Enhancing Supply Chain Efficiency to Build Next-Gen Artificial Intelligence (AI)/Machine Learning Network Through Al-Driven Forecasting

Abstract

The networking hardware industry is characterized by unique challenges when it comes to supply chain management. These include unpredictable demand patterns, complex logistics, besides disruptions caused by rapid technological advancements. This paper explores the integration of artificial intelligence (AI) into forecasting methodologies to enhance supply chain efficiency within the sector. Application of AI-driven forecasting models can help organizations improve demand predictions, refine inventory management, and streamline logistical operations. Drawing on recent research and industry practices, this article highlights the transformative impact of AI on supply chain efficiency and offers insights into best implementation practices. Furthermore, the research investigates the intersection of AI and networking hardware supply chain management, focusing on leveraging AI to analyze hardware failure patterns and interpret hardware-generated alarms and interrupts. By harnessing analytical capabilities of AI, modern organizations can extract actionable insights to reduce failure rates and enhance supply chain forecasting accuracy. This innovative approach enables more effective anticipation and preparation for hardware failures, optimizing spare part inventory management and minimizing the need for costly return merchandise authorizations (RMAs).