DEVELOPMENT OF A DIGITAL TWIN FRAMEWORK USING ANAPLAN AND EMERGING TECHNOLOGIES FOR PREDICTIVE MAINTENANCE IN THE MANUFACTURING INDUSTRY

Abstract

The advent of Industry 4.0 has revolutionized manufacturing by integrating cyber-physical systems, the Internet of Things (IoT), and artificial intelligence (AI) into industrial workflows. Among these innovations, digital twins have emerged as a transformative tool, enabling real-time monitoring, simulation, and optimization of physical assets. This paper proposes a novel digital twin framework that leverages Anaplan’s enterprise planning platform alongside emerging technologies—such as IoT, AI, machine learning (ML), and edge computing—to enhance predictive maintenance strategies in manufacturing. By bridging data silos, enabling dynamic simulations, and delivering actionable insights, the framework aims to reduce downtime, extend asset lifespans, and optimize operational efficiency. The study outlines the architecture, implementation challenges, and real-world applications of the framework, supported by case studies and future trends in digital twin adoption.