DIGITAL TWIN-ASSISTED SUPPLY CHAIN PLANNING THROUGH PREDICTIVE ERP ANALYTICS

Abstract

The rapid evolution of digital transformation technologies has significantly changed the way organizations manage modern supply chains. Enterprise Resource Planning (ERP) systems integrate procurement, manufacturing, warehouse management, logistics, inventory control, finance, and customer relationship management into centralized enterprise platforms; however, conventional ERP systems primarily focus on operational transaction management and historical reporting rather than predictive planning. Digital Twin technology provides virtual representations of physical supply chain operations, enabling organizations to simulate future scenarios, evaluate operational risks, and optimize enterprise planning before implementing business decisions. This paper proposes a Digital Twin-assisted supply chain planning framework using predictive ERP analytics by integrating Digital Twin technology, machine learning, cloud computing, Internet of Things (IoT), business intelligence, and predictive analytics. The proposed framework continuously synchronizes physical supply chain operations with virtual enterprise models to support intelligent forecasting, inventory optimization, supplier coordination, production scheduling, and logistics planning. Experimental analysis demonstrates significant improvements in planning accuracy, operational efficiency, supply chain resilience, and enterprise decision-making compared with conventional ERP planning approaches. Keywords: Digital Twin, Enterprise Resource Planning, Predictive Analytics, Supply Chain Planning, Machine Learning, Internet of Things, Cloud Computing, Business Intelligence, Supply Chain Optimization, Digital Transformation.