ARTIFICIAL INTELLIGENCE IN MODERN SUPPLY CHAIN MANAGEMENT: ADVANCEMENTS, RESILIENCE, AND STRATEGIC INTEGRATION IN THE ERA OF INDUSTRY 5.0

Abstract

The rapid convergence of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and advanced data analytics has fundamentally revolutionized global supply chain management (SCM). Traditional supply chains, historically characterized by linear operations, siloed communication, and reactive decision-making, are increasingly inadequate in navigating contemporary geopolitical volatility, demand unpredictability, and climate-induced disruptions. This research paper investigates the structural integration of AI across core supply chain pillars—namely demand forecasting, inventory optimization, predictive maintenance, logistics route planning, and supplier risk management. Utilizing a mixed-methodological framework incorporating empirical secondary data analysis, systematic literature synthesis of fifteen peer-reviewed studies, and an in-depth case study of a global manufacturing conglomerate, this study demonstrates that AI-driven supply chains achieve up to a 35% reduction in carrying costs, a 50% improvement in forecast accuracy, and a significantly heightened resilience quotient. Furthermore, the paper evaluates ethical considerations, algorithmic bias, data security challenges, and workforce transformation imperatives under the paradigm of Industry 5.0. The findings provide actionable strategic roadmaps for enterprise leaders, supply chain practitioners, and academic researchers seeking to harness cognitive automation for sustainable competitive advantage.