MANUFACTURING MANAGEMENT SYSTEM.LOGISTIC 2 WAREHOUSE,INVENTORY AND TRANSPORTATION FOR FORECASTING

Abstract

This capstone project aims to offer an efficient solution to address logistics-related challenges in manufacturing companies. By applying artificial intelligence and advancing software capabilities, the study seeks to develop a system that manages logistics operations more effectively, reduces costs, and enhances overall business performance. The focus of this research is the integration of AI-based demand forecasting within logistics operations, specifically in warehouse management, inventory control, and transportation planning. The system is designed to optimize the supply chain process in manufacturing environments by predicting future demand and aligning operational strategies accordingly. The development of the Manufacturing Management System focused on improving operational efficiency and reducing resource waste. The system utilized AI-based predictive analytics to enhance inventory accuracy, streamline warehouse operations, and plan transportation activities effectively. Core objectives included optimizing logistics functions, cutting costs, and minimizing material waste. Agile methodology guided the project development to allow for iterative improvements based on user feedback and evolving system requirements. Throughout the project life cycle, the team encountered and successfully addressed several challenges, including changes in project scope due to evolving stakeholder requirements. These were managed through open communication and adaptive planning. Technical hurdles, such as integrating different modules within the system, were resolved through detailed testing and systematic development. The team also learned to balance feature development with technical debt by employing continuous refactoring and thorough code reviews, contributing to the system’s overall stability and performance. The interconnection between demand forecasting, inventory management, warehouse operations, and transportation planning was a key insight of the project. Demand predictions directly influenced inventory decisions, which subsequently impacted warehouse capacity and transportation logistics. For example, a forecasted rise in product demand would prompt the inventory system to order more supplies, requiring the warehouse to manage higher stock volumes and the transportation system to coordinate increased distribution. This study underscores the importance of synchronized logistics components and demonstrates how AI-driven forecasting can enhance manufacturing logistics efficiency.