Integrating Data Analysis and Predictive Analytics into an Advanced Inventory Management System using Machine Learning and Deep Learning to Address Backorders

Abstract

The evolution of ICT technology has benefited various businesses and applications in a variety of ways. The inventory management system is one of those that has used a variety of techniques to improve logistics and inventory management performance. The novel research introduces an advanced inventory management system that intends to improve operational efficiency in the retail industry by providing an intuitive interface for managing purchases, invoicing, client information, product catalogues, and sales numbers. The underlying inventory control systems automatically compute and apply reorder levels and safety stock quantities. Machine learning and deep learning are new technologies employed in current analytics due to their enormous benefits. As a result, the purpose of this research paper is to highlight a combined approach of providing analytical business intelligence via data visualization platforms, as well as predictive analytics via machine learning and deep learning, in order to demonstrate a significant advancement toward the automation and optimization of inventory governance processes. The proposed approach demonstrates how combining traditional software development with cutting-edge data analysis techniques can result in a more efficient, scalable, and informative inventory management solution. It addresses common retail difficulties like stockouts and overstocking by utilising powerful machine learning (ML) and deep learning (DL) techniques. Finally, empirical testing and realworld applications are used to evaluate the suggested approach, which shows significant advances in inventory optimisation as well as other benefits such as enhanced demand forecasting, cost reductions, and operational efficiency. The findings show that machine learning and deep learning have the potential to transform inventory management practices.