Optimizing Inventory Management in B2C E-commerce using H2O AutoML
Аннотация
In the fast-paced and competitive landscape of business-to-consumer (B2C) e-commerce, effective inventory management is crucial for maintaining operational efficiency and ensuring customer satisfaction. This thesis, titled "Optimization of Inventory Management in B2C E-commerce Using H2O Models," explores the application of Automated Machine Learning (AutoML) models to enhance inventory management processes. The research aims to develop a comprehensive AutoML-based system for accurate demand forecasting, optimal stock level maintenance, and streamlined replenishment, leveraging the H2O framework.
The study begins with a detailed review of traditional inventory management techniques, highlighting their limitations in handling the dynamic and complex nature of e-commerce. It then delves into advanced machine learning approaches, comparing models such as regression analysis, decision trees, support vector machines (SVM), and neural networks. A significant focus is placed on the transformative role of AutoML, which automates the model selection, hyperparameter tuning, and deployment processes, making sophisticated predictive analytics accessible to non-experts.
Key contributions of this thesis include the development of an AutoML system architecture tailored for inventory management, integration with customer relationship management (CRM) systems, and a thorough evaluation of the system's performance. The proposed system is validated through extensive simulations and real-world case studies, demonstrating its effectiveness in reducing stockouts, minimizing excess inventory, and improving overall operational efficiency.
The findings underscore the potential of AutoML to revolutionize inventory management in B2C e-commerce by enabling businesses to respond swiftly to market fluctuations, optimize inventory levels, and enhance customer satisfaction. This research provides valuable insights and practical solutions for e-commerce enterprises looking to leverage advanced machine learning technologies for strategic advantage.
The study begins with a detailed review of traditional inventory management techniques, highlighting their limitations in handling the dynamic and complex nature of e-commerce. It then delves into advanced machine learning approaches, comparing models such as regression analysis, decision trees, support vector machines (SVM), and neural networks. A significant focus is placed on the transformative role of AutoML, which automates the model selection, hyperparameter tuning, and deployment processes, making sophisticated predictive analytics accessible to non-experts.
Key contributions of this thesis include the development of an AutoML system architecture tailored for inventory management, integration with customer relationship management (CRM) systems, and a thorough evaluation of the system's performance. The proposed system is validated through extensive simulations and real-world case studies, demonstrating its effectiveness in reducing stockouts, minimizing excess inventory, and improving overall operational efficiency.
The findings underscore the potential of AutoML to revolutionize inventory management in B2C e-commerce by enabling businesses to respond swiftly to market fluctuations, optimize inventory levels, and enhance customer satisfaction. This research provides valuable insights and practical solutions for e-commerce enterprises looking to leverage advanced machine learning technologies for strategic advantage.