Optimizing Human-Centric Warehouse Operations: A Digital Twin Approach Using Dynamic Algorithms and AI/ML

Purpose: This study aims to develop a versatile and adaptive system that optimizes manual warehouse operations through the integration of Digital Twin technology and AI/ML models. Methodology: The framework combines Digital Twin technology with advanced AI/ML analytics to dynamically adjust operational strategies based on real-time data collected from warehouse activities. Findings: A prototype implementation demonstrated significant improvements, including a 28.6% reduction in average picking time, a 20% improvement in inventory turnover, an increase in demand forecasting accuracy from 85% to 92%, and a reduction in labor costs by 15%. Originality: This research uniquely applies Digital Twin technology to manual warehouse environments, showcasing its effectiveness in enhancing operational efficiency without the need for full automation.