Optimizing Supply Chain Logistics Through AI & ML: Lessons from NYX

Modern day Supply chain and logistics management system integrates artificial intelligence (AI) and machine learning (ML) to develop it into an operational transformation which enhances resilience, reduces costs and improves efficiency in corporate offices. This paper evaluates how artificial intelligence and machine learning-based demand forecasting and route optimization systems facilitate process optimization through inventory management. This paper applies to NYX as an example of a mid-sized logistics manufacturer to present real-world applications of these technologies and extract important implementation lessons. The success of predictive analytics combined with artificial intelligence depends on solving data combination and operation scale maintenance issues so it can enhance logistics efficiency through machine learning optimization algorithms. Organizations interested in supply chain modernization can utilize the discovered findings that implementing AI and ML strategically results in substantial operational benefits.