MERCHANDISING MANAGEMENT SYSTEM: LOGISTIC 2 (VENDOR PORTAL, SUPPLIER PERFORMANCE MONITORING, AUTOMATED ORDER PROCESSING) WITH SUPPLIER PERFORMANCE ANALYTICS AI

Abstract

This capstone project presents the development and implementation of the Merchandising Management System designed for the Great Wall of Arts. The system addresses logistical inefficiencies such as poor delivery coordination, manual tracking, and communication gaps between suppliers and customers. It integrates three major modules—Vendor Portal, Supplier Performance Monitoring, and Automated Order Processing—powered by AI-driven analytics to enhance operational efficiency and data accuracy. The project adopted the Agile Scrum methodology to allow iterative development through sprint cycles, with continuous stakeholder collaboration and refinement. A scalable microservices architecture was implemented using Laravel, PHP, MySQL, and JavaScript to ensure system flexibility and maintainability. DevOps practices, including CI/CD pipelines and automated testing, supported the reliability of deployment. RESTful APIs and middleware facilitated seamless integration and service communication. The final system delivered a secure, modular, and data-driven logistics platform. The Vendor Portal enabled real-time interaction, bidding, and tracking for suppliers. The Supplier Performance Monitoring module provided dashboards for assessing vendor reliability based on metrics such as delivery timeliness, product quality, and compliance. The Automated Order Processing module reduced manual work through rule-based order creation and real-time tracking, enhancing delivery accuracy and workflow efficiency. The system effectively resolved core logistics challenges by integrating automation, real-time communication, and supplier performance transparency. Issues such as infrastructure overhead and service communication complexity were addressed through centralized logging, robust security protocols, and modular design. Future upgrades may involve blockchain integration for supply chain transparency and the use of predictive analytics for demand forecasting.