Ai-Driven Predictive Analytics in Supply Chain Management

Abstract

This paper explores the impact of AI-driven predictive analytics on supply chain management (SCM), focusing on inventory optimization, demand forecasting, and order Fulfilment efficiency. The study implemented AI models, including regression-based inventory optimization, LSTM networks for demand forecasting, and machine learning techniques for order Fulfilment optimization, using historical data from a multinational retail company covering Q1 2019 to Q4 2019. Results showed a 38% to 41% improvement in inventory turnover, indicating more efficient stock management. In demand forecasting, AI models outperformed traditional methods, with LSTM reducing the Mean Absolute Percentage Error (MAPE) from 12.5% to 6.3%. Furthermore, order Fulfilment efficiency saw a 37.5% reduction in processing time, a 12.94% increase in on-time deliveries, and an 18.57% decrease in shipping costs. These findings demonstrate the significant role of AI in enhancing SCM efficiency and providing businesses with a competitive advantage in a dynamic global market.