, organizations can make data-driven decisions to improve supply chain efficiency, reduce carrying costs, and enhance customer satisfaction. The project aims to contribute to the advancement of inventory management practices, offering a practical solution for businesses to achieve optimal inventory levels and streamline operations.

Thesis Overview