Optimizing Supply Chain Efficiency Through Ai-Driven Demand Forecasting: An Empirical Analysis of Retail Industries

Abstract

This study investigates the impact of AI-driven demand forecasting on supply chain optimization in the retail sector. With the increasing complexity and competition in retail, accurate demand forecasting is crucial for operational efficiency. Traditional forecasting methods often fall short in addressing the dynamic nature of demand, leading to inefficiencies in inventory management and order fulfillment. This research explores the application of advanced AI models—Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Random Forest—in improving forecast accuracy and overall supply chain performance. Utilizing a quantitative approach, the study analyzes real-world data from various retail industries to evaluate the effectiveness of these AI models. The results reveal significant improvements in forecast accuracy, inventory management, and order fulfillment rates post-AI implementation. Specifically, the LSTM model demonstrated the highest accuracy with the lowest forecast error, leading to notable reductions in inventory discrepancies and enhanced order fulfillment rates. The study also identifies key performance indicators (KPIs) such as sales volume, inventory turnover rate, and stockout occurrences, all of which showed marked improvement following AI adoption. These findings underscore the potential of AI to address traditional forecasting limitations, offering substantial cost savings, operational efficiency, and a competitive advantage for retail businesses. The study concludes with recommendations for future research, including the exploration of AI's role in other supply chain functions and across diverse retail sectors. This research contributes to the growing body of knowledge on AI in supply chain management and provides actionable insights for enhancing retail operations through advanced forecasting techniques.