SCHOOL (MANAGEMENT SYSTEM : LOGISTICS MANAGEMENT SYSTEM WITH DEMAND FORECASTING AND DELIVERY SCHEDULING)

Abstract

Logistics is a crucial component in the efficient operation of educational institutions, particularly in managing the distribution of textbooks and uniforms. Traditional systems often struggle with issues such as inaccurate demand forecasting, excess inventory, and delayed deliveries. To overcome these inefficiencies, this project proposes an integrated School Management System (SMS) that leverages AI-powered demand forecasting and real-time delivery scheduling. The system aims to optimize resource allocation, minimize waste, and ensure the timely and accurate delivery of educational materials to both students and staff. The project utilized an Agile Scrum methodology, incorporating frequent sprint cycles and continuous stakeholder feedback to ensure alignment with requirements. Its microservices architecture provided modularity and easy scalability throughout development. Demand forecasting was powered by machine learning models that considered enrollment patterns, seasonal fluctuations, and past data trends. The use of DevOps practices, such as continuous integration and deployment (CI/CD), facilitated efficient development and testing workflows. Data synchronization across various system modules was handled in real-time through RESTful APIs within a centralized cloud platform. The approach effectively minimized stockouts and overstock situations by accurately forecasting demand for textbooks and uniforms. Enhanced scheduling algorithms and real-time delivery tracking improved distribution efficiency. The administrator dashboard offered valuable insights through data visualization and detailed reporting, facilitating informed decision-making. Consequently, improved planning and transparency led to greater satisfaction among administrators and logistics managers. Combining demand forecasting with logistics management greatly enhances operational efficiency in educational institutions. This system demonstrates how real-time data synchronization and artificial intelligence enable informed decision-making, mitigate common logistical issues, and improve resource utilization. Development challenges, including system integration and stakeholder training, were addressed through iterative improvements and ongoing feedback. The system’s scalable design supports future expansion to cover additional logistics areas such as food services, transportation, and supplier network integration.