Artificial Intelligence in Supply Chain Management

Abstract

This paper examines the use of AI in optimizing SCM, particularly efficiency, sustainability, and resilience. The study uses algorithms driven by AI, including machine learning, predictive analytics, and multi-objective optimization to enhance decision-making processes and streamline SCM operations. These algorithms are Genetic Algorithm (GA), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Particle Swarm Optimization (PSO). All of these were applied to real-world data related to the supply chain. The results showed 23% savings in operational costs by GA, 18% better demand forecasting accuracy by ANN, 15% decrease in lead times by SVM, and 20% better supply chain flexibility by PSO. It was also demonstrated that AI, when integrated with blockchain technology, increases the resilience of supply chains. The disruptions are decreased by a significant 30%. When compared to related work, it becomes evident that the optimization potential of AI for SCM has been overlooked; this study has well delineated the step towards improved operational efficiency, cost cuts, and sustainability in the global supply chain. It gives the present scenario, the key challenges of an industry, the role AI played in meeting those demands, and how it holds future scope for development in most diversified industries.