Design of Inventory Information System Model on Smart Warehouse Management System (WMS) Based on Artificial Intelligence (AI) with Integration of Waterfall Method and Design Thinking to Optimize Inventory Accuracy

Abstract

Modern warehouse operations face significant challenges with manual inventory management processes, resulting in accuracy rates as low as 65% and substantial operational inefficiencies that directly impact customer satisfaction and profitability. This study presents the design and implementation of an innovative Inventory Information System Model for Smart Warehouse Management Systems (WMS) based on Artificial Intelligence technology, specifically developed to address these critical inventory management deficiencies. The research objectives focus on developing an automated system that minimizes human errors, provides real-time data analytics, and enhances overall operational efficiency through intelligent decision-making capabilities. The methodology integrates the structured Waterfall development approach with user-centered Design Thinking principles, ensuring both systematic development and optimal user experience. The AI-powered system incorporates machine learning algorithms for demand forecasting, computer vision for automated stock counting, natural language processing through integrated chatbots for enhanced user interaction, and predictive analytics for optimized inventory levels. Implementation and testing within the Geoff Max Group demonstrated significant improvements, achieving 95% inventory accuracy compared to the previous 70% manual accuracy rate, reducing stock-out incidents by 60%, and decreasing inventory carrying costs by 25%. The system successfully processes real-time data from multiple warehouse locations, providing managers with comprehensive dashboards and automated alerts for critical inventory thresholds. The implications of this research extend beyond operational improvements, offering a scalable solution for modern supply chain management that can be adapted across various industries. This integrated approach represents a significant advancement in warehouse automation, demonstrating how AI-driven systems can transform traditional inventory management practices while providing economic benefits.