LOGISTIC MANAGEMENT SYSTEM: ANALYTICS REPORTING RIS MANAGEMENT MODULE OF REAL TIME DATA ANALYTICS; COST REDUCTION, SCENARIO PLANNING AND AUDIT TRAIL

Abstract

This study presents an overview of the Analytics Reporting Risk Management Module, emphasizing its significance in today's business landscape. It highlights the integration of real-time data analytics into risk management practices to enhance organizational resilience. By utilizing predictive modeling, machine learning, and artificial intelligence, the module conducts comprehensive risk assessments in real time. It achieves this by analyzing diverse data sources, including transactional records, market trends, and historical patterns. The Agile Methodology is widely adopted in logistics management systems to enhance efficiency, collaboration, and adaptability in service and goods delivery. When applied to the Analytics Reporting Risk Management Module, it enables cost reduction, scenario planning, and audit trail development. This approach allows logistics managers to make data-driven decisions, adapt to changing conditions, and continuously improve operational efficiency. The results section describes the structured approach and processes used in the development of the logistics management system. The successful implementation of this system required alignment with project objectives, stakeholder needs, and industry best practices. Furthermore, a robust audit trail mechanism was established to enhance transparency and accountability in decision-making processes. This study explores the broader implications of leveraging real-time data analytics for risk management and cost optimization across various industries. It also discusses potential challenges associated with adopting such modules and provides recommendations for future research to improve the analytical framework.