The Role of Digital Twins in Supply Chain Process Simulation and Optimization

Abstract

Supply chains are increasingly exposed to disruption, demand volatility, and operational complexity that traditional planning tools cannot fully capture in real time. Digital twin technology is emerging as a powerful capability for process simulation and optimization by creating a continuously updated virtual representation of supply chain operations. A digital twin combines physical process data, enterprise system transactions, and predictive analytics to simulate scenarios, evaluate trade-offs, and recommend optimal decisions before execution. This paper explores the role of digital twins in improving supply chain performance through simulation-based planning, bottleneck identification, inventory optimization, transportation network design, and resilience building. The study synthesizes research and industry frameworks to evaluate enabling technologies, implementation approaches, governance requirements, and barriers such as data quality, integration complexity, and organizational change. The findings indicate that digital twins can deliver measurable improvements in service levels, throughput, and cost efficiency by enabling decision-makers to evaluate outcomes under multiple conditions, including disruptions. However, value realization depends on strong master data, integration with ERP and IoT sources, and continuous model validation. The paper concludes with recommendations for implementing digital twins as an operational capability rather than a one-time transformation project.