Enhancing Supply Chain Agility and Sustainability through Machine Learning: Optimization Techniques for Logistics and Inventory Management

In the current global market, supply chains are increasingly complex, necessitating agile and sustainable management strategies. Traditional analytical methods often fall short in addressing these challenges, creating a need for more advanced approaches.

This study leverages advanced machine learning (ML) techniques to enhance logistics and inventory man-agement. Using historical data from a multinational retail corporation, including sales, inventory levels, order fulfillment rates, and operational costs, we applied a variety of ML algorithms, in-cluding regression, classification, clustering, and time series analysis.