Optimizing Sustainable Inventory Management using An Improved Big Data Analytics Approach

Abstract

This study delves into optimizing sustainable inventory management practices through the integration of advanced data analytics methodologies. In response to the complex dynamics of modern supply chains, where inventory control significantly impacts sustainability goals, this research aims to address the intricate interplay between decentralized decision-making, government policies, and strategic choices within supply chain networks. Employing models such as Game Theory and Gated Recurrent Unit (GRU), alongside statistical analyses, our investigation explores the transformative potential of informed decision-making frameworks. Through a comprehensive evaluation of inventory data, including statistical analyses, visual representations, and model evaluations, we illuminate the nuanced relationships and dependencies prevalent within inventory control strategies. Our findings underscore the significance of data-driven decision-making in optimizing inventory practices, mitigating risks, and fostering sustainability within supply chains. The insights gleaned from this study advocate for the continued application of advanced data analytics to pave the way for resilient, environmentally conscious, and economically viable supply chain practices.