A Supply Chain Digital Twin Architecture for Semiconductor Industry

Bulent Soykan (), Sean Mondesire (), Ghaith Rabadi () and Grace Bochenek ()
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Bulent Soykan: University of Central Florida, Institute for Simulation and Training
Sean Mondesire: University of Central Florida, School of Modeling, Simulation and Training
Ghaith Rabadi: University of Central Florida, School of Modeling, Simulation and Training
Grace Bochenek: University of Central Florida, School of Modeling, Simulation and Training

Chapter Chapter 7 in Optimizing Supply Chains Through Digital Twins, 2025, pp 101-120 from Springer

Abstract: Abstract The semiconductor industry operates within a uniquely challenging environment characterized by extreme complexity, rapid technological evolution, and stringent quality requirements, rendering its global supply chains vulnerable to disruption. This chapter explores the application of digital twins (DTs) as a strategic solution to enhance operational management and resilience in this critical sector. We define the semiconductor supply chain DT as a dynamic virtual replica of the physical network, continuously synchronized through real-time data integration. The chapter details the core architecture, focusing on the central data hub and the integration of key components: foundational business systems (ERP, MES, PLM, CRM), specialized planning and dispatching services, advanced analytics and AI/ML tools, and vital external data streams from suppliers, customers, and IoT sensors. Key functionalities enabled by this integrated framework—including real-time monitoring, predictive simulation, and process optimization—are examined. We highlight the significant benefits derived, such as enhanced end-to-end visibility, proactive risk mitigation, improved operational agility, optimized resource allocation, and continuous quality assurance.

Keywords: Digital Twin; Semiconductor industry; Supply chain management; System architecture; Data integration; Predictive analytics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/978-3-032-08147-6_7

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