Dynamic Resource Allocation in Supply Chain Scheduling Using Digital Twin Models

Abstract

The sophistication of supply chains alongside their flexibility have become intricate under the industry 4.0 era,
imposing a need for smart systems capable of intelligent and agile resource allocation and scheduling. This work
presents a digital twin (DT) technology framework concentrating on dynamic resource allocation in supply chain
scheduling. Digital Twins (DTs) captures the virtual counterpart of physical entities and operations, providing
mechanisms for real-world data interfaces, predictive modeling, and decision response systems. The proposed model
applies the Internet of Things (IoT), real-time simulation, and machine learning algorithms for resource dynamically
reallocation to match changes in the environment and operations. The methodology is tested in a simulated
manufacturing supply chain environment and evaluated against static scheduling models using Dijkstra’s algorithm.
The results demonstrate that the DT-based model enhances resource utilization by 18% and decreases lead time by
23% in comparison to conventional models. DT models are suggested as suitable solutions to effectively manage realtime
variability in the system and increase supply chain resilience. This study expands the scope of intelligent
manufacturing research and demonstrates practical DT application methods in supply chain operations.