AI-Driven Demand Forecasting & Inventory Optimization: A Case Study on Supply Chain Efficiency Enhancement

Abstract

The implementation of AI-driven demand sensing in supply chain management represents a significant advancement over traditional forecasting methods that rely primarily on historical data and statistical analysis. By incorporating machine learning algorithms capable of processing diverse data streams—including point-of-sale information, social media sentiment, weather patterns, and macroeconomic indicators—organizations can transition from reactive to proactive inventory management strategies. This article examines how the integration of external variables extends predictive capabilities, enabling the identification of subtle demand signals that conventional methods typically miss. The results demonstrate improved forecast accuracy across diverse product categories, optimized inventory levels, enhanced supply chain collaboration, and powerful anomaly detection capabilities. A human-AI collaborative framework emerges as essential, with human expertise providing crucial context for interpreting model anomalies and making strategic adjustments. Successful implementation requires thoughtful organizational change management, transparent communication, and leadership engagement to transform forecasting processes and decision-making structures.