LOGISTIC MANAGEMENT SYSTEM WEB-BASED DEMAND FORECASTING AND QUALITY CONTROL INTEGRATIONS FOR ENHANCED OPERATIONS

Abstract

This study presents a web-based platform for quality control and demand forecasting aimed at enhancing logistics operations. Demand forecasting utilizes historical data and market trends to predict future demand for StarFlex company’s services. Quality control ensures that the company adheres to industry standards, maintaining consumer trust and delivering consistent, high-quality services. Accurate forecasting enables optimized production and cost reduction, leading to improved operational efficiency. Market demand depends on consumer behavior and industry trends. Predicting consumer demand requires not only analyzing customer needs but also understanding market dynamics. Companies that adopt agile strategies may face challenges in forecasting consumer reactions to market shifts. This study examines demand trends and logistics performance to refine forecasting models and enhance business adaptability. The findings emphasize the importance of performance measurement, progress tracking, and quality assurance in logistics operations. By analyzing key performance indicators (KPIs), teams can identify areas for improvement, implement corrective measures, and maintain high standards in both market prediction and quality control. Demand forecasting and quality control are essential for logistics efficiency. Demand forecasting estimates future consumer demand to optimize resource allocation and inventory management, while quality control ensures that products meet customer expectations, minimizing errors, defects, and waste. Integrating these components improves operational success, cost efficiency, and customer satisfaction. Future advancements may involve AI-driven forecasting, multi-source data integration, and automated quality control systems to further enhance logistics operations.