Data-Driven Logistics Optimization Using Predictive Intelligence and ERP Integration

Abstract

Efficient logistics management has become a strategic requirement for modern enterprises operating within complex and globally distributed supply chain networks. Enterprise Resource Planning (ERP) systems integrate procurement, warehouse management, transportation, inventory control, production planning, finance, and customer relationship management into centralized enterprise platforms; however, conventional ERP systems primarily support transaction processing and historical reporting rather than predictive logistics optimization. Predictive intelligence enables organizations to continuously analyze operational data, anticipate transportation disruptions, optimize delivery schedules, improve fleet utilization, and enhance logistics coordination before operational inefficiencies occur. This paper proposes a data-driven logistics optimization framework using predictive intelligence and ERP integration by combining machine learning, cloud computing, Internet of Things (IoT), business intelligence, and predictive analytics. The proposed framework continuously evaluates transportation activities, warehouse operations, supplier deliveries, customer demand, inventory movement, and fleet performance to generate intelligent logistics recommendations. Experimental analysis demonstrates improved delivery efficiency, transportation utilization, route optimization, inventory availability, operational resilience, and enterprise decisionmaking compared with conventional ERPsupported logistics management systems. Keywords: Logistics Optimization, Enterprise Resource Planning, Predictive Intelligence, Machine Learning, Supply Chain Management, Business Intelligence, Cloud Computing, Internet of Things, Transportation Management, Predictive Analytics.