INVESTIGATING THE IMPACT OF AI ON SUPPLY CHAIN MANAGEMENT

This study investigated the impact of Artificial Intelligence (AI) on supply chain management (SCM), with a focus on understanding its applications, performance implications, and implementation challenges. This study investigated the impact of Artificial Intelligence on supply chain management. Specifically, the study examined the key AI technologies applied in supply chain management and their operational roles, assesses the impact of AI on the performance, efficiency, and responsiveness of supply chains, identify the major challenges and barriers organizations face in implementing AI in supply chain operations. The study adopted a descriptive research design using a survey method to collect primary data. The population comprised 171 supply chain professionals across manufacturing firms in Edo State, Nigeria. A sample size of 120 respondents was selected using the taro Yamane formula, focusing on individuals with operational roles in procurement, inventory management, warehousing, and logistics. Data were collected through structured questionnaires and analyzed using descriptive statistics and inferential analysis techniques. The results revealed that AI significantly enhances supply chain efficiency by improving demand forecasting accuracy, optimizing inventory levels, and reducing lead times. The findings support the argument that while AI offers transformative potential for SCM, its successful adoption requires strategic planning, investment in digital infrastructure, and workforce training. The study concluded that AI is a critical enabler of modern supply chain competitiveness. Firms that overcome the associated barriers stand to benefit from improved operational performance and customer satisfaction. It is recommended that businesses adopt phased implementation strategies and collaborate with technology providers to ensure scalable and sustainable integration of AI tools. Keywords: Artificial Intelligence, Supply chain Management, Forecasting, Inventory optimization, Operational efficiency.