Digital Twin Technology for Predictive Knowledge Management in Industrial Operations

Abstract

This research investigates the application of digital twin technology as a sophisticated tool for predictive knowledge management within complex industrial operations. It explores how real-time data from physical assets, combined with historical knowledge, enables the creation of dynamic digital models that can forecast performance, identify potential failures, and optimize operational efficiency. The study will analyze the benefits and challenges of integrating digital twins into existing KM systems, focusing on their capacity to facilitate proactive decision-making and reduce downtime in manufacturing or energy sectors.