MERCHANDISING MANAGEMENT SYSTEM: LOGISTICS 1 (PROCUREMENT AND WAREHOUSING) WITH WAREHOUSE LAYOUT FOR MONITORING USING GENERATIVE AI AND BARCODE IDENTIFICATION

Abstract

The logistics industry continues to face challenges such as inefficient supply chain management, lack of warehouse monitoring, and poor decision-making. This study aims to develop an AI-powered Logistics Management System (LMS) designed to enable real-time warehouse monitoring and address these operational inefficiencies. The system was created for Great Wall Art, a growing wholesale and retail company specializing in locally produced handmade products. The development process involved business process re-engineering, system analysis and design, and prototyping. The system utilized cloud infrastructure, IoT sensors, and data analytics for real-time inventory monitoring. Integration of real-time data included inventory levels, storage conditions, and sector-specific product tracking. The system also aimed to reduce inventory costs and enhance warehouse decision-making capabilities. The system’s user-friendly interface enabled logistics managers to optimize operations and respond quickly to supply and demand changes. Real-time warehouse monitoring enhanced visibility and control, providing a comprehensive solution that significantly improved logistics efficiency and accuracy. The implementation of the LMS demonstrated that a scalable, adaptable, and flexible logistics system can offer a competitive advantage. The integration of generative AI and machine learning enhances predictive analytics and decision-making processes. Future research may explore further applications of AI for automation and intelligent forecasting in warehousing and procurement.